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Found 290 papers, 134 papers with code
New Frontiers of Information Extraction
This tutorial targets researchers and practitioners who are interested in AI and ML technologies for structural information extraction (IE) from unstructured textual sources.
Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning
Research on neural IR has so far been focused primarily on standard supervised learning settings, where it outperforms traditional term matching baselines.
COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System
To tackle the challenge of accurate and timely communication regarding the COVID-19 pandemic, we present a COVID-19 Claim Radar to automatically extract supporting and refuting claims on a daily basis.
Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport
Timeline Summarization identifies major events from a news collection and describes them following temporal order, with key dates tagged.
Lifelong Event Detection with Knowledge Transfer
We focus on lifelong event detection as an exemplar case and propose a new problem formulation that is also generalizable to other IE tasks.
Personalized Entity Resolution with Dynamic Heterogeneous KnowledgeGraph Representations
We first build a cross-source heterogeneous knowledge graph from customer purchase history and product knowledge graph to jointly learn customer and product embeddings.
Event Schema Induction with Double Graph Autoencoders
To induce event schemas from historical events, previous work uses an event-by-event scheme, ignoring the global structure of the entire schema graph.
Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs
In this work, we propose to automatically convert the background knowledge documents into document semantic graphs and then perform knowledge selection over such graphs.
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios
We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios.
Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction
Event time is one of the most important features for event-event temporal relation extraction.
Cross-document Misinformation Detection based on Event Graph Reasoning
Inspired by this process, we propose a novel task of cross-document misinformation detection.
EventKE: Event-Enhanced Knowledge Graph Embedding
Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities.
EA^2E: Improving Consistency with Event Awareness for Document-Level Argument Extraction
Events are inter-related in documents.
Knowledge-Enriched Natural Language Generation
Knowledge-enriched text generation poses unique challenges in modeling and learning, driving active research in several core directions, ranging from integrated modeling of neural representations and symbolic information in the sequential/hierarchical/graphical structures, learning without direct supervisions due to the cost of structured annotation, efficient optimization and inference with massive and global constraints, to language grounding on multiple modalities, and generative reasoning with implicit commonsense knowledge and background knowledge.
GENE: Global Event Network Embedding
Current methods for event representation ignore related events in a corpus-level global context.
A Zero-Shot Claim Detection Framework Using Question Answering
In recent years, there has been an increasing interest in claim detection as an important building block for misinformation detection.
Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries
Moreover, this can be viewed as an especially challenging cross-lingual retrieval problem by considering the molecules as a language with a very unique grammar.
Ranked #2 on
Cross-Modal Retrieval
on ChEBI-20
Coreference by Appearance: Visually Grounded Event Coreference Resolution
Event coreference resolution is critical to understand events in the growing number of online news with multiple modalities including text, video, speech, etc.
Semi-supervised New Event Type Induction and Event Detection
We design a Semi-Supervised Vector Quantized Variational Autoencoder framework to automatically learn a discrete latent type representation for each seen and unseen type and optimize them using seen type event annotations.
Connecting the Dots: Event Graph Schema Induction with Path Language Modeling
Event schemas can guide our understanding and ability to make predictions with respect to what might happen next.
Text-Based Reasoning About Vector Graphics
By casting an image to a text-based representation, we can leverage the power of language models to learn alignment from SVG to visual primitives and generalize to unseen question-answering tasks.
Towards Better Generalization in Open-Domain Question Answering by Mitigating Context Memorization
In addition, it is still unclear how well an OpenQA model can transfer to completely new knowledge domains.
Fact Checking Beyond Training Set
We then focus on the reader component and propose to train it such that it is insensitive towards the order of claims and evidence documents.
From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models
This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis, providing valuable insights and directions for future research in chart understanding leveraging large foundation models.
Finer: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models
Recent advances in instruction-tuned Large Vision-Language Models (LVLMs) have imbued the models with the ability to generate high-level, image-grounded explanations with ease.
$\textit{L+M-24}$: Building a Dataset for Language + Molecules @ ACL 2024
Language-molecule models have emerged as an exciting direction for molecular discovery and understanding.
LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation
In this paper, we first investigate the potential of LVLM on multimodal misinformation detection.
EVEDIT: Event-based Knowledge Editing with Deductive Editing Boundaries
The dynamic nature of real-world information necessitates efficient knowledge editing (KE) in large language models (LLMs) for knowledge updating.
Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement
The increasing demand for personalized interactions with large language models (LLMs) calls for the development of methodologies capable of accurately and efficiently identifying user opinions and preferences.
ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback
The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future.
Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking
Pretrained large language models have revolutionized many applications but still face challenges related to cultural bias and a lack of cultural commonsense knowledge crucial for guiding cross-culture communication and interactions.
Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate
Fact-checking research has extensively explored verification but less so the generation of natural-language explanations, crucial for user trust.
Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain
In this paper, we introduce a new neural network architecture, termed LoGoNet, with a tailored self-supervised learning (SSL) method to mitigate such challenges.
Executable Code Actions Elicit Better LLM Agents
LLM agents are typically prompted to produce actions by generating JSON or text in a pre-defined format, which is usually limited by constrained action space (e. g., the scope of pre-defined tools) and restricted flexibility (e. g., inability to compose multiple tools).
Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences
In our experiments, we observed that such a model is prone to mislabeling the source entities, which can often appear in the text, as the target entities.
Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction
Fine-grained few-shot entity extraction in the chemical domain faces two unique challenges.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
The prominent large language models (LLMs) of today differ from past language models not only in size, but also in the fact that they are trained on a combination of natural language and formal language (code).
Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint
This includes an iterative version of the Direct Preference Optimization (DPO) algorithm for online settings, and a multi-step rejection sampling strategy for offline scenarios.
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning
This work inaugurates a new domain in factual error correction for chart captions, presenting a novel evaluation mechanism, and demonstrating an effective approach to ensuring the factuality of generated chart captions.
Factual Inconsistency Detection in Chart Captioning
Image Captioning
+1
Large Language Models on Graphs: A Comprehensive Survey
Besides, although LLMs have shown their pure text-based reasoning ability, it is underexplored whether such ability can be generalized to graphs (i. e., graph-based reasoning).
RESIN-EDITOR: A Schema-guided Hierarchical Event Graph Visualizer and Editor
In this paper, we present RESIN-EDITOR, an interactive event graph visualizer and editor designed for analyzing complex events.
InfoPattern: Unveiling Information Propagation Patterns in Social Media
Social media play a significant role in shaping public opinion and influencing ideological communities through information propagation.
ViStruct: Visual Structural Knowledge Extraction via Curriculum Guided Code-Vision Representation
We adopt a weakly-supervised approach to directly generate visual event structures from captions for ViStruct training, capitalizing on abundant image-caption pairs from the web.
TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction
In this work, we identify and address evaluation challenges, including inconsistency due to varying data assumptions or preprocessing steps, the insufficiency of current evaluation frameworks that may introduce dataset or data split bias, and the low reproducibility of some previous approaches.
R-Tuning: Teaching Large Language Models to Refuse Unknown Questions
This approach is formalized by first identifying the knowledge gap between parametric knowledge and the instruction tuning data.
DRESS: Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedback
The critique NLF identifies the strengths and weaknesses of the responses and is used to align the LVLMs with human preferences.
Defining a New NLP Playground
The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in the field's 80-year history.
Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
However, when it comes to information extraction - a classic task in natural language processing - most task-specific systems cannot align well with long-tail ad hoc extraction use cases for non-expert users.
Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design
Discovering novel catalysts requires complex reasoning involving multiple chemical properties and resultant trade-offs, leading to a combinatorial growth in the search space.
Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations
Open-domain dialog involves generating search queries that help obtain relevant knowledge for holding informative conversations.
Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting
Automatic response forecasting for news media plays a crucial role in enabling content producers to efficiently predict the impact of news releases and prevent unexpected negative outcomes such as social conflict and moral injury.
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions
Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks.
Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models
The dominance of proprietary LLMs has led to restricted access and raised information privacy concerns.
Factuality Challenges in the Era of Large Language Models
The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention.
Enabling Language Models to Implicitly Learn Self-Improvement
Specifically, we reformulate the training objective of reinforcement learning from human feedback (RLHF) -- instead of maximizing response quality for a given input, we maximize the quality gap of the response conditioned on a reference response.
Parameter-Efficient Tuning Helps Language Model Alignment
To this end, prior works incorporate controllable generations for alignment to make language models learn multiple preferences and provide outputs with different preferences during inference if asked.
CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks.
MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback
However, current evaluation protocols often emphasize benchmark performance with single-turn exchanges, neglecting the nuanced interactions among the user, LLMs, and external tools, while also underestimating the importance of natural language feedback from users.
Mitigating the Alignment Tax of RLHF
Building on the analysis and the observation that averaging different layers of the transformer leads to significantly different reward-tax trade-offs, we propose Adaptive Model Averaging (AMA) to adaptively find various combination ratios of model layers.
Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models
Based on this pipeline and the existing coarse-grained annotated dataset, we build the CURE benchmark to measure both the zero-shot reasoning performance and consistency of VLMs.
LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models
As a result, their performance suffers drastically on inputs longer than those encountered during training, substantially limiting their applications in real-world tasks involving long contexts such as encoding scientific articles, code repositories, or long dialogues.
Making Pre-trained Language Models both Task-solvers and Self-calibrators
In this work, we consider the practical scenario that we need to effectively utilize training samples to make PLMs both task-solvers and self-calibrators.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration
In this work, we propose Solo Performance Prompting (SPP), which transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas.
Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification
Event schemas are a form of world knowledge about the typical progression of events.
C-PMI: Conditional Pointwise Mutual Information for Turn-level Dialogue Evaluation
Existing reference-free turn-level evaluation metrics for chatbots inadequately capture the interaction between the user and the system.
SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design
We are given a small "personalized dataset" of 10-20 drug synergy relationships in the context of specific cancer cell targets.
Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations
Then we introduce BOSS, a Benchmark suite for Out-of-distribution robustneSS evaluation covering 5 tasks and 20 datasets.
OpenPI-C: A Better Benchmark and Stronger Baseline for Open-Vocabulary State Tracking
Open-vocabulary state tracking is a more practical version of state tracking that aims to track state changes of entities throughout a process without restricting the state space and entity space.
Information Association for Language Model Updating by Mitigating LM-Logical Discrepancy
To evaluate and address the core challenge, we propose a new task formulation of the information updating task that only requires the provision of an unstructured updating corpus and evaluates the performance of information updating on the generalizability to question-answer pairs pertaining to the updating information.
Enhanced Chart Understanding in Vision and Language Task via Cross-modal Pre-training on Plot Table Pairs
Building cross-model intelligence that can understand charts and communicate the salient information hidden behind them is an appealing challenge in the vision and language(V+L) community.
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework
In our experiments, we conduct a robustness evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation framework, and further show the rationality of each component in the framework.
Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning
With emerging online topics as a source for numerous new events, detecting unseen / rare event types presents an elusive challenge for existing event detection methods, where only limited data access is provided for training.
Non-Sequential Graph Script Induction via Multimedia Grounding
To automate the induction of such graph scripts for given tasks, we propose to take advantage of loosely aligned videos of people performing the tasks.
Measuring the Effect of Influential Messages on Varying Personas
Our analysis shows that the best-performing models are capable of predicting responses that are consistent with the personas, and as a byproduct, the task formulation also enables many interesting applications in the analysis of social network groups and their opinions, such as the discovery of extreme opinion groups.
Scientific Opinion Summarization: Meta-review Generation with Checklist-guided Iterative Introspection
Opinions in the scientific domain can be divergent, leading to controversy or consensus among reviewers.
SciMON: Scientific Inspiration Machines Optimized for Novelty
We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature.
Contextualized Literature-based Discovery
Link Prediction
+1
Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization
Motivated by how humans inspect factual inconsistency in summaries, we propose an interpretable fine-grained inconsistency detection model, FineGrainFact, which explicitly represents the facts in the documents and summaries with semantic frames extracted by semantic role labeling, and highlights the related semantic frames to predict inconsistency.
ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media
We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information.
LM-Switch: Lightweight Language Model Conditioning in Word Embedding Space
As pre-training and fine-tuning are costly and might negatively impact model performance, it is desired to efficiently adapt an existing model to different conditions such as styles, sentiments or narratives, when facing different audiences or scenarios.
Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning
A LERP is designed as a vector of probabilistic logical functions on the entity's neighboring sub-graph.
Ranked #9 on
Link Prediction
on WN18RR
Explaining Emergent In-Context Learning as Kernel Regression
Then, we empirically investigate the in-context behaviors of language models.
Understanding the Effect of Data Augmentation on Knowledge Distillation
However, such data augmentation methods are sub-optimal for knowledge distillation since the teacher model could provide label distributions and is more tolerant to semantic shifts.
RCOT: Detecting and Rectifying Factual Inconsistency in Reasoning by Reversing Chain-of-Thought
To detect factual inconsistency, RCoT first asks LLMs to reconstruct the problem based on generated solutions.
Inference-time Re-ranker Relevance Feedback for Neural Information Retrieval
Neural information retrieval often adopts a retrieve-and-rerank framework: a bi-encoder network first retrieves K (e. g., 100) candidates that are then re-ranked using a more powerful cross-encoder model to rank the better candidates higher.
Paxion: Patching Action Knowledge in Video-Language Foundation Models
Action knowledge involves the understanding of textual, visual, and temporal aspects of actions.
Ranked #19 on
Video Question Answering
on NExT-QA
(using extra training data)
LeTI: Learning to Generate from Textual Interactions
We explore LMs' potential to learn from textual interactions (LETI) that not only check their correctness with binary labels but also pinpoint and explain errors in their outputs through textual feedback.
Zero-shot Faithful Factual Error Correction
Faithfully correcting factual errors is critical for maintaining the integrity of textual knowledge bases and preventing hallucinations in sequence-to-sequence models.
Tool Learning with Foundation Models
Considering the lack of a systematic tool learning evaluation in prior works, we experiment with 18 representative tools and show the potential of current foundation models in skillfully utilizing tools.
SmartBook: AI-Assisted Situation Report Generation
Further, experiments show that expert analysts tend to add more information into the SmartBook reports, with only 2. 3% of the existing tokens being deleted, meaning SmartBook can serve as a useful foundation for analysts to build upon when creating intelligence reports.
GLEN: General-Purpose Event Detection for Thousands of Types
Finally, we perform error analysis and show that label noise is still the largest challenge for improving performance for this new dataset.
Human-in-the-Loop Schema Induction
Schema induction builds a graph representation explaining how events unfold in a scenario.
Ensemble Transfer Learning for Multilingual Coreference Resolution
Leveraging the idea that the coreferential links naturally exist between anchor texts pointing to the same article, our method builds a sizeable distantly-supervised dataset for the target language that consists of tens of thousands of documents.
SumREN: Summarizing Reported Speech about Events in News
A primary objective of news articles is to establish the factual record for an event, frequently achieved by conveying both the details of the specified event (i. e., the 5 Ws; Who, What, Where, When and Why regarding the event) and how people reacted to it (i. e., reported statements).
Open Relation and Event Type Discovery with Type Abstraction
To tackle this problem, we introduce the idea of type abstraction, where the model is prompted to generalize and name the type.
Retrieval-Augmented Generative Question Answering for Event Argument Extraction
We propose a retrieval-augmented generative QA model (R-GQA) for event argument extraction.
ADEPT: A DEbiasing PrompT Framework
Despite this, relatively few efforts have been made to debias PLMs by prompt tuning with continuous prompts compared to its discrete counterpart.
Zero-Shot Classification by Logical Reasoning on Natural Language Explanations
To this end, we propose the framework CLORE (Classification by LOgical Reasoning on Explanations).
Video Event Extraction via Tracking Visual States of Arguments
Video event extraction aims to detect salient events from a video and identify the arguments for each event as well as their semantic roles.
Open-Vocabulary Argument Role Prediction for Event Extraction
Specifically, we formulate the role prediction problem as an in-filling task and construct prompts for a pre-trained language model to generate candidate roles.
A Close Look into the Calibration of Pre-trained Language Models
We observe a consistent change in calibration performance across six factors.
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
For instance, we outperform the fully finetuning approaches on a KG completion benchmark by tuning only 1% of the parameters.
Ranked #5 on
Link Prediction
on UMLS
Code4Struct: Code Generation for Few-Shot Event Structure Prediction
As a case study, we formulate Event Argument Extraction (EAE) as converting text into event-argument structures that can be represented as a class object using code.
Language Model Pre-Training with Sparse Latent Typing
Modern large-scale Pre-trained Language Models (PLMs) have achieved tremendous success on a wide range of downstream tasks.
Ranked #6 on
Few-shot NER
on Few-NERD (INTRA)
(using extra training data)
Weakly-Supervised Temporal Article Grounding
Specifically, given an article and a relevant video, WSAG aims to localize all ``groundable'' sentences to the video, and these sentences are possibly at different semantic scales.
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation
However, these data augmentation methods either potentially cause shifts in decision boundaries (representation interpolation), are not expressive enough (token replacement), or introduce too much computational overhead (augmentation with models).
NormSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly
Norm discovery is important for understanding and reasoning about the acceptable behaviors and potential violations in human communication and interactions.
Towards a Unified Multi-Dimensional Evaluator for Text Generation
We re-frame NLG evaluation as a Boolean Question Answering (QA) task, and by guiding the model with different questions, we can use one evaluator to evaluate from multiple dimensions.
Learning to Decompose Visual Features with Latent Textual Prompts
Recent advances in pre-training vision-language models like CLIP have shown great potential in learning transferable visual representations.
Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks
Notably, our proposed $\text{Zemi}_\text{LARGE}$ outperforms T0-3B by 16% on all seven evaluation tasks while being 3. 9x smaller in model size.
Dynamic Global Memory for Document-level Argument Extraction
Extracting informative arguments of events from news articles is a challenging problem in information extraction, which requires a global contextual understanding of each document.
CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval
Given the absence of cross-lingual information retrieval datasets with claim-like queries, we train the retriever with our proposed Cross-lingual Inverse Cloze Task (X-ICT), a self-supervised algorithm that creates training instances by translating the title of a passage.
Ranked #1 on
Zero-shot Cross-lingual Fact-checking
on X-Fact
Cross-lingual Fact-checking
Cross-Lingual Information Retrieval
+4
Incorporating Task-specific Concept Knowledge into Script Learning
In this paper, we present Tetris, a new task of Goal-Oriented Script Completion.
Multimedia Generative Script Learning for Task Planning
Goal-oriented generative script learning aims to generate subsequent steps to reach a particular goal, which is an essential task to assist robots or humans in performing stereotypical activities.
Enhanced Knowledge Selection for Grounded Dialogues via Document Semantic Graphs
Providing conversation models with background knowledge has been shown to make open-domain dialogues more informative and engaging.
Schema-Guided Event Graph Completion
Existing link prediction or graph completion methods have difficulty dealing with event graphs because they are usually designed for a single large graph such as a social network or a knowledge graph, rather than multiple small dynamic event graphs.
Towards Fast Adaptation of Pretrained Contrastive Models for Multi-channel Video-Language Retrieval
We surprisingly find that discrete text tokens coupled with a pretrained contrastive text model yields the best performance, which can even outperform state-of-the-art on the iVQA and How2QA datasets without additional training on millions of video-text data.
Ranked #1 on
Video Question Answering
on iVQA
EA$^2$E: Improving Consistency with Event Awareness for Document-Level Argument Extraction
Events are inter-related in documents.
Seeded Hierarchical Clustering for Expert-Crafted Taxonomies
Practitioners from many disciplines (e. g., political science) use expert-crafted taxonomies to make sense of large, unlabeled corpora.
What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys
We constructed a new human-annotated dataset of human-written follow-up questions with dialogue history and labeled knowledge in the context of conversational surveys.
Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples, such as domain-specific captioning, question answering, and future event prediction.
Translation between Molecules and Natural Language
We present $\textbf{MolT5}$ $-$ a self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings.
Ranked #4 on
Text-based de novo Molecule Generation
on ChEBI-20
Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval
On two public IR benchmarks, we empirically show that the proposed method helps improve both the model's attention patterns and retrieval performance, including in zero-shot settings.
Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation
Despite recent advances in detecting fake news generated by neural models, their results are not readily applicable to effective detection of human-written disinformation.
Rethinking Task Sampling for Few-shot Vision-Language Transfer Learning
While much literature has been devoted to exploring alternative optimization strategies, we identify another essential aspect towards effective few-shot transfer learning, task sampling, which is previously only be viewed as part of data pre-processing in MAML.
A Weibo Dataset for the 2022 Russo-Ukrainian Crisis
Online social networks such as Twitter and Weibo play an important role in how people stay informed and exchange reactions.
Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking
We use the profile to query the indexed search engine to retrieve candidate entities.
P4E: Few-Shot Event Detection as Prompt-Guided Identification and Localization
Our framework decomposes event detection into an identification task and a localization task.
Semi-supervised New Event Type Induction and Description via Contrastive Loss-Enforced Batch Attention
In contrast, we present a novel approach to semi-supervised new event type induction using a masked contrastive loss, which learns similarities between event mentions by enforcing an attention mechanism over the data minibatch.
CLIP-Event: Connecting Text and Images with Event Structures
Vision-language (V+L) pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text.
MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding
Specifically, the task involves multi-hop questions that require reasoning over image-caption pairs to identify the grounded visual object being referred to and then predicting a span from the news body text to answer the question.
NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge
In this work, we present NewsClaims, a new benchmark for attribute-aware claim detection in the news domain.
Sketching as a Tool for Understanding and Accelerating Self-attention for Long Sequences
Transformer-based models are not efficient in processing long sequences due to the quadratic space and time complexity of the self-attention modules.
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nyström Method
Transformers are expensive to train due to the quadratic time and space complexity in the self-attention mechanism.
Learning Invariant Representations on Multilingual Language Models for Unsupervised Cross-Lingual Transfer
Recent advances in neural modeling have produced deep multilingual language models capable of extracting cross-lingual knowledge from unparallel texts, as evidenced by their decent zero-shot transfer performance.
Joint Multimedia Event Extraction from Video and Article
We introduce the new task of Video MultiMedia Event Extraction (Video M2E2) and propose two novel components to build the first system towards this task.
Chemical-Reaction-Aware Molecule Representation Learning
Molecule representation learning (MRL) methods aim to embed molecules into a real vector space.
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training
We study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw text with entity types in a knowledge base.
Corpus-based Open-Domain Event Type Induction
As events of the same type could be expressed in multiple ways, we propose to represent each event type as a cluster of <predicate sense, object head> pairs.
BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks
Biomedical entity linking is the task of linking entity mentions in a biomedical document to referent entities in a knowledge base.
IMG2SMI: Translating Molecular Structure Images to Simplified Molecular-input Line-entry System
A large portion of chemistry literature focuses on new molecules and reactions between molecules.
Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation
Automated knowledge discovery from trending chemical literature is essential for more efficient biomedical research.
A Unified Transformer-based Framework for Duplex Text Normalization
In addition, we also create a cleaned dataset from the Spoken Wikipedia Corpora for German and report the performance of our systems on the dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Event-Centric Natural Language Processing
This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text.
Joint Detection and Coreference Resolution of Entities and Events with Document-level Context Aggregation
The tasks performed by this system are entity and event identification, typing, and coreference resolution.
InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection
To defend against machine-generated fake news, an effective mechanism is urgently needed.
Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation
In this paper, we propose a novel biomedical Information Extraction (IE) model to tackle these two challenges and extract scientific entities and events from English research papers.
Separating Skills and Concepts for Novel Visual Question Answering
Generalization to out-of-distribution data has been a problem for Visual Question Answering (VQA) models.
HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction
Text-to-Graph extraction aims to automatically extract information graphs consisting of mentions and types from natural language texts.
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric)
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System
We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video).
Deep Learning on Graphs for Natural Language Processing
Due to its great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graph techniques (i. e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems.
Event Time Extraction and Propagation via Graph Attention Networks
Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work.
Abstract Meaning Representation Guided Graph Encoding and Decoding for Joint Information Extraction
The tasks of Rich Semantic Parsing, such as Abstract Meaning Representation (AMR), share similar goals with Information Extraction (IE) to convert natural language texts into structured semantic representations.
Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference
It then uses an entity linker to form a knowledge graph containing relevant background knowledge for the the entity mentions in the text.
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method
Transformers are expensive to train due to the quadratic time and space complexity in the self-attention mechanism.
Stage-wise Fine-tuning for Graph-to-Text Generation
Graph-to-text generation has benefited from pre-trained language models (PLMs) in achieving better performance than structured graph encoders.
Ranked #3 on
Data-to-Text Generation
on WebNLG
(using extra training data)
Learning Shared Semantic Space for Speech-to-Text Translation
By projecting audio and text features to a common semantic representation, Chimera unifies MT and ST tasks and boosts the performance on ST benchmarks, MuST-C and Augmented Librispeech, to a new state-of-the-art.
VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension
Existing models on Machine Reading Comprehension (MRC) require complex model architecture for effectively modeling long texts with paragraph representation and classification, thereby making inference computationally inefficient for production use.
Towards Robust Neural Retrieval Models with Synthetic Pre-Training
Recent work has shown that commonly available machine reading comprehension (MRC) datasets can be used to train high-performance neural information retrieval (IR) systems.
Document-Level Event Argument Extraction by Conditional Generation
On the task of argument extraction, we achieve an absolute gain of 7. 6% F1 and 5. 7% F1 over the next best model on the RAMS and WikiEvents datasets respectively.
Document-level Event Extraction
Event Argument Extraction
+2
The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction
We introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations.
Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations
For example, with "add milk to my cart", a customer may refer to a certain organic product, while some customers may want to re-order products they regularly purchase.
Efficient Attentions for Long Document Summarization
The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization.
A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution
Event coreference resolution is an important research problem with many applications.
Controllable and Diverse Text Generation in E-commerce
The algorithm is injected into a Conditional Variational Autoencoder (CVAE), allowing \textit{Apex} to control both (i) the order of keywords in the generated sentences (conditioned on the input keywords and their order), and (ii) the trade-off between diversity and accuracy.
White Paper: Challenges and Considerations for the Creation of a Large Labelled Repository of Online Videos with Questionable Content
This white paper presents a summary of the discussions regarding critical considerations to develop an extensive repository of online videos annotated with labels indicating questionable content.
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning
Pre-trained Language Models (PLMs) have shown superior performance on various downstream Natural Language Processing (NLP) tasks.
Learning from Lexical Perturbations for Consistent Visual Question Answering
Existing Visual Question Answering (VQA) models are often fragile and sensitive to input variations.
MUSE: Textual Attributes Guided Portrait Painting Generation
Experiments show that our approach can accurately illustrate 78% textual attributes, which also help MUSE capture the subject in a more creative and expressive way.
KompaRe: A Knowledge Graph Comparative Reasoning System
In detail, we develop KompaRe, the first of its kind prototype system that provides comparative reasoning capability over large knowledge graphs.
Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation
Despite significant progress, state-of-the-art abstractive summarization methods are still prone to hallucinate content inconsistent with the source document.
Global Attention for Name Tagging
Many name tagging approaches use local contextual information with much success, but fail when the local context is ambiguous or limited.
Text Classification Using Label Names Only: A Language Model Self-Training Approach
In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents.
ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis
To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison.
Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding
Aspect-based sentiment analysis of review texts is of great value for understanding user feedback in a fine-grained manner.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
A Survey of Knowledge-Enhanced Text Generation
To address this issue, researchers have considered incorporating various forms of knowledge beyond the input text into the generation models.
Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding
Linguistic steganography studies how to hide secret messages in natural language cover texts.
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction
We propose a novel Sequence-to-Unordered-Multi-Tree (Seq2UMTree) model to minimize the effects of exposure bias by limiting the decoding length to three within a triplet and removing the order among triplets.
A Joint Neural Model for Information Extraction with Global Features
OneIE performs end-to-end IE in four stages: (1) Encoding a given sentence as contextualized word representations; (2) Identifying entity mentions and event triggers as nodes; (3) Computing label scores for all nodes and their pairwise links using local classifiers; (4) Searching for the globally optimal graph with a beam decoder.
GAIA: A Fine-grained Multimedia Knowledge Extraction System
We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multimedia data from various sources and languages as input, and creates a coherent, structured knowledge base, indexing entities, relations, and events, following a rich, fine-grained ontology.
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions.
Cross-media Structured Common Space for Multimedia Event Extraction
We introduce a new task, MultiMedia Event Extraction (M2E2), which aims to extract events and their arguments from multimedia documents.
Cross-lingual Structure Transfer for Zero-resource Event Extraction
Most of the current cross-lingual transfer learning methods for Information Extraction (IE) have been only applied to name tagging.
Training with Streaming Annotation
In this paper, we address a practical scenario where training data is released in a sequence of small-scale batches and annotation in earlier phases has lower quality than the later counterparts.
Learning to Learn Words from Visual Scenes
Language acquisition is the process of learning words from the surrounding scene.
Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining
Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking.
Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction
In this paper we tackle two unique challenges in biomedical relation extraction.
Zero-Shot Cross-lingual Name Retrieval for Low-Resource Languages
In this paper we address a challenging cross-lingual name retrieval task.
An Attentive Fine-Grained Entity Typing Model with Latent Type Representation
In addition, we propose a two-step mention-aware attention mechanism to enable the model to focus on important words in mentions and contexts.
Cross-lingual Structure Transfer for Relation and Event Extraction
The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages.
Low-Resource Name Tagging Learned with Weakly Labeled Data
Name tagging in low-resource languages or domains suffers from inadequate training data.
Raw-to-End Name Entity Recognition in Social Media
Our model neither requires the conversion from character sequences to word sequences, nor assumes tokenizer can correctly detect all word boundaries.
Reliability-aware Dynamic Feature Composition for Name Tagging
We design a set of word frequency-based reliability signals to indicate the quality of each word embedding.
Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization
Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models for generating summaries unfocused.
Multilingual Entity, Relation, Event and Human Value Extraction
This paper demonstrates a state-of-the-art end-to-end multilingual (English, Russian, and Ukrainian) knowledge extraction system that can perform entity discovery and linking, relation extraction, event extraction, and coreference.
Cross-lingual NIL Entity Clustering for Low-resource Languages
Clustering unlinkable entity mentions across documents in multiple languages (cross-lingual NIL Clustering) is an important task as part of Entity Discovery and Linking (EDL).
Cross-lingual Multi-Level Adversarial Transfer to Enhance Low-Resource Name Tagging
We focus on improving name tagging for low-resource languages using annotations from related languages.
