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Found 100 papers, 46 papers with code
PathQG: Neural Question Generation from Facts
Human evaluation also proves that our model is able to generate relevant and informative questions.
A Structure-Aware Argument Encoder for Literature Discourse Analysis
Existing research for argument representation learning mainly treats tokens in the sentence equally and ignores the implied structure information of argumentative context.
A Progressive Framework for Role-Aware Rumor Resolution
Existing works on rumor resolution have shown great potential in recognizing word appearance and user participation.
A Two Stage Adaptation Framework for Frame Detection via Prompt Learning
Frame detection aims to automatically analyze framing strategy.
基于多质心异质图学习的社交网络用户建模(User Representation Learning based on Multi-centroid Heterogeneous Graph Neural Networks)
“用户建模已经引起了学术界和工业界的广泛关注。现有的方法大多侧重于将用户间的人际关系融入社区, 而用户生成的内容(如帖子)却没有得到很好的研究。在本文中, 我们通过实际舆情传播相关的分析表明, 在舆情传播过程中对用户属性进行研究的重要作用, 并且提出了用户资料数据的筛选方法。同时, 我们提出了一种通过异构多质心图池为用户捕获更多不同社区特征的建模。我们首先构造了一个由用户和关键字组成的异质图, 并在其上采用了一个异质图神经网络。为了方便用户建模的图表示, 提出了一种多质心图池化机制, 将多质心的集群特征融入到表示学习中。在三个基准数据集上的大量实验表明了该方法的有效性。”
An Edge-Enhanced Hierarchical Graph-to-Tree Network for Math Word Problem Solving
Specifically, an edge-enhanced hierarchical graph encoder is used to incorporate edge label information.
DELAN: Dual-Level Alignment for Vision-and-Language Navigation by Cross-Modal Contrastive Learning
For task completion, the agent needs to align and integrate various navigation modalities, including instruction, observation and navigation history.
Debatrix: Multi-dimensinal Debate Judge with Iterative Chronological Analysis Based on LLM
How can we construct an automated debate judge to evaluate an extensive, vibrant, multi-turn debate?
ALaRM: Align Language Models via Hierarchical Rewards Modeling
We introduce ALaRM, the first framework modeling hierarchical rewards in reinforcement learning from human feedback (RLHF), which is designed to enhance the alignment of large language models (LLMs) with human preferences.
Android in the Zoo: Chain-of-Action-Thought for GUI Agents
To address this, this work presents Chain-of-Action-Thought (dubbed CoAT), which takes the description of the previous actions, the current screen, and more importantly the action thinking of what actions should be performed and the outcomes led by the chosen action.
Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation
Simulating the response of the public and forecasting the potential impact has become increasingly important.
SoMeLVLM: A Large Vision Language Model for Social Media Processing
The powerful Large Vision Language Models make it possible to handle a variety of tasks simultaneously, but even with carefully designed prompting methods, the general domain models often fall short in aligning with the unique speaking style and context of social media tasks.
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation
Towards a more scalable, robust and fine-grained evaluation, we implement six reframing operations to construct evolving instances testing LLMs against diverse queries, data noise and probing their problem-solving sub-abilities.
Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs
Our analysis of GPT-series models over a rule subset reveals significant gaps in LLMs' logic understanding compared to human performance, especially in compositional and structural complex rules with certain bias patterns.
AI Hospital: Interactive Evaluation and Collaboration of LLMs as Intern Doctors for Clinical Diagnosis
To simulate the procedure, we collect high-quality medical records to create patient, examiner, and medical director agents.
CauESC: A Causal Aware Model for Emotional Support Conversation
Emotional Support Conversation aims at reducing the seeker's emotional distress through supportive response.
Human vs. LMMs: Exploring the Discrepancy in Emoji Interpretation and Usage in Digital Communication
Leveraging Large Multimodal Models (LMMs) to simulate human behaviors when processing multimodal information, especially in the context of social media, has garnered immense interest due to its broad potential and far-reaching implications.
Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation
The results show the competitiveness of our proposed framework and evaluator in counter-argument generation tasks.
K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning
Automatic psychological counseling requires mass of professional knowledge that can be found in online counseling forums.
Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining
In this paper, we propose the Hierarchical Argumentation Graph (Hi-ArG), a new structure to organize arguments.
GPT-4V(ision) as A Social Media Analysis Engine
Our investigation begins with a preliminary quantitative analysis for each task using existing benchmark datasets, followed by a careful review of the results and a selection of qualitative samples that illustrate GPT-4V's potential in understanding multimodal social media content.
DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning
We propose Multiple Experts Fine-tuning Framework to build a financial large language model (LLM), DISC-FinLLM.
ReForm-Eval: Evaluating Large Vision Language Models via Unified Re-Formulation of Task-Oriented Benchmarks
Recent years have witnessed remarkable progress in the development of large vision-language models (LVLMs).
DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services
We propose DISC-LawLLM, an intelligent legal system utilizing large language models (LLMs) to provide a wide range of legal services.
DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation
We propose DISC-MedLLM, a comprehensive solution that leverages Large Language Models (LLMs) to provide accurate and truthful medical response in end-to-end conversational healthcare services.
Breaking Down the Task: A Unit-Grained Hybrid Training Framework for Vision and Language Decision Making
Vision language decision making (VLDM) is a challenging multimodal task.
DSRM: Boost Textual Adversarial Training with Distribution Shift Risk Minimization
Adversarial training is one of the best-performing methods in improving the robustness of deep language models.
Valley: Video Assistant with Large Language model Enhanced abilitY
Large language models (LLMs), with their remarkable conversational capabilities, have demonstrated impressive performance across various applications and have emerged as formidable AI assistants.
RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction
However, general matching methods lack explicit modeling of the above matching pattern.
Actively Supervised Clustering for Open Relation Extraction
The key to the setting is selecting which instances to label.
Open Set Relation Extraction via Unknown-Aware Training
Inspired by text adversarial attacks, we adaptively apply small but critical perturbations to original training instances and thus synthesizing negative instances that are more likely to be mistaken by the model as known relations.
KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition
Symptom diagnosis in medical conversations aims to correctly extract both symptom entities and their status from the doctor-patient dialogue.
Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs
The results demonstrate the effectiveness of our method on logical reasoning over KGs in both inductive and transductive settings.
AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation
Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance.
Unifying Structure Reasoning and Language Model Pre-training for Complex Reasoning
Recent pre-trained language models (PLMs) equipped with foundation reasoning skills have shown remarkable performance on downstream complex tasks.
Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering
In this paper, we propose an interpretable stepwise reasoning framework to incorporate both single-hop supporting sentence identification and single-hop question generation at each intermediate step, and utilize the inference of the current hop for the next until reasoning out the final result.
A Unified Continuous Learning Framework for Multi-modal Knowledge Discovery and Pre-training
These two steps are iteratively performed in our framework for continuous learning.
DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom Representations
Diagnosis-oriented dialogue system queries the patient's health condition and makes predictions about possible diseases through continuous interaction with the patient.
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation
Dialog response generation in open domain is an important research topic where the main challenge is to generate relevant and diverse responses.
A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets
In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience.
MVPTR: Multi-Level Semantic Alignment for Vision-Language Pre-Training via Multi-Stage Learning
In MVPTR, we follow the nested structure of both modalities to introduce concepts as high-level semantics.
Reasoning Chain Based Adversarial Attack for Multi-hop Question Answering
The results demonstrate significant performance reduction on both answer and supporting facts prediction, verifying the effectiveness of our reasoning chain based attack method for multi-hop reasoning models and the vulnerability of them.
Curriculum Learning for Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) is a task where an agent navigates in an embodied indoor environment under human instructions.
Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training
Aspect-based sentiment analysis aims to identify the sentiment polarity of a specific aspect in product reviews.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
SAM: A Self-adaptive Attention Module for Context-Aware Recommendation System
In this work, we propose a novel and general self-adaptive module, the Self-adaptive Attention Module (SAM), which adjusts the selection bias by capturing contextual information based on its representation.
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations
We study the problem of coarse-grained response selection in retrieval-based dialogue systems.
Constructing Phrase-level Semantic Labels to Form Multi-Grained Supervision for Image-Text Retrieval
Existing research for image text retrieval mainly relies on sentence-level supervision to distinguish matched and mismatched sentences for a query image.
A Partition Filter Network for Joint Entity and Relation Extraction
In our encoder, we leverage two gates: entity and relation gate, to segment neurons into two task partitions and one shared partition.
Ranked #1 on
Relation Extraction
on ADE Corpus
Fine-Grained Element Identification in Complaint Text of Internet Fraud
Existing system dealing with online complaint provides a final decision without explanations.
From LSAT: The Progress and Challenges of Complex Reasoning
Complex reasoning aims to draw a correct inference based on complex rules.
Math Word Problem Solving with Explicit Numerical Values
In recent years, math word problem solving has received considerable attention and achieved promising results, but previous methods rarely take numerical values into consideration.
Align Voting Behavior with Public Statements for Legislator Representation Learning
In addition, we propose a novel task, namely hashtag usage prediction to model the ideology of legislators on Twitter.
TCIC: Theme Concepts Learning Cross Language and Vision for Image Captioning
Considering that theme concepts can be learned from both images and captions, we propose two settings for their representations learning based on TTN.
Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues
It is a common practice for recent works in vision language cross-modal reasoning to adopt a binary or multi-choice classification formulation taking as input a set of source image(s) and textual query.
Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text
Logical reasoning of text requires understanding critical logical information in the text and performing inference over them.
Ranked #7 on
Reading Comprehension
on ReClor
Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure
Numerous online conversations are produced on a daily basis, resulting in a pressing need to conversation understanding.
Mask Attention Networks: Rethinking and Strengthen Transformer
We therefore introduce a new layer named dynamic mask attention network (DMAN) with a learnable mask matrix which is able to model localness adaptively.
Ranked #11 on
Machine Translation
on WMT2014 English-German
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
To guarantee user acceptability, all the text transformations are linguistically based, and we provide a human evaluation for each one.
An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-Level Structural Information
In this paper, we focus on the problem of unsupervised image-sentence matching.
An Enhanced Knowledge Injection Model for Commonsense Generation
Commonsense generation aims at generating plausible everyday scenario description based on a set of provided concepts.
Modeling Evolution of Message Interaction for Rumor Resolution
Previous work for rumor resolution concentrates on exploiting time-series characteristics or modeling topology structure separately.
Automatic Term Name Generation for Gene Ontology: Task and Dataset
Terms contained in Gene Ontology (GO) have been widely used in biology and bio-medicine.
Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection
We study the detection of propagandistic text fragments in news articles.
Hierarchical Reinforcement Learning for Automatic Disease Diagnosis
Existing approaches usually employ a flat policy structure that treat all symptoms and diseases equally for action making.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Salience Estimation with Multi-Attention Learning for Abstractive Text Summarization
Different from neural machine translation, in the task of text summarization, salience estimation for words, phrases or sentences is a critical component, since the output summary is a distillation of the input text.
Storytelling from an Image Stream Using Scene Graphs
In particular, on the within-image level, we employ a Graph Convolution Network (GCN) to enrich local fine-grained region representations of objects on scene graphs.
Ranked #7 on
Visual Storytelling
on VIST
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa.
Ranked #1 on
Entity Typing
on Open Entity
Variational Template Machine for Data-to-Text Generation
We propose the variational template machine (VTM), a novel method to generate text descriptions from data tables.
Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication
Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically.
Ranked #9 on
Visual Storytelling
on VIST
Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models
Human and metrics evaluation on both LSTM models and BERT Transformer models on multiple datasets show that our algorithms outperform prior hierarchical explanation algorithms.
Discrete Argument Representation Learning for Interactive Argument Pair Identification
In this paper, we focus on extracting interactive argument pairs from two posts with opposite stances to a certain topic.
Extract, Transform and Filling: A Pipeline Model for Question Paraphrasing based on Template
Question paraphrasing aims to restate a given question with different expressions but keep the original meaning.
Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph
In this paper, we first construct a dialogue symptom diagnosis dataset based on an online medical forum with a large amount of dialogues between patients and doctors.
A Lexicon-Based Graph Neural Network for Chinese NER
Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that sequentially track character and word information have achieved great success.
Ranked #13 on
Chinese Named Entity Recognition
on OntoNotes 4
Chinese Named Entity Recognition
named-entity-recognition
+3
Multi-Module System for Open Domain Chinese Question Answering over Knowledge Base
For the task of open domain Knowledge Based Question Answering in CCKS2019, we propose a method combining information retrieval and semantic parsing.
Bridging by Word: Image Grounded Vocabulary Construction for Visual Captioning
Existing research usually employs the architecture of CNN-RNN that views the generation as a sequential decision-making process and the entire dataset vocabulary is used as decoding space.
Few-shot Classification on Graphs with Structural Regularized GCNs
We consider the fundamental problem of semi-supervised node classification in attributed graphs with a focus on \emph{few-shot} learning.
A Joint Model of Conversational Discourse Latent Topics on Microblogs
To address this issue, we organize microblog messages as conversation trees based on their reposting and replying relations, and propose an unsupervised model that jointly learns word distributions to represent: (1) different roles of conversational discourse, and (2) various latent topics in reflecting content information.
Incorporating Topic Aspects for Online Comment Convincingness Evaluation
In this paper, we propose to incorporate topic aspects information for online comments convincingness evaluation.
Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning
Automatic essay scoring (AES) is the task of assigning grades to essays without human interference.
A Joint Model of Conversational Discourse and Latent Topics on Microblogs
To address this issue, we organize microblog messages as conversation trees based on their reposting and replying relations, and propose an unsupervised model that jointly learns word distributions to represent: 1) different roles of conversational discourse, 2) various latent topics in reflecting content information.
Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model
In this paper, we investigate the issue of persuasiveness evaluation for argumentative comments.
A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators
Visual Question Generation (VQG) aims to ask natural questions about an image automatically.
Task-oriented Dialogue System for Automatic Diagnosis
In this paper, we make a move to build a dialogue system for automatic diagnosis.
A Joint Framework for Argumentative Text Analysis Incorporating Domain Knowledge
For argumentation mining, there are several sub-tasks such as argumentation component type classification, relation classification.
Using Relevant Public Posts to Enhance News Article Summarization
A news article summary usually consists of 2-3 key sentences that reflect the gist of that news article.
The CUHK Discourse TreeBank for Chinese: Annotating Explicit Discourse Connectives for the Chinese TreeBank
The lack of open discourse corpus for Chinese brings limitations for many natural language processing tasks.
Quantising Opinions for Political Tweets Analysis
There have been increasing interests in recent years in analyzing tweet messages relevant to political events so as to understand public opinions towards certain political issues.
