Search Results for author:
Found 158 papers, 70 papers with code
Finding the Dominant Winning Ticket in Pre-Trained Language Models
Empirically, we show that (a) the dominant winning ticket can achieve performance that is comparable with that of the full-parameter model, (b) the dominant winning ticket is transferable across different tasks, (c) and the dominant winning ticket has a natural structure within each parameter matrix.
Combining Curriculum Learning and Knowledge Distillation for Dialogue Generation
Hence, in this paper, we introduce a combination of curriculum learning and knowledge distillation for efficient dialogue generation models, where curriculum learning can help knowledge distillation from data and model aspects.
Selection and Generation: Learning towards Multi-Product Advertisement Post Generation
(2) generate a post including selected products via the MGenNet (Multi-Generator Network).
Understanding Procedural Text using Interactive Entity Networks
Recent efforts have made great progress to track multiple entities in a procedural text, but usually treat each entity separately and ignore the fact that there are often multiple entities interacting with each other during one process, some of which are even explicitly mentioned.
Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations
In this paper, we propose a reciprocal learning approach to jointly optimize a knowledge retriever and a response ranker for knowledge-grounded response retrieval without ground-truth knowledge labels.
Combining Impression Feature Representation for Multi-turn Conversational Question Answering
Multi-turn conversational Question Answering (ConvQA) is a practical task that requires the understanding of conversation history, such as previous QA pairs, the passage context, and current question.
Plan-CVAE: A Planning-based Conditional Variational Autoencoder for Story Generation
Story generation is a challenging task of automatically creating natural languages to describe a sequence of events, which requires outputting text with not only a consistent topic but also novel wordings.
A Novel Joint Framework for Multiple Chinese Events Extraction
The task of argument extraction is converted to event relation triple extraction.
ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation
To enable the chatbot to foresee the dialogue future, we design a beam-search-like roll-out strategy for dialogue future simulation using a typical dialogue generation model and a dialogue selector.
Multi-Granularity Structural Knowledge Distillation for Language Model Compression
Transferring the knowledge to a small model through distillation has raised great interest in recent years.
StyleChat: Learning Recitation-Augmented Memory in LLMs for Stylized Dialogue Generation
Furthermore, although many prompt-based methods have been proposed to accomplish specific tasks, their performance in complex real-world scenarios involving a wide variety of dialog styles further enhancement.
HawkEye: Training Video-Text LLMs for Grounding Text in Videos
Video-text Large Language Models (video-text LLMs) have shown remarkable performance in answering questions and holding conversations on simple videos.
Ranked #3 on
Video Question Answering
on MVBench
What Makes Quantization for Large Language Models Hard? An Empirical Study from the Lens of Perturbation
Our findings reveal several connections between the properties of perturbations and LLM performance, providing insights into the failure cases of uniform quantization and suggesting potential solutions to improve the robustness of LLM quantization.
PPTC-R benchmark: Towards Evaluating the Robustness of Large Language Models for PowerPoint Task Completion
The growing dependence on Large Language Models (LLMs) for finishing user instructions necessitates a comprehensive understanding of their robustness to complex task completion in real-world situations.
Probing Multimodal Large Language Models for Global and Local Semantic Representations
The advancement of Multimodal Large Language Models (MLLMs) has greatly accelerated the development of applications in understanding integrated texts and images.
Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering
Open-ended question answering requires models to find appropriate evidence to form well-reasoned, comprehensive and helpful answers.
LSTP: Language-guided Spatial-Temporal Prompt Learning for Long-form Video-Text Understanding
Despite progress in video-language modeling, the computational challenge of interpreting long-form videos in response to task-specific linguistic queries persists, largely due to the complexity of high-dimensional video data and the misalignment between language and visual cues over space and time.
STAIR: Spatial-Temporal Reasoning with Auditable Intermediate Results for Video Question Answering
However, most models can only handle simple videos in terms of temporal reasoning, and their performance tends to drop when answering temporal-reasoning questions on long and informative videos.
Multi-Granularity Information Interaction Framework for Incomplete Utterance Rewriting
Recent approaches in Incomplete Utterance Rewriting (IUR) fail to capture the source of important words, which is crucial to edit the incomplete utterance, and introduce words from irrelevant utterances.
Relation-Aware Question Answering for Heterogeneous Knowledge Graphs
To address this issue, we construct a \textbf{dual relation graph} where each node denotes a relation in the original KG (\textbf{primal entity graph}) and edges are constructed between relations sharing same head or tail entities.
A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Models
While large language models exhibit remarkable performance in the Question Answering task, they are susceptible to hallucinations.
Language Models can be Logical Solvers
Despite their impressive performance, any parsing errors will inevitably result in the failure of the execution of the external logical solver and no answer to the logical questions.
PPTC Benchmark: Evaluating Large Language Models for PowerPoint Task Completion
Recent evaluations of Large Language Models (LLMs) have centered around testing their zero-shot/few-shot capabilities for basic natural language tasks and their ability to translate instructions into tool APIs.
Improving Input-label Mapping with Demonstration Replay for In-context Learning
The effectiveness of ICL can be attributed to the strong language modeling capabilities of large language models (LLMs), which enable them to learn the mapping between input and labels based on in-context demonstrations.
From Simple to Complex: A Progressive Framework for Document-level Informative Argument Extraction
These methods extract events according to their appearance order in the document, however, the event that appears in the first sentence does not mean that it is the easiest to extract.
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression
In particular, our approach extracts knowledge from LLMs to construct a knowledge store, from which the small-scale model can retrieve relevant information and leverage it for effective inference.
SCALE: Synergized Collaboration of Asymmetric Language Translation Engines
In this paper, we introduce SCALE, a collaborative framework that connects compact Specialized Translation Models (STMs) and general-purpose Large Language Models (LLMs) as one unified translation engine.
Teaching Text-to-Image Models to Communicate in Dialog
In this paper, we focus on the innovative dialog-to-image generation task, where the model synthesizes a high-resolution image aligned with the given dialog context as a response.
CharacterChat: Learning towards Conversational AI with Personalized Social Support
In our modern, fast-paced, and interconnected world, the importance of mental well-being has grown into a matter of great urgency.
Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction
Experiments on the BEA-2019 shared task and the CoNLL-2014 shared task have shown that AMR-GEC performs comparably to a set of strong baselines with a large number of synthetic data.
Class-Incremental Learning based on Label Generation
Despite the great success of pre-trained language models, it is still a challenge to use these models for continual learning, especially for the class-incremental learning (CIL) setting due to catastrophic forgetting (CF).
From the One, Judge of the Whole: Typed Entailment Graph Construction with Predicate Generation
Entailment Graphs (EGs) have been constructed based on extracted corpora as a strong and explainable form to indicate context-independent entailment relations in natural languages.
MoviePuzzle: Visual Narrative Reasoning through Multimodal Order Learning
We introduce MoviePuzzle, a novel challenge that targets visual narrative reasoning and holistic movie understanding.
How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension
We further analyze how well different paradigms of current multi-answer MRC models deal with different types of multi-answer instances.
Shuo Wen Jie Zi: Rethinking Dictionaries and Glyphs for Chinese Language Pre-training
We introduce CDBERT, a new learning paradigm that enhances the semantics understanding ability of the Chinese PLMs with dictionary knowledge and structure of Chinese characters.
The Magic of IF: Investigating Causal Reasoning Abilities in Large Language Models of Code
Causal reasoning, the ability to identify cause-and-effect relationship, is crucial in human thinking.
PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models
While transformer-based pre-trained language models (PLMs) have dominated a number of NLP applications, these models are heavy to deploy and expensive to use.
VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions
Video-grounded dialogue understanding is a challenging problem that requires machine to perceive, parse and reason over situated semantics extracted from weakly aligned video and dialogues.
More than Classification: A Unified Framework for Event Temporal Relation Extraction
After examining the relation definitions in various ETRE tasks, we observe that all relations can be interpreted using the start and end time points of events.
RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank
In this paper, we propose a novel approach, RankCSE, for unsupervised sentence representation learning, which incorporates ranking consistency and ranking distillation with contrastive learning into a unified framework.
Dealing with Cross-Task Class Discrimination in Online Continual Learning
A novel optimization objective with a gradient-based adaptive method is proposed to dynamically deal with the problem in the online CL process.
Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast
Existing research has shown that a multilingual pre-trained language model fine-tuned with one (source) language also performs well on downstream tasks for non-source languages, even though no fine-tuning is done on these languages.
Decouple knowledge from parameters for plug-and-play language modeling
The key intuition is to decouple the knowledge storage from model parameters with an editable and scalable key-value memory and leverage knowledge in an explainable manner by knowledge retrieval in the DPM.
Smart Word Suggestions for Writing Assistance
The training dataset comprises over 3. 7 million sentences and 12. 7 million suggestions generated through rules.
Synergistic Interplay between Search and Large Language Models for Information Retrieval
Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern retrieval models (RMs).
A Frustratingly Easy Improvement for Position Embeddings via Random Padding
Since the embeddings of rear positions are updated fewer times than the front position embeddings, the rear ones may not be properly trained.
Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory
In this paper, by exploring the duality of the primal problem: better generation also prompts better memory, we propose a novel framework, selfmem, which addresses this limitation by iteratively employing a retrieval-augmented generator to create an unbounded memory pool and using a memory selector to choose one output as memory for the subsequent generation round.
Learning to Plan with Natural Language
To obtain it, we propose the Learning to Plan method, which involves two phases: (1) In the first learning task plan phase, it iteratively updates the task plan with new step-by-step solutions and behavioral instructions, which are obtained by prompting LLMs to derive from training error feedback.
Can BERT Refrain from Forgetting on Sequential Tasks? A Probing Study
Large pre-trained language models help to achieve state of the art on a variety of natural language processing (NLP) tasks, nevertheless, they still suffer from forgetting when incrementally learning a sequence of tasks.
Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension
We convert KB subgraphs into passages to narrow the gap between KB schemas and questions, which enables our model to benefit from recent advances in multilingual pre-trained language models (MPLMs) and cross-lingual machine reading comprehension (xMRC).
Cross-Lingual Question Answering
Machine Reading Comprehension
Towards Personalized Review Summarization by Modeling Historical Reviews from Customer and Product Separately
Review summarization is a non-trivial task that aims to summarize the main idea of the product review in the E-commerce website.
EZInterviewer: To Improve Job Interview Performance with Mock Interview Generator
The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource.
Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order
Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline.
Adam: Dense Retrieval Distillation with Adaptive Dark Examples
Different from previous works that only rely on one positive and hard negatives as candidate passages, we create dark examples that all have moderate relevance to the query through mixing-up and masking in discrete space.
Neural Machine Translation with Contrastive Translation Memories
Retrieval-augmented Neural Machine Translation models have been successful in many translation scenarios.
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation
First, it is the largest multi-modal conversation dataset by the number of dialogues by 88x.
Ranked #2 on
Multimodal Intent Recognition
on MMDialog
Do Charge Prediction Models Learn Legal Theory?
The charge prediction task aims to predict the charge for a case given its fact description.
Towards Efficient Dialogue Pre-training with Transferable and Interpretable Latent Structure
With the availability of massive general-domain dialogue data, pre-trained dialogue generation appears to be super appealing to transfer knowledge from the general domain to downstream applications.
Collaborative Reasoning on Multi-Modal Semantic Graphs for Video-Grounded Dialogue Generation
We study video-grounded dialogue generation, where a response is generated based on the dialogue context and the associated video.
Counterfactual Recipe Generation: Exploring Compositional Generalization in a Realistic Scenario
Although pretrained language models can generate fluent recipe texts, they fail to truly learn and use the culinary knowledge in a compositional way.
Knowledge-enhanced Iterative Instruction Generation and Reasoning for Knowledge Base Question Answering
Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer entity in a knowledge base which is several hops from the topic entity mentioned in the question.
Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation
Contrastive learning has achieved impressive success in generation tasks to militate the "exposure bias" problem and discriminatively exploit the different quality of references.
GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Dense Passage Retrieval
To enhance the performance of dense retrieval models without loss of efficiency, we propose a GNN-encoder model in which query (passage) information is fused into passage (query) representations via graph neural networks that are constructed by queries and their top retrieved passages.
Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED
DocRED is a widely used dataset for document-level relation extraction.
Learning to Express in Knowledge-Grounded Conversation
Grounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses.
Entailment Graph Learning with Textual Entailment and Soft Transitivity
Typed entailment graphs try to learn the entailment relations between predicates from text and model them as edges between predicate nodes.
Things not Written in Text: Exploring Spatial Commonsense from Visual Signals
We probe PLMs and models with visual signals, including vision-language pretrained models and image synthesis models, on this benchmark, and find that image synthesis models are more capable of learning accurate and consistent spatial knowledge than other models.
HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modeling
Most of the CQA methods only incorporate articles or Wikipedia to extract knowledge and answer the user's question.
BNS: Building Network Structures Dynamically for Continual Learning
Continual learning (CL) of a sequence of tasks is often accompanied with the catastrophic forgetting(CF) problem.
Stylized Dialogue Generation with Multi-Pass Dual Learning
Considering there is no parallel data between the contexts and the responses of target style S1, existing works mainly use back translation to generate stylized synthetic data for training, where the data about context, target style S1 and an intermediate style S0 is used.
Gradient Imbalance and solution in Online Continual learning
Most existing techniques for online continual learning are based on experience-replay.
Extract, Integrate, Compete: Towards Verification Style Reading Comprehension
In this paper, we present a new verification style reading comprehension dataset named VGaokao from Chinese Language tests of Gaokao.
Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues
To tackle these challenges, we propose a representation[K]-interaction[L]-matching framework that explores multiple types of deep interactive representations to build context-response matching models for response selection.
Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation
Hence, in this paper, we propose a Relation-aware Related work Generator (RRG), which generates an abstractive related work from the given multiple scientific papers in the same research area.
Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction
Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences.
Ranked #48 on
Relation Extraction
on DocRED
Exploring Distantly-Labeled Rationales in Neural Network Models
Recent studies strive to incorporate various human rationales into neural networks to improve model performance, but few pay attention to the quality of the rationales.
Why Machine Reading Comprehension Models Learn Shortcuts?
A thorough empirical analysis shows that MRC models tend to learn shortcut questions earlier than challenging questions, and the high proportions of shortcut questions in training sets hinder models from exploring the sophisticated reasoning skills in the later stage of training.
Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study
Sequential information, a. k. a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders.
Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis
Causal inference is the process of capturing cause-effect relationship among variables.
Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models
Further analysis shows that Lattice-BERT can harness the lattice structures, and the improvement comes from the exploration of redundant information and multi-granularity representations.
Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots
To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN).
How does Truth Evolve into Fake News? An Empirical Study of Fake News Evolution
Automatically identifying fake news from the Internet is a challenging problem in deception detection tasks.
The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement
In this paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG) that generates headline which captures the attractive content following the attractive style.
Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension
Hence, in this paper, we propose to improve the response generation performance by examining the model's ability to answer a reading comprehension question, where the question is focused on the omitted information in the dialog.
Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling
Understanding neural models is a major topic of interest in the deep learning community.
Meaningful Answer Generation of E-Commerce Question-Answering
To generate more meaningful answers, in this paper, we propose a novel generative neural model, called the Meaningful Product Answer Generator (MPAG), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration.
Learning to Respond with Your Favorite Stickers: A Framework of Unifying Multi-Modality and User Preference in Multi-Turn Dialog
Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context and sticker using history of user.
Towards Context-Aware Code Comment Generation
Code comments are vital for software maintenance and comprehension, but many software projects suffer from the lack of meaningful and up-to-date comments in practice.
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models
We study knowledge-grounded dialogue generation with pre-trained language models.
VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles
Hence, in this paper, we propose the task of Video-based Multimodal Summarization with Multimodal Output (VMSMO) to tackle such a problem.
Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues
To address these issues, in this paper, we propose learning a context-response matching model with auxiliary self-supervised tasks designed for the dialogue data based on pre-trained language models.
Ranked #4 on
Conversational Response Selection
on E-commerce
Domain Adaptation for Semantic Parsing
In this paper, we propose a novel semantic parser for domain adaptation, where we have much fewer annotated data in the target domain compared to the source domain.
Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce
We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language.
Neighborhood Matching Network for Entity Alignment
This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge.
From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information
Text summarization is the research area aiming at creating a short and condensed version of the original document, which conveys the main idea of the document in a few words.
Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog
Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching text labels of stickers with previous utterances.
Low-Resource Knowledge-Grounded Dialogue Generation
In such a low-resource setting, we devise a disentangled response decoder in order to isolate parameters that depend on knowledge-grounded dialogues from the entire generation model.
Integrating Relation Constraints with Neural Relation Extractors
However, such models often make predictions for each entity pair individually, thus often fail to solve the inconsistency among different predictions, which can be characterized by discrete relation constraints.
Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce
Information-seeking conversation system aims at satisfying the information needs of users through conversations.
Transformation of Dense and Sparse Text Representations
The key idea of the proposed approach is to use a Forward Transformation to transform dense representations to sparse representations.
Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders
In this work, we improve the WAE for response generation.
Learning to Update Knowledge Graphs by Reading News
News streams contain rich up-to-date information which can be used to update knowledge graphs (KGs).
Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems
We study how to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems.
Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations
Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario.
Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation
Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information.
RPM-Oriented Query Rewriting Framework for E-commerce Keyword-Based Sponsored Search
Sponsored search optimizes revenue and relevance, which is estimated by Revenue Per Mille (RPM).
Multilingual Dialogue Generation with Shared-Private Memory
Existing dialog systems are all monolingual, where features shared among different languages are rarely explored.
Semi-supervised Text Style Transfer: Cross Projection in Latent Space
Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data.
Learning from Positive and Unlabeled Data with Adversarial Training
Positive-unlabeled (PU) learning learns a binary classifier using only positive and unlabeled examples without labeled negative examples.
Jointly Learning Entity and Relation Representations for Entity Alignment
Entity alignment is a viable means for integrating heterogeneous knowledge among different knowledge graphs (KGs).
Ranked #18 on
Entity Alignment
on DBP15k zh-en
(using extra training data)
How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing
There are two main challenges in this task: (1) the model needs to incorporate learned patterns from the prototype, but (2) should avoid copying contents other than the patternized words---such as irrelevant facts---into the generated summaries.
A Sketch-Based System for Semantic Parsing
The sketch is a high-level structure of the logical form exclusive of low-level details such as entities and predicates.
Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods.
Ranked #20 on
Entity Alignment
on DBP15k zh-en
(using extra training data)
Learning towards Abstractive Timeline Summarization
Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods. In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, we propose a memory-based timeline summarization model (MTS). Concretely, we propose a time-event memory to establish a timeline, and use the time position of events on this timeline to guide generation process. Besides, in each decoding step, we incorporate event-level information into word-level attention to avoid confusion between events. Extensive experiments are conducted on a large-scale real-world dataset, and the results show that MTS achieves the state-of-the-art performance in terms of both automatic and human evaluations.
Ranked #1 on
Timeline Summarization
on MTS
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues
Currently, researchers have paid great attention to retrieval-based dialogues in open-domain.
Ranked #12 on
Conversational Response Selection
on E-commerce
Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References
Due to its potential applications, open-domain dialogue generation has become popular and achieved remarkable progress in recent years, but sometimes suffers from generic responses.
A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots
We present a document-grounded matching network (DGMN) for response selection that can power a knowledge-aware retrieval-based chatbot system.
Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems
Under the framework, we simultaneously learn two matching models with independent training sets.
GSN: A Graph-Structured Network for Multi-Party Dialogues
Existing neural models for dialogue response generation assume that utterances are sequentially organized.
A Guider Network for Multi-Dual Learning
A large amount of parallel data is needed to train a strong neural machine translation (NMT) system.
Overcoming Catastrophic Forgetting via Model Adaptation
Several continual learning methods have been proposed to address the problem.
Lattice CNNs for Matching Based Chinese Question Answering
Short text matching often faces the challenges that there are great word mismatch and expression diversity between the two texts, which would be further aggravated in languages like Chinese where there is no natural space to segment words explicitly.
Product-Aware Answer Generation in E-Commerce Question-Answering
In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes.
Ranked #2 on
Question Answering
on JD Product Question Answer
Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT).
Abstractive Text Summarization by Incorporating Reader Comments
To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect.
Ranked #1 on
Reader-Aware Summarization
on RASG
Chat More If You Like: Dynamic Cue Words Planning to Flow Longer Conversations
To build an open-domain multi-turn conversation system is one of the most interesting and challenging tasks in Artificial Intelligence.
Plan-And-Write: Towards Better Automatic Storytelling
Automatic storytelling is challenging since it requires generating long, coherent natural language to describes a sensible sequence of events.
Encoding Implicit Relation Requirements for Relation Extraction: A Joint Inference Approach
Relation extraction is the task of identifying predefined relationship between entities, and plays an essential role in information extraction, knowledge base construction, question answering and so on.
Multi-grained Attention Network for Aspect-Level Sentiment Classification
We propose a fine-grained attention mechanism, which can capture the word-level interaction between aspect and context.
Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training
It is a challenging task to automatically compose poems with not only fluent expressions but also aesthetic wording.
Iterative Document Representation Learning Towards Summarization with Polishing
In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents.
Ranked #14 on
Extractive Text Summarization
on CNN / Daily Mail
Improving Matching Models with Hierarchical Contextualized Representations for Multi-turn Response Selection
In this paper, we study context-response matching with pre-trained contextualized representations for multi-turn response selection in retrieval-based chatbots.
Modeling discourse cohesion for discourse parsing via memory network
Identifying long-span dependencies between discourse units is crucial to improve discourse parsing performance.
Marrying up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding
The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data.
One "Ruler" for All Languages: Multi-Lingual Dialogue Evaluation with Adversarial Multi-Task Learning
Automatic evaluating the performance of Open-domain dialogue system is a challenging problem.
Topic-Based Question Generation
In the paper, we propose a new question generation problem, which also requires the input of a target topic in addition to a piece of descriptive text.
Tree2Tree Learning with Memory Unit
Traditional recurrent neural network (RNN) or convolutional neural net- work (CNN) based sequence-to-sequence model can not handle tree structural data well.
An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems.
Human-computer conversation systems have attracted much attention in Natural Language Processing.
Scale Up Event Extraction Learning via Automatic Training Data Generation
We show that this large volume of training data not only leads to a better event extractor, but also allows us to detect multiple typed events.
Style Transfer in Text: Exploration and Evaluation
Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with higher style transfer strength and similar content preservation score comparing to auto-encoder.
Ranked #5 on
Unsupervised Text Style Transfer
on Yelp
Diversifying Neural Conversation Model with Maximal Marginal Relevance
However, traditional seq2seq suffer from a severe weakness: during beam search decoding, they tend to rank universal replies at the top of the candidate list, resulting in the lack of diversity among candidate replies.
Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems
The study on human-computer conversation systems is a hot research topic nowadays.
Learning to Predict Charges for Criminal Cases with Legal Basis
The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description.
How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models
Generative conversational systems are attracting increasing attention in natural language processing (NLP).
Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix
We show that the dynamic transition matrix can effectively characterize the noise in the training data built by distant supervision.
A Constrained Sequence-to-Sequence Neural Model for Sentence Simplification
For word-level studies, words are simplified but also have potential grammar errors due to different usages of words before and after simplification.
RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems
Open-domain human-computer conversation has been attracting increasing attention over the past few years.
Hybrid Question Answering over Knowledge Base and Free Text
While these systems are able to provide more precise answers than information retrieval (IR) based QA systems, the natural incompleteness of KB inevitably limits the question scope that the system can answer.
Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems
In this paper, we propose a novel ensemble of retrieval-based and generation-based dialog systems in the open domain.
Question Answering on Freebase via Relation Extraction and Textual Evidence
Existing knowledge-based question answering systems often rely on small annotated training data.
Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling
Syntactic features play an essential role in identifying relationship in a sentence.
Ranked #3 on
Relation Classification
on SemEval 2010 Task 8
