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Found 175 papers, 74 papers with code
面向话题的讽刺识别:新任务、新数据和新方法(Topic-Oriented Sarcasm Detection: New Task, New Dataset and New Method)
“现有的文本讽刺识别研究通常只停留在句子级别的讽刺表达分类, 缺乏考虑讽刺对象对讽刺表达的影响。针对这一问题, 本文提出一个新的面向话题的讽刺识别任务。该任务通过话题的引入, 以话题作为讽刺对象, 有助于更好地理解和建模讽刺表达。对应地, 本文构建了一个新的面向话题的讽刺识别数据集。这个数据集包含了707个话题, 以及对应的4871个话题-评论对组。在此基础上, 基于提示学习和大规模预训练语言模型, 提出了一种面向话题的讽刺表达提示学习模型。在本文构建的面向话题讽刺识别数据集上的实验结果表明, 相比基线模型, 本文所提出的面向话题的讽刺表达提示学习模型取得了更优的性能。同时, 实验分析也表明本文提出的面向话题的讽刺识别任务相比传统的句子级讽刺识别任务更具挑战性。”
MSAMSum: Towards Benchmarking Multi-lingual Dialogue Summarization
Dialogue summarization helps users capture salient information from various types of dialogues has received much attention recently.
Improving Controllable Text Generation with Position-Aware Weighted Decoding
Weighted decoding methods composed of the pretrained language model (LM) and the controller have achieved promising results for controllable text generation.
Retrieve, Discriminate and Rewrite: A Simple and Effective Framework for Obtaining Affective Response in Retrieval-Based Chatbots
To address this problem, we propose a simple and effective Retrieve-Discriminate-Rewrite framework.
MuCDN: Mutual Conversational Detachment Network for Emotion Recognition in Multi-Party Conversations
Specifically, two detachment ways are devised to perform context and speaker-specific modeling within detached threads and they are bridged through a mutual module.
SSR: Utilizing Simplified Stance Reasoning Process for Robust Stance Detection
Motivated by how humans tackle stance detection tasks, we propose to incorporate the stance reasoning process as task knowledge to assist in learning genuine features and reducing reliance on bias features.
CogBERT: Cognition-Guided Pre-trained Language Models
To fill the gap, we propose CogBERT, a framework that can induce fine-grained cognitive features from cognitive data and incorporate cognitive features into BERT by adaptively adjusting the weight of cognitive features for different NLP tasks.
Neural Natural Logic Inference for Interpretable Question Answering
Many open-domain question answering problems can be cast as a textual entailment task, where a question and candidate answers are concatenated to form hypotheses.
Less Is More: Domain Adaptation with Lottery Ticket for Reading Comprehension
Then, we only fine-tune the lottery subnetwork, a small fraction of the whole parameters, on the annotated target domain data for adaptation.
Causal-Guided Active Learning for Debiasing Large Language Models
Although achieving promising performance, recent analyses show that current generative large language models (LLMs) may still capture dataset biases and utilize them for generation, leading to poor generalizability and harmfulness of LLMs.
Diagnosing and Remedying Knowledge Deficiencies in LLMs via Label-free Curricular Meaningful Learning
This makes it a challenge to diagnose and remedy the deficiencies of LLMs through rich label-free user queries.
Learning Fine-Grained Grounded Citations for Attributed Large Language Models
Despite the impressive performance on information-seeking tasks, large language models (LLMs) still struggle with hallucinations.
Enhancing Complex Causality Extraction via Improved Subtask Interaction and Knowledge Fusion
However, existing fine-tuning based ECE methods cannot address all three key challenges in ECE simultaneously: 1) Complex Causality Extraction, where multiple causal-effect pairs occur within a single sentence; 2) Subtask~ Interaction, which involves modeling the mutual dependence between the two subtasks of ECE, i. e., extracting events and identifying the causal relationship between extracted events; and 3) Knowledge Fusion, which requires effectively fusing the knowledge in two modalities, i. e., the expressive pretrained language models and the structured knowledge graphs.
Self-Evolving GPT: A Lifelong Autonomous Experiential Learner
To improve the performance of large language models (LLMs), researchers have explored providing LLMs with textual task-solving experience via prompts.
GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models
Evaluating the graph comprehension and reasoning abilities of Large Language Models (LLMs) is challenging and often incomplete.
Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models
To mitigate this, we further propose a training-free method called Residual Visual Decoding, where we revise the output distribution of LVLMs with the one derived from the residual visual input, providing models with direct access to the visual information.
BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering
For composite questions, the LLM combines beam candidates, explores multiple reasoning paths through probabilistic aggregation, and prioritizes the most promising trajectory.
GUIDE: A Guideline-Guided Dataset for Instructional Video Comprehension
Our proposed benchmark consists of three sub-tasks to evaluate comprehension ability of models: (1) Step Captioning: models have to generate captions for specific steps from videos.
MolFusion: Multimodal Fusion Learning for Molecular Representations via Multi-granularity Views
Artificial Intelligence predicts drug properties by encoding drug molecules, aiding in the rapid screening of candidates.
Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis
Large language model unlearning has garnered increasing attention due to its potential to address security and privacy concerns, leading to extensive research in the field.
Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey
Compared to traditional sentiment analysis, which only considers text, multimodal sentiment analysis needs to consider emotional signals from multimodal sources simultaneously and is therefore more consistent with the way how humans process sentiment in real-world scenarios.
Planning Like Human: A Dual-process Framework for Dialogue Planning
In proactive dialogue, the challenge lies not just in generating responses but in steering conversations toward predetermined goals, a task where Large Language Models (LLMs) typically struggle due to their reactive nature.
Improving In-Context Learning with Prediction Feedback for Sentiment Analysis
Large language models (LLMs) have achieved promising results in sentiment analysis through the in-context learning (ICL) paradigm.
RKLD: Reverse KL-Divergence-based Knowledge Distillation for Unlearning Personal Information in Large Language Models
With the passage of the Right to Be Forgotten (RTBF) regulations and the scaling up of language model training datasets, research on model unlearning in large language models (LLMs) has become more crucial.
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation
Retrieval-augmented generation integrates the capabilities of large language models with relevant information retrieved from an extensive corpus, yet encounters challenges when confronted with real-world noisy data.
Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation
To this end, we propose FunCoder, a code generation framework incorporating the divide-and-conquer strategy with functional consensus.
MoGU: A Framework for Enhancing Safety of Open-Sourced LLMs While Preserving Their Usability
When encountering malicious instructions, the router will assign a higher weight to the safe LLM to ensure that responses are harmless.
Towards Comprehensive and Efficient Post Safety Alignment of Large Language Models via Safety Patching
Safety alignment of large language models (LLMs) has been gaining increasing attention.
Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration
To address this challenge, DeePEn maps the probability distribution of each model from its own probability space to a universal relative space based on the relative representation theory, and performs aggregation.
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict
Furthermore, we construct RU22Fact, a novel multilingual explainable fact-checking dataset on the Russia-Ukraine conflict in 2022 of 16K samples, each containing real-world claims, optimized evidence, and referenced explanation.
Meaningful Learning: Advancing Abstract Reasoning in Large Language Models via Generic Fact Guidance
Large language models (LLMs) have developed impressive performance and strong explainability across various reasoning scenarios, marking a significant stride towards mimicking human-like intelligence.
Learning to Describe for Predicting Zero-shot Drug-Drug Interactions
Adverse drug-drug interactions~(DDIs) can compromise the effectiveness of concurrent drug administration, posing a significant challenge in healthcare.
How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider Transformer Models
In this work, we attempt to explain and reverse the decline in base capabilities caused by the architecture of FFN-Wider Transformers, seeking to provide some insights.
Vanilla Transformers are Transfer Capability Teachers
However, studies have indicated that MoE Transformers underperform vanilla Transformers in many downstream tasks, significantly diminishing the practical value of MoE models.
AS-ES Learning: Towards Efficient CoT Learning in Small Models
Chain-of-Thought (CoT) serves as a critical emerging ability in LLMs, especially when it comes to logical reasoning.
Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning
Through pretraining on a corpus with various sources, Large Language Models (LLMs) have gained impressive performance.
Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence
Emotional Intelligence (EI), consisting of emotion perception, emotion cognition and emotion expression, plays the critical roles in improving user interaction experience for the current large language model (LLM) based conversational general AI assistants.
Beyond the Answers: Reviewing the Rationality of Multiple Choice Question Answering for the Evaluation of Large Language Models
In the field of natural language processing (NLP), Large Language Models (LLMs) have precipitated a paradigm shift, markedly enhancing performance in natural language generation tasks.
Multiple-choice
Multiple Choice Question Answering (MCQA)
+1
Beyond Direct Diagnosis: LLM-based Multi-Specialist Agent Consultation for Automatic Diagnosis
Automatic diagnosis is a significant application of AI in healthcare, where diagnoses are generated based on the symptom description of patients.
MolTailor: Tailoring Chemical Molecular Representation to Specific Tasks via Text Prompts
The agent accentuates task-relevant features in the molecular representation by understanding the natural language description of the task, just as a tailor customizes clothes for clients.
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models
Existing methods devise the learning module to acquire task-specific knowledge with parameter-efficient tuning (PET) block and the selection module to pick out the corresponding one for the testing input, aiming at handling the challenges of catastrophic forgetting and knowledge transfer in CL.
Aligning Translation-Specific Understanding to General Understanding in Large Language Models
To align the translation-specific understanding to the general one, we propose a novel translation process xIoD (Cross-Lingual Interpretation of Difficult words), explicitly incorporating the general understanding on the content incurring inconsistent understanding to guide the translation.
CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language Models
Cognitive dynamics are pivotal to advance human understanding of the world.
Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding
In this survey, we present these advances towards length extrapolation in a unified notation from the perspective of PE.
Emage: Non-Autoregressive Text-to-Image Generation
Compared with autoregressive baselines that needs to run one thousand times, our model only runs 16 times to generate images of competitive quality with an order of magnitude lower inference latency.
KwaiAgents: Generalized Information-seeking Agent System with Large Language Models
Driven by curiosity, humans have continually sought to explore and understand the world around them, leading to the invention of various tools to satiate this inquisitiveness.
Analyzing the Inherent Response Tendency of LLMs: Real-World Instructions-Driven Jailbreak
The jailbreak idea of our method is "Inherent Response Tendency Analysis" which identifies real-world instructions that can inherently induce LLMs to generate affirmation responses and the corresponding jailbreak strategy is "Real-World Instructions-Driven Jailbreak" which involves strategically splicing real-world instructions identified through the above analysis around the malicious instruction.
TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models
Grasping the concept of time is a fundamental facet of human cognition, indispensable for truly comprehending the intricacies of the world.
Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications
In this paper, we propose a review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications.
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), leading to remarkable advancements in text understanding and generation.
MTGER: Multi-view Temporal Graph Enhanced Temporal Reasoning over Time-Involved Document
To address this issue, we propose MTGER, a novel Multi-view Temporal Graph Enhanced Temporal Reasoning framework for temporal reasoning over time-involved documents.
An Early Evaluation of GPT-4V(ision)
In this paper, we evaluate different abilities of GPT-4V including visual understanding, language understanding, visual puzzle solving, and understanding of other modalities such as depth, thermal, video, and audio.
Make Your Decision Convincing! A Unified Two-Stage Framework: Self-Attribution and Decision-Making
Through extensive experiments on five reasoning datasets from the ERASER benchmark, we demonstrate that our framework not only establishes a more reliable link between the generated rationale and model decision but also achieves competitive results in task performance and the quality of rationale.
Retrieval-Generation Synergy Augmented Large Language Models
Large language models augmented with task-relevant documents have demonstrated impressive performance on knowledge-intensive tasks.
Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future
We hope this paper serves as an introduction for beginners and fosters future research.
From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule Discovery
Furthermore, our method shows a sustained improvement as the volume of pseudo data increases, revealing the great potential of pseudo data in advancing low-resource cross-modal molecule discovery.
Manifold-based Verbalizer Space Re-embedding for Tuning-free Prompt-based Classification
Experimental results indicate that even without tuning any parameters, our LLE-INC is on par with automated verbalizers with parameter tuning.
Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Reliable Response Generation in Chinese
To address this challenge, we propose knowledge-tuning, which leverages structured medical knowledge bases for the LLMs to grasp domain knowledge efficiently and facilitate reliable response generation.
Don't Ignore Dual Logic Ability of LLMs while Privatizing: A Data-Intensive Analysis in Medical Domain
We conduct several experiments to analyze the dual logic ability of LLMs by examining the consistency of the stance in responses to paired questions about the same fact.
GLS-CSC: A Simple but Effective Strategy to Mitigate Chinese STM Models' Over-Reliance on Superficial Clue
To address this issue, it is crucial to analyze and mitigate the influence of superficial clues on STM models.
Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarization
However, existing work has focused on training models on centralized data, neglecting real-world scenarios where meeting data are infeasible to collect centrally, due to their sensitive nature.
UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human language
In this paper, we propose fMRI2text, the first openvocabulary task aiming to bridge fMRI time series and human language.
Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completion
The proposed approach comprises two main components: a GNN-based predictor and a reasoning path distiller.
SkillNet-X: A Multilingual Multitask Model with Sparsely Activated Skills
Traditional multitask learning methods basically can only exploit common knowledge in task- or language-wise, which lose either cross-language or cross-task knowledge.
A Diffusion Model for Event Skeleton Generation
Event skeleton generation, aiming to induce an event schema skeleton graph with abstracted event nodes and their temporal relations from a set of event instance graphs, is a critical step in the temporal complex event schema induction task.
Improved Visual Story Generation with Adaptive Context Modeling
Diffusion models developed on top of powerful text-to-image generation models like Stable Diffusion achieve remarkable success in visual story generation.
Towards Higher Pareto Frontier in Multilingual Machine Translation
Multilingual neural machine translation has witnessed remarkable progress in recent years.
SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models
Despite achieving remarkable performance on various vision-language tasks, Transformer-based Vision-Language Models (VLMs) suffer from redundancy in inputs and parameters, significantly hampering their efficiency in real-world applications.
UNIMO-3: Multi-granularity Interaction for Vision-Language Representation Learning
The experimental results show that our model achieves state-of-the-art performance in various downstream tasks, and through ablation study can prove that effective cross-layer learning improves the model's ability of multimodal representation.
Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate
Through extensive experiments on various datasets, LLMs can effectively collaborate to reach a consensus despite noticeable inter-inconsistencies, but imbalances in their abilities can lead to domination by superior LLMs.
NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing
To mitigate the effects of label noise, learning with noisy labels (LNL) methods are designed to achieve better generalization performance.
Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluation
Causal reasoning ability is crucial for numerous NLP applications.
Improving Cross-Task Generalization with Step-by-Step Instructions
Instruction tuning has been shown to be able to improve cross-task generalization of language models.
TransESC: Smoothing Emotional Support Conversation via Turn-Level State Transition
Specifically, we construct the state transition graph with a two-step way, named transit-then-interact, to grasp such three types of turn-level transition information.
The Role of Summarization in Generative Agents: A Preliminary Perspective
Generative agents that simulate human society show tremendous potential for further research and practical applications.
Is ChatGPT Equipped with Emotional Dialogue Capabilities?
This report presents a study on the emotional dialogue capability of ChatGPT, an advanced language model developed by OpenAI.
HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge
Large Language Models (LLMs), such as the LLaMA model, have demonstrated their effectiveness in various general-domain natural language processing (NLP) tasks.
Global Prompt Cell: A Portable Control Module for Effective Prompt Tuning
As a novel approach to tuning pre-trained models, prompt tuning involves freezing the parameters in downstream tasks while inserting trainable embeddings into inputs in the first layer.
Hierarchical Catalogue Generation for Literature Review: A Benchmark
Scientific literature review generation aims to extract and organize important information from an abundant collection of reference papers and produces corresponding reviews while lacking a clear and logical hierarchy.
STOA-VLP: Spatial-Temporal Modeling of Object and Action for Video-Language Pre-training
Although large-scale video-language pre-training models, which usually build a global alignment between the video and the text, have achieved remarkable progress on various downstream tasks, the idea of adopting fine-grained information during the pre-training stage is not well explored.
Semantic-aware Contrastive Learning for Electroencephalography-to-Text Generation with Curriculum Learning
To mitigate this challenge, we devise a Curriculum Semantic-aware Contrastive Learning strategy (C-SCL), which effectively re-calibrates the subject-dependent EEG representation to the semantic-dependent EEG representation, thus reducing the discrepancy.
Debiasing Stance Detection Models with Counterfactual Reasoning and Adversarial Bias Learning
Stance detection models may tend to rely on dataset bias in the text part as a shortcut and thus fail to sufficiently learn the interaction between the targets and texts.
Controllable Text Generation via Probability Density Estimation in the Latent Space
Previous work on controllable text generation has explored the idea of control from the latent space, such as optimizing a representation with attribute-related classifiers or sampling a representation from relevant discrete samples.
ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks
Causal chain reasoning (CCR) is an essential ability for many decision-making AI systems, which requires the model to build reliable causal chains by connecting causal pairs.
Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment
Moreover, social-interaction CSK serves as emotion-level bridge (E-bridge) and action-level bridge (A-bridge) to connect candidate utterances with the target one, which provides explicit causal clues for the Emotional Interaction module and Actional Interaction module to reason the target emotion.
Ranked #4 on
Causal Emotion Entailment
on RECCON
BigCilin: An Automatic Chinese Open-domain Knowledge Graph with Fine-grained Hypernym-Hyponym Relations
One is that it supports querying any Chinese named entity and browsing the extracted hypernym-hyponym paths surro-unding the query entity.
Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia
In this paper, we propose Kuaipedia, a large-scale multi-modal encyclopedia consisting of items, aspects, and short videos lined to them, which was extracted from billions of videos of Kuaishou (Kwai), a well-known short-video platform in China.
Don't Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness
As a critical step to achieve human-like chatbots, empathetic response generation has attained increasing interests.
An Efficient End-to-End Transformer with Progressive Tri-modal Attention for Multi-modal Emotion Recognition
At the low-level, we propose the progressive tri-modal attention, which can model the tri-modal feature interactions by adopting a two-pass strategy and can further leverage such interactions to significantly reduce the computation and memory complexity through reducing the input token length.
Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words
Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain.
Zero-shot Aspect-level Sentiment Classification via Explicit Utilization of Aspect-to-Document Sentiment Composition
Our key intuition is that the sentiment representation of a document is composed of the sentiment representations of all the aspects of that document.
DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination
To relieve this issue, curriculum learning is proposed to improve model performance and generalization by ordering training samples in a meaningful (e. g., easy to hard) sequence.
Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark
We then establish a strong baseline that scores a R@1 of 46. 2% on Few-Shot and 76. 6% on Zero-Shot on our dataset.
VEM$^2$L: A Plug-and-play Framework for Fusing Text and Structure Knowledge on Sparse Knowledge Graph Completion
In this paper, we solve the sparse KGC from these two motivations simultaneously and handle their respective drawbacks further, and propose a plug-and-play unified framework VEM$^2$L over sparse KGs.
MACSA: A Multimodal Aspect-Category Sentiment Analysis Dataset with Multimodal Fine-grained Aligned Annotations
In this paper, we propose a new dataset, the Multimodal Aspect-Category Sentiment Analysis (MACSA) dataset, which contains more than 21K text-image pairs.
Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label Correlation
In this study, we focus on the spurious correlation between word features and labels that models learn from the biased data distribution of training data.
A Graph Enhanced BERT Model for Event Prediction
To this end, we incorporate an additional structured variable into BERT to learn to predict the event connections in the training process.
e-CARE: a New Dataset for Exploring Explainable Causal Reasoning
Understanding causality has vital importance for various Natural Language Processing (NLP) applications.
Unifying the Convergences in Multilingual Neural Machine Translation
In this paper, we propose a novel training strategy named LSSD (Language-Specific Self-Distillation), which can alleviate the convergence inconsistency and help MNMT models achieve the best performance on each language pair simultaneously.
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors
Through further analysis of the ASR outputs, we find that in some cases the sentiment words, the key sentiment elements in the textual modality, are recognized as other words, which makes the sentiment of the text change and hurts the performance of multimodal sentiment models directly.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Pretraining without Wordpieces: Learning Over a Vocabulary of Millions of Words
(2) how to predict a word via cloze test without knowing the number of wordpieces in advance?
Distilled Dual-Encoder Model for Vision-Language Understanding
We propose a cross-modal attention distillation framework to train a dual-encoder model for vision-language understanding tasks, such as visual reasoning and visual question answering.
GEDIT: Geographic-Enhanced and Dependency-Guided Tagging for Joint POI and Accessibility Extraction at Baidu Maps
We approach this task as a sequence tagging problem, where the goal is to produce <POI name, accessibility label> pairs from unstructured text.
ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning
ExCAR first acquires additional evidence information from a large-scale causal event graph as logical rules for causal reasoning.
Learning Event Graph Knowledge for Abductive Reasoning
Abductive reasoning aims at inferring the most plausible explanation for observed events, which would play critical roles in various NLP applications, such as reading comprehension and question answering.
CausalBERT: Injecting Causal Knowledge Into Pre-trained Models with Minimal Supervision
Recent work has shown success in incorporating pre-trained models like BERT to improve NLP systems.
A Survey on Dialogue Summarization: Recent Advances and New Frontiers
We hope that this first survey of dialogue summarization can provide the community with a quick access and a general picture to this task and motivate future researches.
Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization
Current dialogue summarization systems usually encode the text with a number of general semantic features (e. g., keywords and topics) to gain more powerful dialogue modeling capabilities.
The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey
Recently, various neural encoder-decoder models pioneered by Seq2Seq framework have been proposed to achieve the goal of generating more abstractive summaries by learning to map input text to output text.
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension
Multiparty Dialogue Machine Reading Comprehension (MRC) differs from traditional MRC as models must handle the complex dialogue discourse structure, previously unconsidered in traditional MRC.
Ranked #4 on
Question Answering
on Molweni
Learning to Share by Masking the Non-shared for Multi-domain Sentiment Classification
To this end, we propose the BertMasker network which explicitly masks domain-related words from texts, learns domain-invariant sentiment features from these domain-agnostic texts, and uses those masked words to form domain-aware sentence representations.
General Classification
Multi-Domain Sentiment Classification
+3
Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization
First, we present a Dialogue Discourse-Dware Meeting Summarizer (DDAMS) to explicitly model the interaction between utterances in a meeting by modeling different discourse relations.
Biomedical Knowledge Graph Refinement with Embedding and Logic Rules
This paper proposes a method BioGRER to improve the BioKG's quality, which comprehensively combines the knowledge graph embedding and logic rules that support and negate triplets in the BioKG.
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations
We noticed that the gold emotion labels of the context utterances can provide explicit and accurate emotion interaction, but it is impossible to input gold labels at inference time.
Ranked #44 on
Emotion Recognition in Conversation
on IEMOCAP
HIT-SCIR at SemEval-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection
In this task, we dedicate to detecting causation, especially counterfactuals from texts.
TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching
Although neural table-to-text models have achieved remarkable progress with the help of large-scale datasets, they suffer insufficient learning problem with limited training data.
Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification
Neural table-to-text models, which select and order salient data, as well as verbalizing them fluently via surface realization, have achieved promising progress.
Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks
In detail, we consider utterance and commonsense knowledge as two different types of data and design a Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information.
Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network
Noun phrases and relational phrases in Open Knowledge Bases are often not canonical, leading to redundant and ambiguous facts.
How Does Selective Mechanism Improve Self-Attention Networks?
Self-attention networks (SANs) with selective mechanism has produced substantial improvements in various NLP tasks by concentrating on a subset of input words.
Revisiting Pre-Trained Models for Chinese Natural Language Processing
Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models.
Ranked #13 on
Stock Market Prediction
on Astock
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure
Research into the area of multiparty dialog has grown considerably over recent years.
Ranked #7 on
Discourse Parsing
on Molweni
Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation
In this paper, we focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer and aims to preserve text styles while altering the content.
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation tasks.
Ranked #1 on
Code Documentation Generation
on CodeSearchNet - Go
An Annotation Scheme of A Large-scale Multi-party Dialogues Dataset for Discourse Parsing and Machine Comprehension
In this paper, we propose the scheme for annotating large-scale multi-party chat dialogues for discourse parsing and machine comprehension.
Transforming Wikipedia into Augmented Data for Query-Focused Summarization
The limited size of existing query-focused summarization datasets renders training data-driven summarization models challenging.
Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text
In this work, we propose a new sequence labeling framework (as well as a new tag schema) to jointly extract the fact and condition tuples from statement sentences.
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge
Recent neural models for data-to-text generation rely on massive parallel pairs of data and text to learn the writing knowledge.
Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning
Neural semantic parsing has achieved impressive results in recent years, yet its success relies on the availability of large amounts of supervised data.
Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)
To address aforementioned problems, not only do we model each table cell considering other records in the same row, we also enrich table's representation by modeling each table cell in context of other cells in the same column or with historical (time dimension) data respectively.
Cross-Lingual Machine Reading Comprehension
In this paper, we propose Cross-Lingual Machine Reading Comprehension (CLMRC) task for the languages other than English.
Constructing Information-Lossless Biological Knowledge Graphs from Conditional Statements
Conditions are essential in the statements of biological literature.
Pre-Training with Whole Word Masking for Chinese BERT
To demonstrate the effectiveness of these models, we create a series of Chinese pre-trained language models as our baselines, including BERT, RoBERTa, ELECTRA, RBT, etc.
Learning to Ask Unanswerable Questions for Machine Reading Comprehension
We also present a way to construct training data for our question generation models by leveraging the existing reading comprehension dataset.
Attribute Acquisition in Ontology based on Representation Learning of Hierarchical Classes and Attributes
This paper investigates an attention-based automatic paradigm called TransATT for attribute acquisition, by learning the representation of hierarchical classes and attributes in Chinese ontology.
Towards Time-Aware Distant Supervision for Relation Extraction
Time-DS is composed of a time series instance-popularity and two strategies.
Learning to Refine Source Representations for Neural Machine Translation
Neural machine translation (NMT) models generally adopt an encoder-decoder architecture for modeling the entire translation process.
An AMR Aligner Tuned by Transition-based Parser
In this paper, we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser.
Ranked #2 on
AMR Parsing
on LDC2014T12:
Adaptive Multi-pass Decoder for Neural Machine Translation
Although end-to-end neural machine translation (NMT) has achieved remarkable progress in the recent years, the idea of adopting multi-pass decoding mechanism into conventional NMT is not well explored.
Knowledge Based Machine Reading Comprehension
Machine reading comprehension (MRC) requires reasoning about both the knowledge involved in a document and knowledge about the world.
Knowledge-Aware Conversational Semantic Parsing Over Web Tables
Results show that our knowledge-aware model outperforms the state-of-the-art approaches.
Distilling Knowledge for Search-based Structured Prediction
Many natural language processing tasks can be modeled into structured prediction and solved as a search problem.
Semantic Parsing with Syntax- and Table-Aware SQL Generation
We present a generative model to map natural language questions into SQL queries.
Ranked #4 on
Code Generation
on WikiSQL
Parsing Tweets into Universal Dependencies
Nonetheless, using the new treebank, we build a pipeline system to parse raw tweets into UD.
Ranked #2 on
Dependency Parsing
on Tweebank
English-Chinese Knowledge Base Translation with Neural Network
Knowledge base (KB) such as Freebase plays an important role for many natural language processing tasks.
Truth Discovery with Memory Network
We use feedforward memory network and feedback memory network to learn the representation of the credibility of statements which are about the same object.
Aspect Level Sentiment Classification with Deep Memory Network
Such importance degree and text representation are calculated with multiple computational layers, each of which is a neural attention model over an external memory.
Aspect-Based Sentiment Analysis (ABSA)
General Classification
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