Search Results for author:
Found 57 papers, 22 papers with code
Modeling Unified Semantic Discourse Structure for High-quality Headline Generation
To consolidate the efficacy of S3 graphs, we further devise a hierarchical structure pruning mechanism to dynamically screen the redundant and nonessential nodes within the graph.
CMNER: A Chinese Multimodal NER Dataset based on Social Media
Multimodal Named Entity Recognition (MNER) is a pivotal task designed to extract named entities from text with the support of pertinent images.
Reverse Multi-Choice Dialogue Commonsense Inference with Graph-of-Thought
With the proliferation of dialogic data across the Internet, the Dialogue Commonsense Multi-choice Question Answering (DC-MCQ) task has emerged as a response to the challenge of comprehending user queries and intentions.
Compositional Generalization for Multi-label Text Classification: A Data-Augmentation Approach
Our experiments show that this data augmentation approach significantly improves the compositional generalization capabilities of classification models on our benchmarks, with both generation models surpassing other text generation baselines.
A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition
The joint task of Dialog Sentiment Classification (DSC) and Act Recognition (DAR) aims to predict the sentiment label and act label for each utterance in a dialog simultaneously.
Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition
On the other hand, during the feature fusion stage, we propose a Contribution-aware Fusion Mechanism (CFM) and a Context Refusion Mechanism (CRM) for multimodal and context integration, respectively.
Ranked #5 on
Emotion Recognition in Conversation
on IEMOCAP
DialogRE^C+: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs
Dialogue relation extraction (DRE) that identifies the relations between argument pairs in dialogue text, suffers much from the frequent occurrence of personal pronouns, or entity and speaker coreference.
ECQED: Emotion-Cause Quadruple Extraction in Dialogs
The existing emotion-cause pair extraction (ECPE) task, unfortunately, ignores extracting the emotion type and cause type, while these fine-grained meta-information can be practically useful in real-world applications, i. e., chat robots and empathic dialog generation.
TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition
Few-shot named entity recognition (NER) exploits limited annotated instances to identify named mentions.
Revisiting Conversation Discourse for Dialogue Disentanglement
Conversation utterances are essentially organized and described by the underlying discourse, and thus dialogue disentanglement requires the full understanding and harnessing of the intrinsic discourse attribute.
FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing
Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval.
Ranked #2 on
Human Judgment Correlation
on Flickr8k-Expert
On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training
In this study, we propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis
The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
TOE: A Grid-Tagging Discontinuous NER Model Enhanced by Embedding Tag/Word Relations and More Fine-Grained Tags
Concretely, we construct tag representations and embed them into TREM, so that TREM can treat tag and word representations as queries/keys/values and utilize self-attention to model their relationships.
Entity-centered Cross-document Relation Extraction
First, we focus on input construction for our RE model and propose an entity-based document-context filter to retain useful information in the given documents by using the bridge entities in the text paths.
Conversational Semantic Role Labeling with Predicate-Oriented Latent Graph
In this work, we investigate the integration of a latent graph for CSRL.
Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction
We first propose a feature-task alignment to explicitly model the specific emotion-&cause-specific features and the shared interactive feature.
OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction
Event extraction (EE) is an essential task of information extraction, which aims to extract structured event information from unstructured text.
Global inference with explicit syntactic and discourse structures for dialogue-level relation extraction
In our global reasoning framework, D2G and ARG work collaboratively, iteratively performing lexical, syntactic and semantic information exchange and representation learning over the entire dialogue context.
Ranked #3 on
Dialog Relation Extraction
on DialogRE
Inheriting the Wisdom of Predecessors: A Multiplex Cascade Framework for Unified Aspect-based Sentiment Analysis
So far, aspect-based sentiment analysis (ABSA) has involved with total seven subtasks, in which, however the interactions among them have been left unexplored sufficiently.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis
The essential label set consists of the basic labels for this task, which are relatively balanced and applied in the prediction layer.
Unified Named Entity Recognition as Word-Word Relation Classification
So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka.
Ranked #2 on
Chinese Named Entity Recognition
on OntoNotes 4
Nonautoregressive Encoder-Decoder Neural Framework for End-to-End Aspect-Based Sentiment Triplet Extraction
Aspect-based sentiment triplet extraction (ASTE) aims at recognizing the joint triplets from texts, i. e., aspect terms, opinion expressions, and correlated sentiment polarities.
Ranked #3 on
Aspect Sentiment Triplet Extraction
on ASTE-Data-V2
Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role Labeling
Unified opinion role labeling (ORL) aims to detect all possible opinion structures of 'opinion-holder-target' in one shot, given a text.
Ranked #1 on
Fine-Grained Opinion Analysis
on MPQA
(F1 (Opinion) metric)
A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition
Second, we perform relation classification to judge whether a given pair of entity fragments to be overlapping or succession.
Rethinking Boundaries: End-To-End Recognition of Discontinuous Mentions with Pointer Networks
A majority of research interests in irregular (eg, nested or discontinuous) named entity recognition (NER) have been paid on nested entities, while discontinuous entities received limited attention.
Encoder-decoder based unified semantic role labeling with label-aware syntax
Currently the unified semantic role labeling (SRL) that achieves predicate identification and argument role labeling in an end-to-end manner has received growing interests.
Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extractionwith Rich Syntactic Knowledge
In this paper, we propose to enhance the pair-wise aspect and opinion terms extraction (PAOTE) task by incorporating rich syntactic knowledge.
End-to-end Semantic Role Labeling with Neural Transition-based Model
It performs the two subtasks of SRL: predicate identification and argument role labeling, jointly.
Ranked #1 on
Semantic Role Labeling
on CoNLL-2009
Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions
In this paper, we target improving the joint dialogue act recognition (DAR) and sentiment classification (SC) tasks by fully modeling the local contexts of utterances.
HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations
Emotion detection in conversations (EDC) is to detect the emotion for each utterance in conversations that have multiple speakers.
Ranked #19 on
Emotion Recognition in Conversation
on EmoryNLP
Improving Text Understanding via Deep Syntax-Semantics Communication
Recent studies show that integrating syntactic tree models with sequential semantic models can bring improved task performance, while these methods mostly employ shallow integration of syntax and semantics.
Aggressive Language Detection with Joint Text Normalization via Adversarial Multi-task Learning
Aggressive language detection (ALD), detecting the abusive and offensive language in texts, is one of the crucial applications in NLP community.
Nominal Compound Chain Extraction: A New Task for Semantic-enriched Lexical Chain
Lexical chain consists of cohesion words in a document, which implies the underlying structure of a text, and thus facilitates downstream NLP tasks.
Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic NLP
Syntax has been shown useful for various NLP tasks, while existing work mostly encodes singleton syntactic tree using one hierarchical neural network.
Retrofitting Structure-aware Transformer Language Model for End Tasks
We consider retrofitting structure-aware Transformer-based language model for facilitating end tasks by proposing to exploit syntactic distance to encode both the phrasal constituency and dependency connection into the language model.
High-order Refining for End-to-end Chinese Semantic Role Labeling
Current end-to-end semantic role labeling is mostly accomplished via graph-based neural models.
Cross-lingual Semantic Role Labeling with Model Transfer
In this paper, we fill the gap of cross-lingual SRL by proposing an end-to-end SRL model that incorporates a variety of universal features and transfer methods.
Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification
The idea is to allow the dependency graph to guide the representation learning of the transformer encoder and vice versa.
AMR Parsing with Latent Structural Information
Abstract Meaning Representations (AMRs) capture sentence-level semantics structural representations to broad-coverage natural sentences.
Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus
Many efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding.
End to End Chinese Lexical Fusion Recognition with Sememe Knowledge
In this paper, we present Chinese lexical fusion recognition, a new task which could be regarded as one kind of coreference recognition.
Multi-Task Learning with Auxiliary Speaker Identification for Conversational Emotion Recognition
Conversational emotion recognition (CER) has attracted increasing interests in the natural language processing (NLP) community.
Ranked #22 on
Emotion Recognition in Conversation
on EmoryNLP
Bacteria Biotope Relation Extraction via Lexical Chains and Dependency Graphs
abstract In this article, we describe our approach for the Bacteria Biotopes relation extraction (BB-rel) subtask in the BioNLP Shared Task 2019.
DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets
Recognizing and linking such fine-grained location mentions to well-defined location profiles are beneficial for retrieval and recommendation systems.
Recognizing irregular entities in biomedical text via deep neural networks
We evaluate our models on two datasets for recognizing regular and irreg- ular biomedical entities.
A Bi-LSTM-RNN Model for Relation Classification Using Low-Cost Sequence Features
This model divides a sentence or text segment into five parts, namely two target entities and their three contexts.
Multi-prototype Chinese Character Embedding
Chinese sentences are written as sequences of characters, which are elementary units of syntax and semantics.
Distance Metric Learning for Aspect Phrase Grouping
Aspect phrase grouping is an important task in aspect-level sentiment analysis.
