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Found 24 papers, 14 papers with code
Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages
In this paper, we propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages.
DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis
In this work, we introduce a novel task of conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aiming to detect the sentiment quadruple of target-aspect-opinion-sentiment in a dialogue.
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.
Conversation Disentanglement with Bi-Level Contrastive Learning
Conversation disentanglement aims to group utterances into detached sessions, which is a fundamental task in processing multi-party conversations.
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.
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.
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.
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.
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.
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.
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.
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.
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.
