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Claim this profileWorks153 from public data
- GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification249
A graph-based evidence aggregating and reasoning (GEAR) framework which enables information to transfer on a fully-connected evidence graph and then utilizes different aggregators to collect multi-evidence information is proposed.
- MAVEN: A Massive General Domain Event Detection Dataset232
A MAssive eVENt detection dataset is presented, which contains 4,480 Wikipedia documents, 117,200 event mention instances, and 207 event types and shows that existing ED methods cannot achieve promising results as on the small datasets, which suggests ED in real world remains a challenging task and requires further research efforts.
- SK-GCN: Modeling Syntax and Knowledge via Graph Convolutional Network for aspect-level sentiment classification210
A new Syntax- and Knowledge-based Graph Convolutional Network (SK-GCN) model is proposed for aspect-level sentiment classification, which leverages the syntactic dependency tree and commonsense knowledge via GCN to enhance the representation of the sentence toward the given aspect.
- RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models164
This work constructs a word-based robustness-aware perturbation to distinguish poisoned samples from clean samples to defend against the backdoor attacks on natural language processing (NLP) models.
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- Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision, and Challenges152
A taxonomy of deep learning-based aspect-level sentiment classification is designed and a comprehensive summary of the state-of-the-art methods is given, showing the tremendous progress that has already been made in ASC.
- Rethinking Stealthiness of Backdoor Attack against NLP Models151
A novel word-based backdoor attacking method based on negative data augmentation and modifying word embeddings is proposed, making an important step towards achieving stealthy backdoor attacking.
- CLEVE: Contrastive Pre-training for Event Extraction133
CLEVE is proposed, a contrastive pre-training framework for EE to better learn event knowledge from large unsupervised data and their semantic structures (e.g. AMR) obtained with automatic parsers.
- Improving Multi-turn Dialogue Modelling with Utterance ReWriter125
This paper proposes rewriting the human utterance as a pre-process to help multi-turn dialgoue modelling and introduces a Transformer-based utterance rewriting architecture using the pointer network, which achieves remarkably good performance on the utterance rewrite task.
- Cross-modal fine-grained alignment and fusion network for multimodal aspect-based sentiment analysis93
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- SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis93
This paper pre-train a sentiment-aware language model (SentiX) via domain-invariant sentiment knowledge from large-scale review datasets, and utilize it for cross-domain sentiment analysis task without fine-tuning.
- A dependency syntactic knowledge augmented interactive architecture for end-to-end aspect-based sentiment analysis91
This paper proposes a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA that significantly outperforms existing state-of-the-art methods.
- CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding90
A novel Collaborative Memory Network (CM-Net) based on the well-designed block, named CM-block, which achieves the state-of-the-art results on the ATIS and SNIPS in most of criteria, and significantly outperforms the baseline models on the CAIS.
- Recent Advances of Foundation Language Models-based Continual Learning: A Survey85
This article delves into a comprehensive review, summarization, and classification of the existing literature on CL-based approaches applied to foundation language models, such as pre-trained language models, large language models, and vision-language models.
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- Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances68
The DialoFlow model is proposed, in which a dynamic flow mechanism to model the context flow is introduced, and three training objectives are designed to capture the information dynamics across dialogue utterances by addressing the semantic influence brought about by each utterance in large-scale pre-training.
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- Position-aware hierarchical transfer model for aspect-level sentiment classification60
A position-aware hierarchical transfer (PAHT) model that models the position information from multiple levels and enhances the ASC performance by transferring hierarchical knowledge from the resource-rich sentence-level sentiment classification (SSC) dataset is proposed.
- A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis49
A novel Aspect-Guided Deep Transition model, named AGDT, is proposed, which utilizes the given aspect to guide the sentence encoding from scratch with the specially-designed deep transition architecture, and an aspect-oriented objective is designed to enforce AGDT to reconstruct thegiven aspect with the generated sentence representation.
- Towards Fine-grained Text Sentiment Transfer47
This paper proposes a novel Seq2SentiSeq model, where the numeric sentiment intensity value is incorporated into the decoder via a Gaussian kernel layer to finely control the sentiment intensity of the output.
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- An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis44
This work proposes a novel Iterative Multi-Knowledge Transfer Network (IMKTN) for end-to-end ABSA, which transfers the task-specific knowledge from any two of the three subtasks to another one at the token level by utilizing a well-designed routing algorithm.
- Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check42
A conversation-level RAG (ConvRAG) approach, which incorporates fine-grained retrieval augmentation and self-check for conversational question answering (CQA) and a Chinese CQA dataset with new features, which facilitates further researches in RAG enhanced CQA.
- Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack39
A Contrastive Zero-Shot Learning with Adversarial Attack (CZSL-Adv) method for the cross-domain slot filling that aims to map slot value contextual representations to the corresponding slot description representations and introduces an adversarial attack training strategy to improve model robustness.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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