Search Results for author: Deyu Zhou

Found 27 papers, 4 papers with code

Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge

no code implementations ACL 2022 Linhai Zhang, Xuemeng Hu, Boyu Wang, Deyu Zhou, Qian-Wen Zhang, Yunbo Cao

Recent years have witnessed growing interests in incorporating external knowledge such as pre-trained word embeddings (PWEs) or pre-trained language models (PLMs) into neural topic modeling.

Topic Models Word Embeddings

Implicit Sentiment Analysis with Event-centered Text Representation

no code implementations EMNLP 2021 Deyu Zhou, Jianan Wang, Linhai Zhang, Yulan He

Implicit sentiment analysis, aiming at detecting the sentiment of a sentence without sentiment words, has become an attractive research topic in recent years.

Representation Learning Sentiment Analysis

A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering

no code implementations Findings (EMNLP) 2021 Deyu Zhou, Yanzheng Xiang, Linhai Zhang, Chenchen Ye, Qian-Wen Zhang, Yunbo Cao

However, most of existing approaches only detect one single path to obtain the answer without considering other correct paths, which might affect the final performance.

Knowledge Base Question Answering

Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection

1 code implementation ACL 2021 Lixing Zhu, Gabriele Pergola, Lin Gui, Deyu Zhou, Yulan He

Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states.

Emotion Recognition in Conversation

Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification

1 code implementation ACL 2021 Jiasheng Si, Deyu Zhou, Tongzhe Li, Xingyu Shi, Yulan He

To alleviate the above issues, we propose a novel topic-aware evidence reasoning and stance-aware aggregation model for more accurate fact verification, with the following four key properties: 1) checking topical consistency between the claim and evidence; 2) maintaining topical coherence among multiple pieces of evidence; 3) ensuring semantic similarity between the global topic information and the semantic representation of evidence; 4) aggregating evidence based on their implicit stances to the claim.

Fact Verification Semantic Similarity +1

Variational Gaussian Topic Model with Invertible Neural Projections

no code implementations21 May 2021 Rui Wang, Deyu Zhou, Yuxuan Xiong, Haiping Huang

Based on the variational auto-encoder, the proposed VaGTM models each topic with a multivariate Gaussian in decoder to incorporate word relatedness.

Topic Models Word Embeddings

Neural Topic Modeling with Cycle-Consistent Adversarial Training

no code implementations EMNLP 2020 Xuemeng Hu, Rui Wang, Deyu Zhou, Yuxuan Xiong

ToMCAT employs a generator network to interpret topics and an encoder network to infer document topics.

Text Classification

Neural Topic Modeling by Incorporating Document Relationship Graph

no code implementations EMNLP 2020 Deyu Zhou, Xuemeng Hu, Rui Wang

Graph Neural Networks (GNNs) that capture the relationships between graph nodes via message passing have been a hot research direction in the natural language processing community.

A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings

1 code implementation11 Aug 2020 Lixing Zhu, Yulan He, Deyu Zhou

We propose a novel generative model to explore both local and global context for joint learning topics and topic-specific word embeddings.

Sentiment Analysis Topic Models +3

Neural Temporal Opinion Modelling for Opinion Prediction on Twitter

no code implementations ACL 2020 Lixing Zhu, Yulan He, Deyu Zhou

Opinion prediction on Twitter is challenging due to the transient nature of tweet content and neighbourhood context.

Neural Topic Modeling with Bidirectional Adversarial Training

1 code implementation ACL 2020 Rui Wang, Xuemeng Hu, Deyu Zhou, Yulan He, Yuxuan Xiong, Chenchen Ye, Haiyang Xu

Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA).

Text Clustering Topic Models

Interpretable Relevant Emotion Ranking with Event-Driven Attention

no code implementations IJCNLP 2019 Yang Yang, Deyu Zhou, Yulan He, Meng Zhang

Unveiling the hidden event information can help to understand how the emotions are evoked and provide explainable results.

Variational Conditional GAN for Fine-grained Controllable Image Generation

no code implementations22 Sep 2019 Mingqi Hu, Deyu Zhou, Yulan He

In this paper, we propose a novel variational generator framework for conditional GANs to catch semantic details for improving the generation quality and diversity.

Image Generation Variational Inference

Open Event Extraction from Online Text using a Generative Adversarial Network

no code implementations IJCNLP 2019 Rui Wang, Deyu Zhou, Yulan He

Experimental results show that our model outperforms the baseline approaches on all the datasets, with more significant improvements observed on the news article dataset where an increase of 15\% is observed in F-measure.

Event Extraction

ATM:Adversarial-neural Topic Model

no code implementations1 Nov 2018 Rui Wang, Deyu Zhou, Yulan He

The proposed ATM models topics with Dirichlet prior and employs a generator network to capture the semantic patterns among latent topics.

Event Extraction Topic Models

Relevant Emotion Ranking from Text Constrained with Emotion Relationships

no code implementations NAACL 2018 Deyu Zhou, Yang Yang, Yulan He

As such, emotion detection, to predict multiple emotions associated with a given text, can be cast into a multi-label classification problem.

Multi-Label Classification Multi-Label Learning +1

Neural Storyline Extraction Model for Storyline Generation from News Articles

no code implementations NAACL 2018 Deyu Zhou, Linsen Guo, Yulan He

To tackle this problem, approaches based on probabilistic graphic models jointly model the generations of events and storylines without the use of annotated data.

Topic Models

Event extraction from Twitter using Non-Parametric Bayesian Mixture Model with Word Embeddings

no code implementations EACL 2017 Deyu Zhou, Xuan Zhang, Yulan He

To extract structured representations of newsworthy events from Twitter, unsupervised models typically assume that tweets involving the same named entities and expressed using similar words are likely to belong to the same event.

Event Extraction Word Embeddings

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