11 citations · 45 across the 18 of their papers we have counts for
18 papers
EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification
Yingjie Zhu, Jiasheng Si, Yibo Zhao +3
Automatic multi-hop fact verification task has gained significant attention in recent years. Despite impressive results, these well-designed models perform poorly on out-of-domain…
Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources
Jiasheng Si, Yingjie Zhu, Xingyu Shi +2
Given a controversial target such as ``nuclear energy'', argument mining aims to identify the argumentative text from heterogeneous sources. Current approaches focus on exploring b…
Feature Representation Learning for NL2SQL Generation Based on Coupling and Decoupling
Chenduo Hao, Xu Zhang, Chuanbao Gao +1
The NL2SQL task involves parsing natural language statements into SQL queries. While most state-of-the-art methods treat NL2SQL as a slot-filling task and use feature representatio…
Consistent Multi-Granular Rationale Extraction for Explainable Multi-hop Fact Verification
Jiasheng Si, Yingjie Zhu, Deyu Zhou
The success of deep learning models on multi-hop fact verification has prompted researchers to understand the behavior behind their veracity. One possible way is erasure search: ob…
Talking Head Generation with Probabilistic Audio-to-Visual Diffusion Priors
Zhentao Yu, Zixin Yin, Deyu Zhou +3
In this paper, we introduce a simple and novel framework for one-shot audio-driven talking head generation. Unlike prior works that require additional driving sources for controlle…
Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning
Jiasheng Si, Yingjie Zhu, Deyu Zhou
The opaqueness of the multi-hop fact verification model imposes imperative requirements for explainability. One feasible way is to extract rationales, a subset of inputs, where the…
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition
Zeng Yang, Linhai Zhang, Deyu Zhou
Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Current few-shot NER methods focus on leveraging existing datasets i…
Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification
Jiasheng Si, Deyu Zhou, Tongzhe Li +2
Fact verification is a challenging task that requires simultaneously reasoning and aggregating over multiple retrieved pieces of evidence to evaluate the truthfulness of a claim. E…
Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection
Lixing Zhu, Gabriele Pergola, Lin Gui +2
Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intr…
Variational Gaussian Topic Model with Invertible Neural Projections
Rui Wang, Deyu Zhou, Yuxuan Xiong +1
Neural topic models have triggered a surge of interest in extracting topics from text automatically since they avoid the sophisticated derivations in conventional topic models. How…
Neural Topic Modeling by Incorporating Document Relationship Graph
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.…
Neural Topic Modeling with Cycle-Consistent Adversarial Training
Xuemeng Hu, Rui Wang, Deyu Zhou +1
Advances on deep generative models have attracted significant research interest in neural topic modeling. The recently proposed Adversarial-neural Topic Model models topics with an…
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings
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. In particular, we assume that global late…
Neural Temporal Opinion Modelling for Opinion Prediction on Twitter
Lixing Zhu, Yulan He, Deyu Zhou
Opinion prediction on Twitter is challenging due to the transient nature of tweet content and neighbourhood context. In this paper, we model users' tweet posting behaviour as a tem…
Neural Topic Modeling with Bidirectional Adversarial Training
Rui Wang, Xuemeng Hu, Deyu Zhou +4
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 fo…
Variational Conditional GAN for Fine-grained Controllable Image Generation
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. Traditional gen…
Open Event Extraction from Online Text using a Generative Adversarial Network
Rui Wang, Deyu Zhou, Yulan He
To extract the structured representations of open-domain events, Bayesian graphical models have made some progress. However, these approaches typically assume that all words in a d…
ATM:Adversarial-neural Topic Model
Rui Wang, Deyu Zhou, Yulan He
Topic models are widely used for thematic structure discovery in text. But traditional topic models often require dedicated inference procedures for specific tasks at hand. Also, t…