activity
20182022
most citedTopic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection

11 citations · 32 across the 11 of their papers we have counts for

collaborators

14 papers

cs.GR20221 cited

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…

cs.CL20222 cited

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…

cs.CL20225 cited

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…

cs.CL20212 cited

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…

cs.CL202111 cited

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…

cs.AI20211 cited

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…