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Zhenzhou Ji

7 accepted papers

2025

A Dual Contrastive Learning Framework for Enhanced Multimodal Conversational Emotion Recognition

COLING 2025main

Multimodal Emotion Recognition in Conversations (MERC) identifies utterance emotions by integrating both contextual and multimodal information from dialogue videos. Existing methods struggle to capture emotion shifts due to label replication and fail to preserve positive independent modality contrib…

2025

Do LLMs Behave as Claimed? Investigating How LLMs Follow Their Own Claims using Counterfactual Questions

EMNLP 2025

Large Language Models (LLMs) require robust evaluation. However, existing frameworks often rely on curated datasets that, once public, may be accessed by newer LLMs. This creates a risk of data leakage, where test sets inadvertently become part of training data, compromising evaluation fairness and

Cited by 0SourcePDFScholar
2022

A Commonsense Knowledge Enhanced Network with Retrospective Loss for Emotion Recognition in Spoken Dialog

ICASSP 2022accepted

The recent surges in the open conversational data caused Emotion Recognition in Spoken Dialog (ERSD) to gain much attention. However, the existing ERSD datasets’ scale limits the model’s complete reasoning. Moreover, the artificial dialogue agent is ideally able to reference past dialogue experience…

Cited by 0SourceScholar
2022

How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis

ACL 2022findings

Recently, there has been a trend to investigate the factual knowledge captured by Pre-trained Language Models (PLMs). Many works show the PLMs’ ability to fill in the missing factual words in cloze-style prompts such as ”Dante was born in [MASK].” However, it is still a mystery how PLMs generate the…

Cited by 54SourcePDFScholar
2022

Pre-training Language Models with Deterministic Factual Knowledge

EMNLP 2022main

Previous works show that Pre-trained Language Models (PLMs) can capture factual knowledge. However, some analyses reveal that PLMs fail to perform it robustly, e.g., being sensitive to the changes of prompts when extracting factual knowledge. To mitigate this issue, we propose to let PLMs learn the…

2021

HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions

AAAI 2021technical

Collecting supporting evidence from large corpora of text (e.g., Wikipedia) is of great challenge for open-domain Question Answering (QA). Especially, for multi-hop open-domain QA, scattered evidence pieces are required to be gathered together to support the answer extraction. In this paper, we prop…

Cited by 36SourcePDFScholar
2021

Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations

EMNLP 2021finding

Emotion Recognition in Conversation (ERC) has gained much attention from the NLP community recently. Some models concentrate on leveraging commonsense knowledge or multi-task learning to help complicated emotional reasoning. However, these models neglect direct utterance-knowledge interaction. In ad…

Cited by 40SourcePDFScholar