Papers by Baoquan Zhang

2 papers
A Challenge Dataset and Effective Models for Conversational Stance Detection (2024.lrec-main)

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Challenge: stance detection studies focus on evaluating stances within individual instances, hindering progress of conversational stance analysis.
Approach: They propose a multi-turn conversation stance detection dataset that encompasses multiple targets for conversational stance detector.
Outcome: The proposed dataset encompasses multiple targets for conversational stance detection.
Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis (2022.coling-1)

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Challenge: Aspect-term sentiment analysis (ATSA) is a fine-grained task that aims to infer the sentiment towards the given aspect-terms.
Approach: They propose a novel ATSA method that is interpretable and has high accuracy . they propose SILTN, which is a neurosymbolic formalism, to improve the accuracy based on syntax knowledge distillation.
Outcome: The proposed method is interpretable because it is a neurosymbolic formalism and a computational model that supports learning and reasoning about data with a differentiable first-order logic language.

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