2024
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Exploring Faithful and Informative Commonsense Reasoning and Moral Understanding in Children’s Stories
Zimu Wang
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Wang Yuqi
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Nijia Han
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Qi Chen
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Haiyang Zhang
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Yushan Pan
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Qiufeng Wang
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Wei Wang
Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
“Commonsense reasoning and moral understanding are crucial tasks in artificial intelligence (AI) and natural language processing (NLP). However, existing research often falls short in terms of faithfulness and informativeness during the reasoning process. We propose a novel framework for performing commonsense reasoning and moral understanding using large language models (LLMs), involving constructing guided prompts by incorporating relevant knowledge for commonsense reasoning and extracting facts from stories for moral understanding. We conduct extensive experiments on the Commonsense Reasoning and Moral Understanding in Children’s Stories (CRMUS) dataset with widely recognised LLMs under both zero-shot and fine-tuning settings, demonstrating the effectiveness of our proposed method. Furthermore, we analyse the adaptability of different LLMs in extracting facts for moral understanding performance.”
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MTSwitch: A Web-based System for Translation between Molecules and Texts
Nijia Han
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Zimu Wang
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Yuqi Wang
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Haiyang Zhang
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Daiyun Huang
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Wei Wang
Proceedings of the 17th International Natural Language Generation Conference: System Demonstrations
We introduce MTSwitch, a web-based system for the bidirectional translation between molecules and texts, leveraging various large language models (LLMs). It supports two crucial tasks, including molecule captioning (explaining the properties of a molecule) and molecule generation (designing a molecule based on specific properties). To the best of our knowledge, MTSwitch is currently the first accessible system that allows users to translate between molecular representations and descriptive text contents. The system and a screencast can be found in https://github.com/hanninaa/MTSwitch.
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Knowledge Distillation from Monolingual to Multilingual Models for Intelligent and Interpretable Multilingual Emotion Detection
Yuqi Wang
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Zimu Wang
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Nijia Han
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Wei Wang
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Qi Chen
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Haiyang Zhang
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Yushan Pan
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Anh Nguyen
Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
Emotion detection from text is a crucial task in understanding natural language with wide-ranging applications. Existing approaches for multilingual emotion detection from text face challenges with data scarcity across many languages and a lack of interpretability. We propose a novel method that leverages both monolingual and multilingual pre-trained language models to improve performance and interpretability. Our approach involves 1) training a high-performing English monolingual model in parallel with a multilingual model and 2) using knowledge distillation to transfer the emotion detection capabilities from the monolingual teacher to the multilingual student model. Experiments on a multilingual dataset demonstrate significant performance gains for refined multilingual models like XLM-RoBERTa and E5 after distillation. Furthermore, our approach enhances interpretability by enabling better identification of emotion-trigger words. Our work presents a promising direction for building accurate, robust and explainable multilingual emotion detection systems.