Yequan Wang


2022

pdf bib
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict
Yiyi Liu | Yequan Wang | Aixin Sun | Xuying Meng | Jing Li | Jiafeng Guo
Findings of the Association for Computational Linguistics: NAACL 2022

Sarcasm employs ambivalence, where one says something positive but actually means negative, and vice versa. The essence of sarcasm, which is also a sufficient and necessary condition, is the conflict between literal and implied sentiments expressed in one sentence. However, it is difficult to recognize such sentiment conflict because the sentiments are mixed or even implicit. As a result, the recognition of sophisticated and obscure sentiment brings in a great challenge to sarcasm detection. In this paper, we propose a Dual-Channel Framework by modeling both literal and implied sentiments separately. Based on this dual-channel framework, we design the Dual-Channel Network (DC-Net) to recognize sentiment conflict. Experiments on political debates (i.e. IAC-V1 and IAC-V2) and Twitter datasets show that our proposed DC-Net achieves state-of-the-art performance on sarcasm recognition. Our code is released to support research.

pdf bib
CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction
Yequan Wang | Xiang Li | Aixin Sun | Xuying Meng | Huaming Liao | Jiafeng Guo
Proceedings of the 29th International Conference on Computational Linguistics

Quotation extraction aims to extract quotations from written text. There are three components in a quotation: source refers to the holder of the quotation, cue is the trigger word(s), and content is the main body. Existing solutions for quotation extraction mainly utilize rule-based approaches and sequence labeling models. While rule-based approaches often lead to low recalls, sequence labeling models cannot well handle quotations with complicated structures. In this paper, we propose the Context and Former-Label Enhanced Net () for quotation extraction. is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets (and ) and one proprietary dataset (), we show that our achieves state-of-the-art performance on complicated quotation extraction.

2016

pdf bib
Attention-based LSTM for Aspect-level Sentiment Classification
Yequan Wang | Minlie Huang | Xiaoyan Zhu | Li Zhao
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing