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CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts
We approach comparative opinion classification through prompt learning, taking the advantage of embedded knowledge in pre-trained language model. We design a twin framework with dual prompts, named CORT. This extremely simple model delivers state-of-the-art and robust performance on all benchmark datasets for comparative opinion classification. We believe CORT well serves as a new baseline for comparative opinion classification.
Yequan Wang
,
Hengran Zhang
,
Aixin Sun
,
Xuying Meng
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EDU-Capsule: aspect-based sentiment analysis at clause level
Elementary discourse unit (EDU) in rhetorical structure theory is an atomic semantic unit, similar to a clause in a sentence. In this paper, we propose to study ABSA at EDU-level.
Ting Lin
,
Aixin Sun
,
Yequan Wang
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阿拉伯语语言大模型 (ALM 1.0)
我们构建并开源了阿拉伯语语言大模型 (ALM 1.0)。
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CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction
We propose the Context and Former-Label Enhanced Net (CofeNet) for quotation extraction. CofeNet is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets and one proprietary dataset, we show that our achieves state-of-the-art performance on complicated quotation extraction.
Yequan Wang
,
Xiang Li
,
Aixin Sun
,
Xuying Meng
,
Huaming Liao
,
Jiafeng Guo
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A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict
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.
Yiyi Liu
,
Yequan Wang
,
Aixin Sun
,
Xuying Meng
,
Jing Li
,
Jiafeng Guo
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