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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Bing, Lidong; Hai, Zhen. - : Underline Science Inc., 2021 BASE Show details 2 On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Bing, Lidong; Cheng, liying.cheng@alibaba-inc.com. - : Underline Science Inc., 2021 BASE Show details 3 Towards Generative Aspect-Based Sentiment Analysis ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Wai; Bing, Lidong; Deng, Yang; Li, Xin; Zhang, Wenxuan. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-short.64 Abstract: Aspect-based sentiment analysis (ABSA) has received increasing attention recently. Most existing work tackles ABSA in a discriminative manner, designing various task-specific classification networks for the prediction. Despite their effectiveness, these methods ignore the rich label semantics in ABSA problems and require extensive task-specific designs. In this paper, we propose to tackle various ABSA tasks in a unified generative framework. Two types of paradigms, namely annotation-style and extraction-style modeling, are designed to enable the training process by formulating each ABSA task as a text generation problem. We conduct experiments on four ABSA tasks across multiple benchmark datasets where our proposed generative approach achieves new state-of-the-art results in almost all cases. This also validates the strong generality of the proposed framework which can be easily adapted to arbitrary ABSA task without additional ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://dx.doi.org/10.48448/kr6a-6s64 https://underline.io/lecture/25603-towards-generative-aspect-based-sentiment-analysis BASE Hide details 4 Argument Pair Extraction via Attention-guided Multi-Layer Multi-Cross Encoding ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Bing, Lidong; Cheng, Liying. - : Underline Science Inc., 2021 BASE Show details 5 Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Bing, Lidong; Chia, Yew Ken. - : Underline Science Inc., 2021 BASE Show details 6 MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Bing, Lidong; Ding, Bosheng. - : Underline Science Inc., 2021 BASE Show details

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