@inproceedings{vacareanu-etal-2024-weak,
title = "A Weak Supervision Approach for Few-Shot Aspect Based Sentiment Analysis",
author = "Vacareanu, Robert and
Varia, Siddharth and
Halder, Kishaloy and
Wang, Shuai and
Paolini, Giovanni and
Anna John, Neha and
Ballesteros, Miguel and
Muresan, Smaranda",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.167",
pages = "2734--2752",
abstract = "We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks. We propose a pipeline approach to construct a noisy ABSA dataset, and we use it to adapt a pre-trained sequence-to-sequence model to the ABSA tasks. We test the resulting model on three widely used ABSA datasets, before and after fine-tuning. Our proposed method preserves the full fine-tuning performance while showing significant improvements (15.84 absolute F1) in the few-shot learning scenario for the harder tasks. In zero-shot (i.e., without fine-tuning), our method outperforms the previous state of the art on the aspect extraction sentiment classification (AESC) task and is, additionally, capable of performing the harder aspect sentiment triplet extraction (ASTE) task.",
}
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<abstract>We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks. We propose a pipeline approach to construct a noisy ABSA dataset, and we use it to adapt a pre-trained sequence-to-sequence model to the ABSA tasks. We test the resulting model on three widely used ABSA datasets, before and after fine-tuning. Our proposed method preserves the full fine-tuning performance while showing significant improvements (15.84 absolute F1) in the few-shot learning scenario for the harder tasks. In zero-shot (i.e., without fine-tuning), our method outperforms the previous state of the art on the aspect extraction sentiment classification (AESC) task and is, additionally, capable of performing the harder aspect sentiment triplet extraction (ASTE) task.</abstract>
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%0 Conference Proceedings
%T A Weak Supervision Approach for Few-Shot Aspect Based Sentiment Analysis
%A Vacareanu, Robert
%A Varia, Siddharth
%A Halder, Kishaloy
%A Wang, Shuai
%A Paolini, Giovanni
%A Anna John, Neha
%A Ballesteros, Miguel
%A Muresan, Smaranda
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F vacareanu-etal-2024-weak
%X We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks. We propose a pipeline approach to construct a noisy ABSA dataset, and we use it to adapt a pre-trained sequence-to-sequence model to the ABSA tasks. We test the resulting model on three widely used ABSA datasets, before and after fine-tuning. Our proposed method preserves the full fine-tuning performance while showing significant improvements (15.84 absolute F1) in the few-shot learning scenario for the harder tasks. In zero-shot (i.e., without fine-tuning), our method outperforms the previous state of the art on the aspect extraction sentiment classification (AESC) task and is, additionally, capable of performing the harder aspect sentiment triplet extraction (ASTE) task.
%U https://aclanthology.org/2024.eacl-long.167
%P 2734-2752
Markdown (Informal)
[A Weak Supervision Approach for Few-Shot Aspect Based Sentiment Analysis](https://aclanthology.org/2024.eacl-long.167) (Vacareanu et al., EACL 2024)
ACL
- Robert Vacareanu, Siddharth Varia, Kishaloy Halder, Shuai Wang, Giovanni Paolini, Neha Anna John, Miguel Ballesteros, and Smaranda Muresan. 2024. A Weak Supervision Approach for Few-Shot Aspect Based Sentiment Analysis. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2734–2752, St. Julian’s, Malta. Association for Computational Linguistics.