Abstract
Sentiment analysis is an important task in natural language processing. In recent works, pre-trained language models are often used to achieve state-of-the-art results, especially when training data is scarce. It is common to fine-tune on the downstream task, usually by adding task-specific layers on top of the model. In this paper, we focus on aspect-based sentiment analysis, which involves extracting aspect term, category, and predicting their corresponding polarities. In particular, we are interested in few-shot settings. We propose to reformulate the extraction and prediction tasks into the sequence generation task, using a generative language model with unidirectional attention (GPT2 is used unless stated otherwise). This way, the model learns to accomplish the tasks via language generation without the need of training task-specific layers. Our evaluation results on the single-task polarity prediction show that our approach outperforms the previous state-of-the-art (based on BERT) on average performance by a large margins in few-shot and full-shot settings. More importantly, our generative approach significantly reduces the model variance caused by low-resource data. We further demonstrate that the proposed generative language model can handle joint and multi-task settings, unlike previous work. We observe that the proposed sequence generation method achieves further improved performances on polarity prediction when the model is trained via joint and multi-task settings. Further evaluation on similar sentiment analysis datasets, SST-2, SST-5 and OOS intent detection validates the superiority and noise robustness of generative language model in few-shot settings.- Anthology ID:
- 2022.findings-naacl.58
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2022
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 770–787
- Language:
- URL:
- https://aclanthology.org/2022.findings-naacl.58
- DOI:
- 10.18653/v1/2022.findings-naacl.58
- Cite (ACL):
- Ehsan Hosseini-Asl, Wenhao Liu, and Caiming Xiong. 2022. A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 770–787, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis (Hosseini-Asl et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.findings-naacl.58.pdf
- Code
- salesforce/fewshot_absa
- Data
- SST