Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.08777 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909091995058176 |
|---|---|
| author | Liu, Haochen Rallabandi, Sai Krishna Wu, Yijing Dakle, Parag Pravin Raghavan, Preethi |
| author_facet | Liu, Haochen Rallabandi, Sai Krishna Wu, Yijing Dakle, Parag Pravin Raghavan, Preethi |
| contents | Sentiment analysis is a crucial task in natural language processing that involves identifying and extracting subjective sentiment from text. Self-training has recently emerged as an economical and efficient technique for developing sentiment analysis models by leveraging a small amount of labeled data and a large amount of unlabeled data. However, given a set of training data, how to utilize them to conduct self-training makes a significant difference in the final performance of the model. We refer to this methodology as the self-training strategy. In this paper, we present an empirical study of various self-training strategies for sentiment analysis. First, we investigate the influence of the self-training strategy and hyper-parameters on the performance of traditional small language models (SLMs) in various few-shot settings. Second, we also explore the feasibility of leveraging large language models (LLMs) to help self-training. We propose and empirically compare several self-training strategies with the intervention of LLMs. Extensive experiments are conducted on three real-world sentiment analysis datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_08777 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Self-training Strategies for Sentiment Analysis: An Empirical Study Liu, Haochen Rallabandi, Sai Krishna Wu, Yijing Dakle, Parag Pravin Raghavan, Preethi Computation and Language Sentiment analysis is a crucial task in natural language processing that involves identifying and extracting subjective sentiment from text. Self-training has recently emerged as an economical and efficient technique for developing sentiment analysis models by leveraging a small amount of labeled data and a large amount of unlabeled data. However, given a set of training data, how to utilize them to conduct self-training makes a significant difference in the final performance of the model. We refer to this methodology as the self-training strategy. In this paper, we present an empirical study of various self-training strategies for sentiment analysis. First, we investigate the influence of the self-training strategy and hyper-parameters on the performance of traditional small language models (SLMs) in various few-shot settings. Second, we also explore the feasibility of leveraging large language models (LLMs) to help self-training. We propose and empirically compare several self-training strategies with the intervention of LLMs. Extensive experiments are conducted on three real-world sentiment analysis datasets. |
| title | Self-training Strategies for Sentiment Analysis: An Empirical Study |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2309.08777 |