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Fine-Grained Sentiment-Controlled Text Generation Approach Based on Pre-Trained Language Model
- Source :
- Applied Sciences, Vol 13, Iss 1, p 264 (2022)
- Publication Year :
- 2022
- Publisher :
- MDPI AG, 2022.
-
Abstract
- Sentiment-controlled text generation aims to generate texts according to the given sentiment. However, most of the existing studies focus only on the document- or sentence-level sentiment control, leaving a gap for finer-grained control over the content of generated results. Fine-grained control allows a generated review to express different opinions toward multiple aspects. Some previous works attempted to generate reviews conditioned on aspect-level sentiments, but they usually suffer from low adaptability and the lack of an annotated dataset. To alleviate these problems, we propose a novel pre-trained extended generative model that can dynamically refer to the prompt sentiment, together with an auxiliary classifier that extracts the fine-grained sentiments from the unannotated sentences, thus we conducted training on both annotated and unannotated datasets. We also propose a query-hint mechanism to further guide the generation process toward the aspect-level sentiments at every time step. Experimental results from real-world datasets demonstrated that our model has excellent adaptability in generating aspect-level sentiment-controllable review texts with high sentiment coverage and stable quality since, on both datasets, our model steadily outperforms other baseline models in the metrics of BLEU-4, METETOR, and ROUGE-L etc. The limitation of this work is that we only focus on fine-grained sentiments that are explicitly expressed. Moreover, the implicitly expressed fine-grained sentiment-controllable text generation will be an important puzzle for future work.
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 13
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Applied Sciences
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.1367ba1d2cd42da824419c1a0788f33
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/app13010264