| Distilbert finetuned for Aspect-Based Sentiment Analysis (ABSA) with auxiliary sentence. | |
| Fine-tuned using a dataset provided by NAVER for the CentraleSupélec NLP course. | |
| ```bibtex | |
| @inproceedings{sun-etal-2019-utilizing, | |
| title = "Utilizing {BERT} for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence", | |
| author = "Sun, Chi and | |
| Huang, Luyao and | |
| Qiu, Xipeng", | |
| booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", | |
| month = jun, | |
| year = "2019", | |
| address = "Minneapolis, Minnesota", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://www.aclweb.org/anthology/N19-1035", | |
| doi = "10.18653/v1/N19-1035", | |
| pages = "380--385", | |
| abstract = "Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA). In this paper, we construct an auxiliary sentence from the aspect and convert ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI). We fine-tune the pre-trained model from BERT and achieve new state-of-the-art results on SentiHood and SemEval-2014 Task 4 datasets. The source codes are available at https://github.com/HSLCY/ABSA-BERT-pair.", | |
| } | |
| ``` |