Abstract
Suggestion Mining is the task of classifying sentences into suggestions or non-suggestions. SemEval-2019 Task 9 sets the task to mine suggestions from online texts. For each of the two subtasks, the classification has to be applied on a different domain. Subtask A addresses the domain of posts in suggestion online forums and comes with a set of training examples, that is used for supervised training. A combination of LSTM and CNN networks is constructed to create a model which uses BERT word embeddings as input features. For subtask B, the domain of hotel reviews is regarded. In contrast to subtask A, no labeled data for supervised training is provided, so that additional unlabeled data is taken to apply a cross-domain classification. This is done by using adversarial training of the three model parts label classifier, domain classifier and the shared feature representation. For subtask A, the developed model archives a F1-score of 0.7273, which is in the top ten of the leader board. The F1-score for subtask B is 0.8187 and is ranked in the top five of the submissions for that task.- Anthology ID:
- S19-2209
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Editors:
- Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1192–1198
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/S19-2209/
- DOI:
- 10.18653/v1/S19-2209
- Cite (ACL):
- Tobias Cabanski. 2019. DS at SemEval-2019 Task 9: From Suggestion Mining with neural networks to adversarial cross-domain classification. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1192–1198, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- DS at SemEval-2019 Task 9: From Suggestion Mining with neural networks to adversarial cross-domain classification (Cabanski, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/S19-2209.pdf