Abstract
We propose a novel multi-grained attention network (MGAN) model for aspect level sentiment classification. Existing approaches mostly adopt coarse-grained attention mechanism, which may bring information loss if the aspect has multiple words or larger context. We propose a fine-grained attention mechanism, which can capture the word-level interaction between aspect and context. And then we leverage the fine-grained and coarse-grained attention mechanisms to compose the MGAN framework. Moreover, unlike previous works which train each aspect with its context separately, we design an aspect alignment loss to depict the aspect-level interactions among the aspects that have the same context. We evaluate the proposed approach on three datasets: laptop and restaurant are from SemEval 2014, and the last one is a twitter dataset. Experimental results show that the multi-grained attention network consistently outperforms the state-of-the-art methods on all three datasets. We also conduct experiments to evaluate the effectiveness of aspect alignment loss, which indicates the aspect-level interactions can bring extra useful information and further improve the performance.- Anthology ID:
- D18-1380
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3433–3442
- Language:
- URL:
- https://aclanthology.org/D18-1380
- DOI:
- 10.18653/v1/D18-1380
- Cite (ACL):
- Feifan Fan, Yansong Feng, and Dongyan Zhao. 2018. Multi-grained Attention Network for Aspect-Level Sentiment Classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3433–3442, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Multi-grained Attention Network for Aspect-Level Sentiment Classification (Fan et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/D18-1380.pdf
- Data
- SemEval-2014 Task-4