Abstract
We firstly use GloVe to learn the distributed representations automatically from the instance, question and answer triples. Then an attentionbased Bidirectional LSTM (BiLSTM) model is used to encode the triples. We also perform a simple ensemble method to improve the effectiveness of our model. The system we developed obtains an encouraging result on this task. It achieves the accuracy 0.7472 on the test set. We rank 5th according to the official ranking.- Anthology ID:
- S18-1174
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venues:
- SemEval | *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1043–1047
- Language:
- URL:
- https://aclanthology.org/S18-1174
- DOI:
- 10.18653/v1/S18-1174
- Cite (ACL):
- Peng Ding and Xiaobing Zhou. 2018. YNU_Deep at SemEval-2018 Task 11: An Ensemble of Attention-based BiLSTM Models for Machine Comprehension. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 1043–1047, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- YNU_Deep at SemEval-2018 Task 11: An Ensemble of Attention-based BiLSTM Models for Machine Comprehension (Ding & Zhou, SemEval-*SEM 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/S18-1174.pdf