FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering
Giuseppe Attardi, Antonio Carta, Federico Errica, Andrea Madotto, Ludovica Pannitto
Abstract
In this paper we present ThReeNN, a model for Community Question Answering, Task 3, of SemEval-2017. The proposed model exploits both syntactic and semantic information to build a single and meaningful embedding space. Using a dependency parser in combination with word embeddings, the model creates sequences of inputs for a Recurrent Neural Network, which are then used for the ranking purposes of the Task. The score obtained on the official test data shows promising results.- Anthology ID:
- S17-2048
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 299–304
- Language:
- URL:
- https://aclanthology.org/S17-2048
- DOI:
- 10.18653/v1/S17-2048
- Cite (ACL):
- Giuseppe Attardi, Antonio Carta, Federico Errica, Andrea Madotto, and Ludovica Pannitto. 2017. FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 299–304, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering (Attardi et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/S17-2048.pdf