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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/paclic-22-ingestion/S17-2048.pdf