@inproceedings{attardi-etal-2017-fa3l,
title = "{FA}3{L} at {S}em{E}val-2017 Task 3: A {T}h{R}ee Embeddings Recurrent Neural Network for Question Answering",
author = "Attardi, Giuseppe and
Carta, Antonio and
Errica, Federico and
Madotto, Andrea and
Pannitto, Ludovica",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2048",
doi = "10.18653/v1/S17-2048",
pages = "299--304",
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.",
}
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%0 Conference Proceedings
%T FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering
%A Attardi, Giuseppe
%A Carta, Antonio
%A Errica, Federico
%A Madotto, Andrea
%A Pannitto, Ludovica
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F attardi-etal-2017-fa3l
%X 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.
%R 10.18653/v1/S17-2048
%U https://aclanthology.org/S17-2048
%U https://doi.org/10.18653/v1/S17-2048
%P 299-304
Markdown (Informal)
[FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering](https://aclanthology.org/S17-2048) (Attardi et al., SemEval 2017)
ACL