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
2017 Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)  
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.
doi:10.18653/v1/s17-2048 dblp:conf/semeval/AttardiCEMP17 fatcat:w7d6cd2gcbg7jitxmgni7fl52m