MTE-NN at SemEval-2016 task 3: Can machine translation evaluation help community question answering?

Abstract

We present a system for answer ranking (SemEval-2016 Task 3, subtask A) that is a direct adaptation of a pairwise neural network model for machine translation evaluation (MTE). In particular, the network incorporates MTE features, as well as rich syntactic and semantic embeddings, and it efficiently models complex non-linear interactions between them. With the addition of lightweight task-specific features, we obtained very encouraging experimental results, with sizeable contributions from both the MTE features and from the pairwise network architecture. We also achieved good results on subtask C.