Machine Translation Evaluation Meets Community Question Answering
This work addresses answer ranking for community Question Answering platforms, but it is incremental as it adapts existing methods to a new domain.
The paper tackled answer ranking in community Question Answering by applying machine translation evaluation methods, achieving state-of-the-art performance with significant contributions from MTE features and a pairwise neural network architecture.
We explore the applicability of machine translation evaluation (MTE) methods to a very different problem: answer ranking in community Question Answering. In particular, we adopt a pairwise neural network (NN) architecture, which incorporates MTE features, as well as rich syntactic and semantic embeddings, and which efficiently models complex non-linear interactions. The evaluation results show state-of-the-art performance, with sizeable contribution from both the MTE features and from the pairwise NN architecture.