Machine Translation Evaluation Meets Community Question Answering
Computation and Language
2019-12-09 v1 Information Retrieval
Machine Learning
Abstract
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.
Cite
@article{arxiv.1912.02998,
title = {Machine Translation Evaluation Meets Community Question Answering},
author = {Francisco Guzmán and Lluís Màrquez and Preslav Nakov},
journal= {arXiv preprint arXiv:1912.02998},
year = {2019}
}
Comments
community question answering, machine translation evaluation, pairwise ranking, learning to rank