Machine Translation Evaluation Meets Community Question Answering

Francisco Guzmán; Lluís Màrquez; Preslav Nakov

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arXiv · 1912.02998

Machine Translation Evaluation Meets Community Question Answering

Francisco Guzmán·Lluís Màrquez·Preslav Nakov

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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.

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Francisco Guzmán, Lluís Màrquez, Preslav Nakov. 2019-12-06. Machine Translation Evaluation Meets Community Question Answering. https://arxiv.org/abs/1912.02998

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