Machine Translation Evaluation Meets Community Question Answering

2019-12-06 · ACL 2016 8 · Francisco Guzmán, Lluís Màrquez, Preslav Nakov

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.

📄 PDF Abstract BibTeX arXiv:1912.02998

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Tasks

Community Question Answering Machine Translation Question Answering Translation

MTE-NN at SemEval-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering?

2016-06-01 · SEMEVAL 2016 6 · Francisco Guzm{\'a}n, Preslav Nakov, Llu{\'\i}s M{\`a}rquez

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This paper studies the impact of different types of features applied to learning to re-rank questions in community Question Answering. We tested our models on two datasets released in SemEval-2016 Task 3 on "Community Qu…

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