# Machine Translation Evaluation Meets Community Question Answering

Source: [Machine Translation Evaluation Meets Community Question Answering](<https://www.sota2.com/research/paper/68fa66ce1e209131cea12798>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:59:21.344Z

Authors: Francisco Guzm\\'an, Llu\\'is M\\\`arquez, Preslav Nakov

Published: 2019-12-06T06:35:21.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1912.02998>)

[PDF](<https://arxiv.org/pdf/1912.02998.pdf>)