# Machine Translation Evaluation Meets Community Question Answering

arXiv: https://arxiv.org/abs/1912.02998 · HTML: https://mlsanity.com/paper/1912.02998/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [SUper Team at SemEval-2016 Task 3: Building a feature-rich system for community question answering](https://mlsanity.com/paper/2109.15120/) (2021)

## Abstract (arXiv)

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.