# Learning to Translate for Multilingual Question Answering

Source: [Learning to Translate for Multilingual Question Answering](<https://www.sota2.com/research/paper/68fa62bc1e209131ce9fafd1>)

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

Data updated: 2025-10-28T13:56:32.302Z

Authors: Ferhan Ture, Elizabeth Boschee

Published: 2016-09-26T22:12:50.000Z

## Abstract

In multilingual question answering, either the question needs to be translated into the document language, or vice versa. In addition to direction, there are multiple methods to perform the translation, four of which we explore in this paper: word-based, 10-best, context-based, and grammar-based. We build a feature for each combination of translation direction and method, and train a model that learns optimal feature weights. On a large forum dataset consisting of posts in English, Arabic, and Chinese, our novel learn-to-translate approach was more effective than a strong baseline (p\<0.05): translating all text into English, then training a classifier based only on English (original or translated) text.

## 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/1609.08210>)

[PDF](<https://arxiv.org/pdf/1609.08210.pdf>)