# Cross-language Learning with Adversarial Neural Networks: Application to Community Question Answering

Source: [Cross-language Learning with Adversarial Neural Networks: Application to Community Question Answering](<https://www.sota2.com/research/paper/68fa636e1e209131ce9fec3e>)

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

Data updated: 2025-10-25T20:41:16.530Z

Authors: Shafiq Joty, Preslav Nakov, Llu\\'is M\\\`arquez, Israa Jaradat

Published: 2017-06-21T06:11:59.000Z

## Abstract

We address the problem of cross-language adaptation for question-question similarity reranking in community question answering, with the objective to port a system trained on one input language to another input language given labeled training data for the first language and only unlabeled data for the second language. In particular, we propose to use adversarial training of neural networks to learn high-level features that are discriminative for the main learning task, and at the same time are invariant across the input languages. The evaluation results show sizable improvements for our cross-language adversarial neural network (CLANN) model over a strong non-adversarial system.

## 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/1706.06749>)

[PDF](<https://arxiv.org/pdf/1706.06749.pdf>)