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

arXiv: https://arxiv.org/abs/1706.06749 · HTML: https://mlsanity.com/paper/1706.06749/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2017.

## Built on by (most cited first, 6 of 6)

- [Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots](https://mlsanity.com/paper/2103.09593/) (2021)
- [PReGAN: Answer Oriented Passage Ranking with Weakly Supervised GAN](https://mlsanity.com/paper/2207.01762/) (2022)
- [Multilingual Multiword Expression Identification Using Lateral Inhibition and Domain Adaptation](https://mlsanity.com/paper/2306.10419/) (2023)
- [Interpreting Indirect Answers to Yes-No Questions in Multiple Languages](https://mlsanity.com/paper/2310.13290/) (2023)
- [DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue](https://mlsanity.com/paper/2212.08054/) (2022)
- [Cross-Language Speaker Attribute Prediction Using MIL and RL](https://mlsanity.com/paper/2601.04257/) (2026)

## Abstract (arXiv)

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.