Cross-language Learning with Adversarial Neural Networks: Application to Community Question Answering
This addresses the problem of porting question answering systems across languages with limited labeled data, which is incremental as it builds on existing adversarial methods for domain adaptation.
The paper tackles cross-language adaptation for question-question similarity reranking in community question answering by using adversarial neural networks to learn features that are both discriminative and language-invariant, resulting in sizable improvements over a strong non-adversarial baseline.
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.