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

1706.06749Jun 21, 2017CoNLL-2017: The SIGNLL Conference on Computational Natural Language Learning; cross-language adversarial neural network (CLANN) model; adversarial training; cross-language adaptation; community question answering; question-question similarity

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

Shafiq Joty, Preslav Nakov, Lluís Màrquez, Israa Jaradat

Computation and Language

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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.

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Researchers working in cs.CL and adjacent areas.

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The paper addresses: Cross-language Learning with Adversarial Neural Networks: Application to Community Question Answering.
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.

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Author comment: CoNLL-2017: The SIGNLL Conference on Computational Natural Language Learning; cross-language adversarial neural network (CLANN) model; adversarial training; cross-language adaptation; community question answering; question-question similarity