Cross-language Learning with Adversarial Neural Networks

Shafiq Joty, Preslav Nakov, Lluís Màrquez, Israa Jaradat


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.
Anthology ID:
K17-1024
Volume:
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
226–237
Language:
URL:
https://www.aclweb.org/anthology/K17-1024
DOI:
10.18653/v1/K17-1024
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PDF:
http://aclanthology.lst.uni-saarland.de/K17-1024.pdf