Cross-language Learning with Adversarial Neural Networks
| Autor: | Shafiq Joty, Preslav Nakov, Lluís Màrquez, Israa Jaradat |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
Computer science business.industry Competitive learning First language 05 social sciences 02 engineering and technology Language acquisition Adversarial system Discriminative model 0202 electrical engineering electronic engineering information engineering Question answering 020201 artificial intelligence & image processing Artificial intelligence 0509 other social sciences Invariant (mathematics) 050904 information & library sciences business |
| Zdroj: | CoNLL |
| DOI: | 10.18653/v1/k17-1024 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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