Adversarial Training for Cross-Domain Universal Dependency Parsing

2017-08-01 · CONLL 2017 8 · Motoki Sato, Hitoshi Manabe, Hiroshi Noji, Yuji Matsumoto

We describe our submission to the CoNLL 2017 shared task, which exploits the shared common knowledge of a language across different domains via a domain adaptation technique. Our approach is an extension to the recently proposed adversarial training technique for domain adaptation, which we apply on top of a graph-based neural dependency parsing model on bidirectional LSTMs. In our experiments, we find our baseline graph-based parser already outperforms the official baseline model (UDPipe) by a large margin. Further, by applying our technique to the treebanks of the same language with different domains, we observe an additional gain in the performance, in particular for the domains with less training data.

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Dependency Parsing Domain Adaptation

APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing

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Dependency Parsing Sentence

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Cross-Lingual POS Tagging Cross-Lingual Transfer Dependency Parsing POS+1

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