Cross-Lingual Domain Adaptation for Dependency Parsing
Cross-Lingual Domain Adaptation for Dependency Parsing
复制标题
依存句法的跨语言域适应
DOI：
10.18653/v1/2020.tlt-1.6
复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
Sara Stymne
中科院分区：
文献类型：
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作者：
Sara Stymne
我们展示了如何通过将跨语言的树库组合成具有树库嵌入的解析器模型来使解析适应低资源领域。我们演示了如何利用来自其他语言的域内树库，并展示了当只有域外树库可用于目标语言时，这一点特别有用。通过使用相关语言的域外树库，该方法也扩展到低资源语言。提出了两种在测试时应用树库嵌入的无参数方法，当应用于Twitter数据和转录的语音时，这两种方法的结果与调谐方法相当。这为我们提供了一种选择树库并训练针对任何领域和语言组合的解析器的方法。
We show how we can adapt parsing to low-resource domains by combining treebanks across languages for a parser model with treebank embeddings. We demonstrate how we can take advantage of in-domain treebanks from other languages, and show that this is especially useful when only out-of-domain treebanks are available for the target language. The method is also extended to low-resource languages by using out-of-domain treebanks from related languages. Two parameter-free methods for applying treebank embeddings at test time are proposed, which give competitive results to tuned methods when applied to Twitter data and transcribed speech. This gives us a method for selecting treebanks and training a parser targeted at any combination of domain and language.