Translating Pronouns with Latent Anaphora Resolution
Translating Pronouns with Latent Anaphora Resolution
复制标题
使用潜在照应解析来翻译代词
DOI：
10.18653/v1/p17-1050
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Joakim Nivre
中科院分区：
文献类型：
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作者：
Christian Hardmeier;J. Tiedemann;Joakim Nivre
我们讨论了统计机器从英语翻译成法语的统计计算机中的图表的翻译。代词翻译需要在输入中解决代词的先例，这是一个经典的话语处理问题，通常通过从手动注释的数据中进行监督学习来解决。我们将跨语言代词预测作为分类任务进行了介绍，并提出了神经网络体系结构，该架构将阻碍器与潜在的先例之间的连接作为含量变量，从而使我们能够在平行文本上对分类器进行训练，而无需明确监督Anaphora Resololver。我们证明，我们的方法与使用外部核心分辨率相同，而在实用翻译实验中的影响更为有限。
We discuss the translation of anaphoric pronouns in statistical machine translation from English into French. Pronoun translation requires resolving the antecedents of the pronouns in the input, a classic discourse processing problem that is usually approached through supervised learning from manually annotated data. We cast cross-lingual pronoun prediction as a classification task and present a neural network architecture that incorporates the links between anaphors and potential antecedents as latent variables, allowing us to train the classifier on parallel text without explicit supervision for the anaphora resolver. We demonstrate that our approach works just as well for classification as using an external coreference resolver whereas its impact in a practical translation experiment is more limited.