论文标题
学习对半监督单词翻译的对齐嵌入使用最大平均差异
Learning aligned embeddings for semi-supervised word translation using Maximum Mean Discrepancy
论文作者
论文摘要
单词翻译是语言翻译不可或缺的一部分。在机器翻译中，每种语言都被视为具有自己单词嵌入的域。单词嵌入之间的对齐允许在多语言上下文中链接语义上等效的单词。此外，它提供了一种在没有直接翻译的情况下推断单词的跨语性含义的方法。当前的单词嵌入对齐方式的方法要么受到监督，即它们需要已知的单词对，要么以无监督的方式学习固定嵌入的跨域转换。在这里，我们提出了一种端到端的方法，用于单词嵌入对齐，不需要已知的单词对。我们的方法通过MMD（WAM）称为单词对齐方式，学习了在嵌入式之间使用局部最大平均差异（MMD）约束在句子翻译训练期间对齐的嵌入。我们表明，我们的方法不仅超过了无监督的方法，还可以监督对已知单词翻译进行训练的方法。
Word translation is an integral part of language translation. In machine translation, each language is considered a domain with its own word embedding. The alignment between word embeddings allows linking semantically equivalent words in multilingual contexts. Moreover, it offers a way to infer cross-lingual meaning for words without a direct translation. Current methods for word embedding alignment are either supervised, i.e. they require known word pairs, or learn a cross-domain transformation on fixed embeddings in an unsupervised way. Here we propose an end-to-end approach for word embedding alignment that does not require known word pairs. Our method, termed Word Alignment through MMD (WAM), learns embeddings that are aligned during sentence translation training using a localized Maximum Mean Discrepancy (MMD) constraint between the embeddings. We show that our method not only out-performs unsupervised methods, but also supervised methods that train on known word translations.
