We introduce a novel discriminative latent-variable model for the task of
bilingual lexicon induction. Our model combines the bipartite matching
dictionary prior of Haghighi et al. (2008) with a state-of-the-art
embedding-based approach. To train the model, we derive an efficient Viterbi EM
algorithm. We provide empirical improvements on six language pairs under two
metrics and show that the prior theoretically and empirically helps to mitigate
the hubness problem. We also demonstrate how previous work may be viewed as a
similarly fashioned latent-variable model, albeit with a different prior.

我们提出了一种新的判别式潜变量模型，用于双语词典归纳任务，该模型将 Haghighi 等人的二分匹配字典先验与最先进的基于嵌入的方法相结合。我们提供了在两个度量标准下六个语言对上的经验改进，并表明先前的工作可以被视为类似配置的潜变量模型，尽管具有不同的先验。

双语词表归纳的判别式潜变量模型

A Discriminative Latent-Variable Model for Bilingual Lexicon Induction

We introduce an agreement-based approach to learning parallel lexicons and
phrases from non-parallel corpora. The basic idea is to encourage two
asymmetric latent-variable translation models (i.e., source-to-target and
target-to-source) to agree on identifying latent phrase and word alignments.
The agreement is defined at both word and phrase levels. We develop a Viterbi
EM algorithm for jointly training the two unidirectional models efficiently.
Experiments on the Chinese-English dataset show that agreement-based learning
significantly improves both alignment and translation performance.

我们介绍了一种基于协议的方法来学习非平行语料库中的平行词汇和短语。我们通过鼓励两个不对称的隐变量翻译模型（即源至目标和目标至源）达成一致，同时在单向模型的词和短语级别定义一致性。我们开发了一个 Viterbi EM 算法来高效地联合训练这两个单向模型。在汉英数据集上的实验表明，基于协议的学习显著提高了对齐和翻译性能。