This paper proposes a modularized sense induction and representation learning
model that jointly learns bilingual sense embeddings that align well in the
vector space, where the cross-lingual signal in the English-Chinese parallel
corpus is exploited to capture the collocation and distributed characteristics
in the language pair. The model is evaluated on the Stanford Contextual Word
Similarity (SCWS) dataset to ensure the quality of monolingual sense
embeddings. In addition, we introduce Bilingual Contextual Word Similarity
(BCWS), a large and high-quality dataset for evaluating cross-lingual sense
embeddings, which is the first attempt of measuring whether the learned
embeddings are indeed aligned well in the vector space. The proposed approach
shows the superior quality of sense embeddings evaluated in both monolingual
and bilingual spaces.

本文提出了一个模块化的感知诱导和表示学习模型，联合学习双语感知嵌入，在向量空间中很好地对齐，利用英汉平行语料库中的跨语言信号捕捉语言对中的词汇搭配和分布特征。通过在 Stanford 上下文单词相似性（SCWS）数据集上进行评估，确保单语感知嵌入的质量。此外，我们还介绍了双语上下文单词相似性（BCWS），这是评估跨语言感知嵌入的大型高质量数据集，是衡量学习的嵌入是否在向量空间中确实对齐的第一次尝试。所提出方法展示了在单语和双语空间中评估感知嵌入的优越质量。