Word Alignment Integration in Cross-Lingual Sentence Embeddings for XNLI Zero-Shot Transfer

Abstract

Abstract: We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus (e.g. a few hundred sentence pairs). Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence. Through sharing model parameters among different languages, our model jointly trains the word embeddings in a common cross-lingual space. We also propose to combine word and subword embeddings to make use of orthographic similarities across different languages. We base our experiments on real-world data from

Research Question

How does the integration of word alignment techniques impact the performance of cross-lingual sentence embeddings on the XNLI benchmark when evaluated through zero-shot transfer accuracy in low-resource languages compared to high-resource languages?

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