An Autoencoder Approach to Learning Bilingual Word Representations (1402.1454v1)

Abstract: Cross-language learning allows us to use training data from one language to build models for a different language. Many approaches to bilingual learning require that we have word-level alignment of sentences from parallel corpora. In this work we explore the use of autoencoder-based methods for cross-language learning of vectorial word representations that are aligned between two languages, while not relying on word-level alignments. We show that by simply learning to reconstruct the bag-of-words representations of aligned sentences, within and between languages, we can in fact learn high-quality representations and do without word alignments. Since training autoencoders on word observations presents certain computational issues, we propose and compare different variations adapted to this setting. We also propose an explicit correlation maximizing regularizer that leads to significant improvement in the performance. We empirically investigate the success of our approach on the problem of cross-language test classification, where a classifier trained on a given language (e.g., English) must learn to generalize to a different language (e.g., German). These experiments demonstrate that our approaches are competitive with the state-of-the-art, achieving up to 10-14 percentage point improvements over the best reported results on this task.

• The paper introduces a bilingual autoencoder that learns aligned word vectors from sentence-aligned corpora without relying on word-level alignments.
• It employs a cross-lingual correlation regularization technique, achieving up to 14 percentage points improvement in cross-language document classification.
• The approach simplifies multilingual NLP tasks and sets the stage for future extensions to handle more languages and complex textual representations.

An Autoencoder Approach to Learning Bilingual Word Representations

The paper "An Autoencoder Approach to Learning Bilingual Word Representations" investigates the utilization of autoencoder-based techniques for deriving aligned, bilingual word vectors without relying on word-level alignments within parallel corpora. This work addresses the growing necessity for NLP tools across languages with sparse labeled resources. The authors propose employing a novel bilingual autoencoder model that circumvents typical computational constraints associated with training on word observations, while also incorporating a cross-lingual correlation regularization term to enhance representation quality.

The paper is built on the premise that vectorial representations of text can significantly enhance NLP tasks by exploiting unlabeled data. Previous methodologies often leveraged word alignments to generate multilingual embeddings, but this paper pioneers a method that only requires sentence-aligned corpora, diverging from traditional reliance on tools like GIZA++. Instead, the proposed bilingual autoencoder concurrently reconstructs bag-of-words vectors of translated sentence pairs, encouraging the encoder representations to be informative of one another.

Three variations of the autoencoder model are evaluated: tree-based decoder training (BAE-tr), binary bag-of-words reconstruction (BAE-cr), and binary reconstruction with cross-lingual correlation regularization (BAE-cr/corr). The correlation regularization is particularly noteworthy, as it explicitly optimizes for alignment between bilingual embeddings, which is a critical factor in their model achieving up to 10–14 percentage point improvements over state-of-the-art methods in cross-language document classification tasks.

Empirically, the authors illustrate that these bilingual embeddings can successfully bridge languages, facilitating cross-language document classification where training occurs in one language, but classification is required in another. In experiments using English and German parallel corpora, the BAE-cr/corr model consistently outperformed both traditional Machine Translation (MT) based approaches and prior bilingual embedding methods that utilized word alignments. This is an important result as it demonstrates the viability of their approach in practical NLP applications.

From a theoretical perspective, this research contributes to the ongoing dialogue on the development of language-agnostic models in NLP. The absence of dependence on word-level alignments not only presents significant simplifications in model training but also extends the applicability of such models to other language pairs with limited parallel data resources.

Future developments stemming from this research could include expansions of the autoencoder framework to accommodate multilingual settings, learning representations across more than two languages simultaneously. Moreover, adaptations of the model to handle bags-of-n-grams instead of just bags-of-words could further enhance its utility in complex language tasks such as machine translation and sentiment analysis.

In conclusion, this work offers a robust autoencoder framework for bilingual word representation learning that sidesteps some conventional challenges in cross-lingual NLP endeavors, contributing valuable insights into creating scalable, language-independent NLP systems. The significant improvements in classification tasks underscore the potential of this approach to influence further research on multilingual representation learning.