Abstract
Linguists have been focused on a qualitative comparison of the semantics from different languages. Evaluation of the semantic interpretation among disparate language pairs like English and Tamil is an even more formidable task than for Slavic languages. The concept of word embedding in Natural Language Processing (NLP) has enabled a felicitous opportunity to quantify linguistic semantics. Multi-lingual tasks can be performed by projecting the word embeddings of one language onto the semantic space of the other. This research presents a suite of data-efficient deep learning approaches to deduce the transfer function from the embedding space of English to that of Tamil, deploying three popular embedding algorithms: Word2Vec, GloVe and FastText. A novel evaluation paradigm was devised for the generation of embeddings to assess their effectiveness, using the original embeddings as ground truths. Transferability across other target languages of the proposed model was assessed via pre-trained Word2Vec embeddings from Hindi and Chinese languages. We empirically prove that with a bilingual dictionary of a thousand words and a corresponding small monolingual target (Tamil) corpus, useful embeddings can be generated by transfer learning from a well-trained source (English) embedding. Furthermore, we demonstrate the usability of generated target embeddings in a few NLP use-case tasks, such as text summarization, part-of-speech (POS) tagging, and bilingual dictionary induction (BDI), bearing in mind that those are not the only possible applications.
Highlights
Introduction published maps and institutional affilThe mapping of a word to a representation of its meaning is termed semantic representation
The pre-trained encoder of Machine Translation–Long Short-Term Memory (MT-LSTM), Contextual word Vectors (CoVe), is applied across various downstream Natural Language Processing (NLP) tasks such as sentiment analysis and question classifier based on the transfer learning idea
Multi-Layer Perceptron (MLP), being fully connected, are unable to ignore noisy aspects of the data, whereas Convolutional Neural Networks (CNN) is ideally suited for disregarding noise and filtering in the aspects that are most prominent in the data
Summary
Cross-lingual embedding is accomplished by mapping the vectors from one language’s embedding space into that of the other language through a transfer function. Multiple experiments with various methodologies are carried out to obtain target word vectors for English–Tamil language pairs. The trained cross-lingual model, Transfer Function-based Generated. Pre-trained Hindi and Chinese embeddings (Word2Vec) were piped through the cross-lingual model on the target side to show the sharing property (transferability). The generated embeddings were further validated with real NLP tasks such as Text Summarisation, a multi-class model of the Part-Of-Speech Tagging and Bilingual Dictionary Induction (BDI) for low-resource languages featuring Tamil
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