Improving neural machine translation for morphologically rich languages

Machine Translation aims to provide a seamless communication and interaction, thereby overcoming human language barriers. Recently, Neural Machine Translation (NMT) approaches have been very successful and achieve state-of-the-art performance in many language pairs. NMT systems consist of millions of neurons that are optimised to learn the input-output mapping between the source and the target languages. However, these systems produce poor translation quality under low-resource conditions and are unable to handle a large vocabulary particularly for languages with rich morphology such as Turkish, Tamil and German. In this project, we present a source vocabulary expansion technique to handle the problem of translating rare and unknown words by incorporating morphological information in the words. The effectiveness of the proposed technique is demonstrated by translating from two morphologically rich languages to English. Using this technique, we achieve a performance gain of approximately 2 BLEU points for both German → English and Turkish → English.Neural Machine Translation (NMT)language pairsneuronsinput-output mappingmorphology2 BLE

Bridging Language Gaps in Health Information Access: Konkani-English CLIR System for Medical Knowledge

This paper addresses the challenges posed by linguistic diversity in terms of medical information by introducing a Cross-Language Information Retrieval System attuned to the needs of Konkani language information seekers. The proposed system leverages Konkani queries entered by the user, translates them to English, and retrieves the documents using a thesaurus- based approach. Various strategies also have been considered to address the challenges posed by the source language – Konkani which is a minority language spoken in the Indian subcontinent. The proposed approach showcases the potential of combining language technology, information retrieval, and medical domain expertise to bridge linguistic barriers. As healthcare information remains a critical societal need, this work holds promise in facilitating equitable access to medical knowledge

Understanding and Enhancing the Use of Context for Machine Translation

To understand and infer meaning in language, neural models have to learn complicated nuances. Discovering distinctive linguistic phenomena from data is not an easy task. For instance, lexical ambiguity is a fundamental feature of language which is challenging to learn. Even more prominently, inferring the meaning of rare and unseen lexical units is difficult with neural networks. Meaning is often determined from context. With context, languages allow meaning to be conveyed even when the specific words used are not known by the reader. To model this learning process, a system has to learn from a few instances in context and be able to generalize well to unseen cases. The learning process is hindered when training data is scarce for a task. Even with sufficient data, learning patterns for the long tail of the lexical distribution is challenging. In this thesis, we focus on understanding certain potentials of contexts in neural models and design augmentation models to benefit from them. We focus on machine translation as an important instance of the more general language understanding problem. To translate from a source language to a target language, a neural model has to understand the meaning of constituents in the provided context and generate constituents with the same meanings in the target language. This task accentuates the value of capturing nuances of language and the necessity of generalization from few observations. The main problem we study in this thesis is what neural machine translation models learn from data and how we can devise more focused contexts to enhance this learning. Looking more in-depth into the role of context and the impact of data on learning models is essential to advance the NLP field. Moreover, it helps highlight the vulnerabilities of current neural networks and provides insights into designing more robust models.Comment: PhD dissertation defended on November 10th, 202