92 research outputs found
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
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