Integrating Knowledge Encoded by Linguistic Phenomena of Indian Languages with Neural Machine Translation

This work presents an in-depth analysis of machine translations of morphologically-rich Indo-Aryan and Dravidian languages under zero-resource conditions. It focuses on Zero-Shot Systems for these languages and leverages transfer-learning by exploiting target-side monolingual corpora and parallel translations from other languages. These systems are compared with direct translations using the BLEU and TER metrics. Further, Zero-Shot Systems are used as pre-trained models for fine-tuning with real human-generated data taken in different proportions that range from 100 sentences to the entire training set. Performances of the Indo-Aryan and Dravidian languages are compared with a focus on their morphological complexity. The systems with a Dravidian source language performed much better and reached very near to the level of direct translations. This is observed likely due to morphological richness and complexity in the language, which in turn provided more room for transfer-learning in this case. A comparative analysis based on language families has been done. These systems were fine-tuned further, which in turn outperformed direct translations with just 500 parallel sentences for a Dravidian source language. However, systems with an Indo-Aryan source language showed similar performance after getting fine-tuned with 10,000 sentences.

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Enhancing Neural Machine Translation Quality for Kannada–Tulu Language Pairs through Transformer Architecture: A Linguistic Feature Integration

Supriya,

Acharya,

Nayak

2024

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The rise of intelligent systems demands good machine translation models that are less data hungry and more efficient, especially for low- and extremely-low-resource languages with few or no data available. By integrating a linguistic feature to enhance the quality of translation, we have developed a generic Neural Machine Translation (NMT) model for Kannada–Tulu language pairs. The NMT model uses Transformer architecture and a state-of-the-art model for translating text from Kannada to Tulu and learns based on the parallel data. Kannada and Tulu are both low-resource Dravidian languages, with Tulu recognised as an extremely-low-resource language. Dravidian languages are morphologically rich and are highly agglutinative in nature and there exist only a few NMT models for Kannada–Tulu language pairs. They exhibit poor translation scores as they fail to capture the linguistic features of the language. The proposed generic approach can benefit other low-resource Indic languages that have smaller parallel corpora for NMT tasks. Evaluation metrics like Bilingual Evaluation Understudy (BLEU), character-level F-score (chrF) and Word Error Rate (WER) are considered to obtain the improved translation scores for the linguistic-feature-embedded NMT model. These results hold promise for further experimentation with other low- and extremely-low-resource language pairs.

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Integrating Knowledge Encoded by Linguistic Phenomena of Indian Languages with Neural Machine Translation

Cited by 3 publications

References 11 publications

Relation Extraction in Knowledge Base Question Answering: From General-Domain to the Catering Industry

Relation Extraction in Knowledge Base Question Answering: From General-Domain to the Catering Industry

Translating Morphologically Rich Indian Languages under Zero-Resource Conditions

Enhancing Neural Machine Translation Quality for Kannada–Tulu Language Pairs through Transformer Architecture: A Linguistic Feature Integration

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