← Back to papers
2021 EMNLP EMNLP 2021

Neural Machine Translation for Tamil–Telugu Pair

Abstract

AbstractThe neural machine translation approach has gained popularity in machine translation because of its context analysing ability and its handling of long-term dependency issues. We have participated in the WMT21 shared task of similar language translation on a Tamil-Telugu pair with the team name: CNLP-NITS. In this task, we utilized monolingual data via pre-train word embeddings in transformer model based neural machine translation to tackle the limitation of parallel corpus. Our model has achieved a bilingual evaluation understudy (BLEU) score of 4.05, rank-based intuitive bilingual evaluation score (RIBES) score of 24.80 and translation edit rate (TER) score of 97.24 for both Tamil-to-Telugu and Telugu-to-Tamil translations respectively.

🌉 Interdisciplinary Bridge — Deep Learning and Natural Language Processing
🧭 Keyword Pioneer — pre-train word embedding
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Sahinur Rahman Laskar , Bishwaraj Paul , Prottay Kumar Adhikary , Partha Pakray , Sivaji Bandyopadhyay

Topics

Deep Learning > Architectures > Transformers Natural Language Processing > Applications > Machine Translation Natural Language Processing > Resources & Methods > Multilingual NLP Natural Language Processing > Generation > Machine Translation Deep Learning > Models > Transformers Deep Learning > Learning Types > Transfer Learning

Keywords

neural machine translation low-resource language word embedding long-term dependency monolingual datum transformer model pre-trained word embedding pre-train word embedding
Download PDF

Related papers

Continual Learning in Multilingual NMT via Language-Specific Embeddings 2021
MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents 2021
Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity 2021
Neural Machine Translation with Heterogeneous Topic Knowledge Embeddings 2021
Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis 2021