paper-with-me

홈 › Papers

Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021

2021-08-19 · MTSummit 2021 8 · Karthik Puranik, Adeep Hande, Ruba Priyadharshini, Thenmozhi Durairaj, Anbukkarasi Sampath, Kingston Pal Thamburaj, Bharathi Raja Chakravarthi

This paper reports the Machine Translation (MT) systems submitted by the IIITT team for the English->Marathi and English->Irish language pairs LoResMT 2021 shared task. The task focuses on getting exceptional translations for rather low-resourced languages like Irish and Marathi. We fine-tune IndicTrans, a pretrained multilingual NMT model for English->Marathi, using external parallel corpus as input for additional training. We have used a pretrained Helsinki-NLP Opus MT English->Irish model for the latter language pair. Our approaches yield relatively promising results on the BLEU metrics. Under the team name IIITT, our systems ranked 1, 1, and 2 in English->Marathi, Irish->English, and English->Irish, respectively.

📄 PDF Abstract BibTeX arXiv:2108.08556

Code (1)

karthikpuranik11/loresmt 공식 구현

Tasks

Machine TranslationNMTTranslation

Similar Papers 제목 키워드 기반

Comparing Approaches to Automatic Summarization in Less-Resourced Languages

2025-12-30 · Chester Palen-Michel, Constantine Lignos arxiv

Automatic text summarization has achieved high performance in high-resourced languages like English, but comparatively less attention has been given to summarization in less-resourced languages. This work compares a vari…

Text SummarizationData Augmentation

Performance of Recent Large Language Models for a Low-Resourced Language

2024-07-31 · Ravindu Jayakody, Gihan Dias

Large Language Models (LLMs) have shown significant advances in the past year. In addition to new versions of GPT and Llama, several other LLMs have been introduced recently. Some of these are open models available for d…

MURAL: Multimodal, Multitask Representations Across Languages

2021-11-01 · Findings (EMNLP) 2021 11 · Aashi Jain, Mandy Guo, Krishna Srinivasan, Ting Chen 외

Both image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages. We use both types of pairs in MURAL (MUltimodal, MUltitask Representations Across Langu…

Cross-Modal RetrievalImage-text matchingRetrievalText Matching+1

MURAL: Multimodal, Multitask Retrieval Across Languages

2021-09-10 · Aashi Jain, Mandy Guo, Krishna Srinivasan, Ting Chen 외

Both image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages. We use both types of pairs in MURAL (MUltimodal, MUltitask Representations Across Langu…

Cross-Modal RetrievalImage-text matchingRetrievalSemantic Image Similarity+4

Multilingual Neural Machine Translation for Zero-Resource Languages

2019-09-16 · Surafel M. Lakew, Marcello Federico, Matteo Negri, Marco Turchi

In recent years, Neural Machine Translation (NMT) has been shown to be more effective than phrase-based statistical methods, thus quickly becoming the state of the art in machine translation (MT). However, NMT systems ar…

Machine TranslationNMTTranslation