Proceedings of the 17th International Conference on Natural Language Processing (ICON): Adap-MT 2020 Shared Task

Dipti Misra Sharma, Asif Ekbal, Karunesh Arora, Sudip Kumar Naskar, Dipankar Ganguly, Sobha L, Radhika Mamidi, Sunita Arora, Pruthwik Mishra, Vandan Mujadia (Editors)

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Patna, India
NLP Association of India (NLPAI)
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Proceedings of the 17th International Conference on Natural Language Processing (ICON): Adap-MT 2020 Shared Task
Dipti Misra Sharma | Asif Ekbal | Karunesh Arora | Sudip Kumar Naskar | Dipankar Ganguly | Sobha L | Radhika Mamidi | Sunita Arora | Pruthwik Mishra | Vandan Mujadia

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JUNLP@ICON2020: Low Resourced Machine Translation for Indic Languages
Sainik Mahata | Dipankar Das | Sivaji Bandyopadhyay

In the current work, we present the description of the systems submitted to a machine translation shared task organized by ICON 2020: 17th International Conference on Natural Language Processing. The systems were developed to show the capability of general domain machine translation when translating into Indic languages, English-Hindi, in our case. The paper shows the training process and quantifies the performance of two state-of-the-art translation systems, viz., Statistical Machine Translation and Neural Machine Translation. While Statistical Machine Translation systems work better in a low-resource setting, Neural Machine Translation systems are able to generate sentences that are fluent in nature. Since both these systems have contrasting advantages, a hybrid system, incorporating both, was also developed to leverage all the strong points. The submitted systems garnered BLEU scores of 8.701943312, 0.6361336198, and 11.78873307 respectively and the scores of the hybrid system helped us to the fourth spot in the competition leaderboard.

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AdapNMT : Neural Machine Translation with Technical Domain Adaptation for Indic Languages
Hema Ala | Dipti Sharma

Adapting new domain is highly challenging task for Neural Machine Translation (NMT). In this paper we show the capability of general domain machine translation when translating into Indic languages (English - Hindi , English - Telugu and Hindi - Telugu), and low resource domain adaptation of MT systems using existing general parallel data and small in domain parallel data for AI and Chemistry Domains. We carried out our experiments using Byte Pair Encoding(BPE) as it solves rare word problems. It has been observed that with addition of little amount of in-domain data to the general data improves the BLEU score significantly.

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Domain Adaptation of NMT models for English-Hindi Machine Translation Task : AdapMT Shared Task ICON 2020
Ramchandra Joshi | Rusbabh Karnavat | Kaustubh Jirapure | Raviraj Joshi

Recent advancements in Neural Machine Translation (NMT) models have proved to produce a state of the art results on machine translation for low resource Indian languages. This paper describes the neural machine translation systems for the English-Hindi language presented in AdapMT Shared Task ICON 2020. The shared task aims to build a translation system for Indian languages in specific domains like Artificial Intelligence (AI) and Chemistry using a small in-domain parallel corpus. We evaluated the effectiveness of two popular NMT models i.e, LSTM, and Transformer architectures for the English-Hindi machine translation task based on BLEU scores. We train these models primarily using the out of domain data and employ simple domain adaptation techniques based on the characteristics of the in-domain dataset. The fine-tuning and mixed-domain data approaches are used for domain adaptation. The system achieved the second-highest score on chemistry and general domain En-Hi translation task and the third-highest score on the AI domain En-Hi translation task.

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Terminology-Aware Sentence Mining for NMT Domain Adaptation: ADAPT’s Submission to the Adap-MT 2020 English-to-Hindi AI Translation Shared Task
Rejwanul Haque | Yasmin Moslem | Andy Way

This paper describes the ADAPT Centre’s submission to the Adap-MT 2020 AI Translation Shared Task for English-to-Hindi. The neural machine translation (NMT) systems that we built to translate AI domain texts are state-of-the-art Transformer models. In order to improve the translation quality of our NMT systems, we made use of both in-domain and out-of-domain data for training and employed different fine-tuning techniques for adapting our NMT systems to this task, e.g. mixed fine-tuning and on-the-fly self-training. For this, we mined parallel sentence pairs and monolingual sentences from large out-of-domain data, and the mining process was facilitated through automatic extraction of terminology from the in-domain data. This paper outlines the experiments we carried out for this task and reports the performance of our NMT systems on the evaluation test set.

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MUCS@Adap-MT 2020: Low Resource Domain Adaptation for Indic Machine Translation
Asha Hegde | H.l. Shashirekha

Machine Translation (MT) is the task of automatically converting the text in source language to text in target language by preserving the meaning. MT usually require large corpus for training the translation models. Due to scarcity of resources very less attention is given to translating into low resource languages and in particular into Indic languages. In this direction, a shared task called “Adap-MT 2020: Low Resource Domain Adaptation for Indic Machine Translation” is organized to illustrate the capability of general domain MT when translating into Indic languages and low resource domain adaptation of MT systems. In this paper, we, team MUCS, describe a simple word extraction based domain adaptation approach applied to English-Hindi MT only. MT in the proposed model is carried out using Open-NMT - a popular Neural Machine Translation tool. A general domain corpus is built effectively combining the available English-Hindi corpora and removing the duplicate sentences. Further, domain specific corpus is updated by extracting the sentences from generic corpus that contains the words given in the domain specific corpus. The proposed model exhibited satisfactory results for small domain specific AI and CHE corpora provided by the organizers in terms of BLEU score with 1.25 and 2.72 respectively. Further, this methodology is quite generic and can easily be extended to other low resource language pairs as well.