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Register a new userEvaluating the Performance of Back-translation for Low Resource English-Marathi Language Pair: CFILT-IITBombay @ LoResMT 2021
تقييم أداء الترجمة الخلفي لزوج LOWN-ROUNDUNTION English-Marathi زوج: CFILT-IITBOBBAY @ Loresmt 2021
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في هذه الورقة، نناقش تفاصيل أنظمة الترجمة المختلفة (MT) التي قدمناها لمهمة Loresmt الإنجليزية Marathi.كجزء من هذه المهمة، قدمنا ثلاثة أنظمة ترجمة آلية عصبية مختلفة (NMT)؛نظام أساسي في اللغة الإنجليزية - المراثي، نظام ماريثي-إنجليزي خط الأساس، ونظام إنجليزي - مراثي يعتمد على تقنية الترجمة الخلفي.نستكشف أداء أنظمة NMT هذه بين لغات اللغة الإنجليزية والمراثي، والتي تشكل زوج لغة موارد منخفضة بسبب عدم توفر بيانات متوازية كافية.نستكشف أيضا أداء تقنية الترجمة الخلفي عندما يتم الحصول على البيانات المترجمة الخلفي من أنظمة NMT التي يتم تدريبها على كمية أقل من البيانات.من تجاربنا، نلاحظ أن تقنية الترجمة الخلفي يمكن أن تساعد في تحسين جودة MT على خط الأساس لزوج اللغة الإنجليزية المهاراتية.
In this paper, we discuss the details of the various Machine Translation (MT) systems that we have submitted for the English-Marathi LoResMT task. As a part of this task, we have submitted three different Neural Machine Translation (NMT) systems; a Baseline English-Marathi system, a Baseline Marathi-English system, and an English-Marathi system that is based on the back-translation technique. We explore the performance of these NMT systems between English and Marathi languages, which forms a low resource language pair due to unavailability of sufficient parallel data. We also explore the performance of the back-translation technique when the back-translated data is obtained from NMT systems that are trained on a very less amount of data. From our experiments, we observe that the back-translation technique can help improve the MT quality over the baseline for the English-Marathi language pair.
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In this paper, we (team - oneNLP-IIITH) describe our Neural Machine Translation approaches for English-Marathi (both direction) for LoResMT-20211 . We experimented with transformer based Neural Machine Translation and explored the use of different li
nguistic features like POS and Morph on subword unit for both English-Marathi and Marathi-English. In addition, we have also explored forward and backward translation using web-crawled monolingual data. We obtained 22.2 (overall 2 nd) and 31.3 (overall 1 st) BLEU scores for English-Marathi and Marathi-English on respectively
In this paper and we explore different techniques of overcoming the challenges of low-resource in Neural Machine Translation (NMT) and specifically focusing on the case of English-Marathi NMT. NMT systems require a large amount of parallel corpora to
obtain good quality translations. We try to mitigate the low-resource problem by augmenting parallel corpora or by using transfer learning. Techniques such as Phrase Table Injection (PTI) and back-translation and mixing of language corpora are used for enhancing the parallel data; whereas pivoting and multilingual embeddings are used to leverage transfer learning. For pivoting and Hindi comes in as assisting language for English-Marathi translation. Compared to baseline transformer model and a significant improvement trend in BLEU score is observed across various techniques. We have done extensive manual and automatic and qualitative evaluation of our systems. Since the trend in Machine Translation (MT) today is post-editing and measuring of Human Effort Reduction (HER) and we have given our preliminary observations on Translation Edit Rate (TER) vs. BLEU score study and where TER is regarded as a measure of HER.
We present the findings of the LoResMT 2021 shared task which focuses on machine translation (MT) of COVID-19 data for both low-resource spoken and sign languages. The organization of this task was conducted as part of the fourth workshop on technolo
gies for machine translation of low resource languages (LoResMT). Parallel corpora is presented and publicly available which includes the following directions: English↔Irish, English↔Marathi, and Taiwanese Sign language↔Traditional Chinese. Training data consists of 8112, 20933 and 128608 segments, respectively. There are additional monolingual data sets for Marathi and English that consist of 21901 segments. The results presented here are based on entries from a total of eight teams. Three teams submitted systems for English↔Irish while five teams submitted systems for English↔Marathi. Unfortunately, there were no systems submissions for the Taiwanese Sign language↔Traditional Chinese task. Maximum system performance was computed using BLEU and follow as 36.0 for English--Irish, 34.6 for Irish--English, 24.2 for English--Marathi, and 31.3 for Marathi--English.
Translation models for the specific domain of translating Covid data from English to Irish were developed for the LoResMT 2021 shared task. Domain adaptation techniques, using a Covid-adapted generic 55k corpus from the Directorate General of Transla
tion, were applied. Fine-tuning, mixed fine-tuning and combined dataset approaches were compared with models trained on an extended in-domain dataset. As part of this study, an English-Irish dataset of Covid related data, from the Health and Education domains, was developed. The highestperforming model used a Transformer architecture trained with an extended in-domain Covid dataset. In the context of this study, we have demonstrated that extending an 8k in-domain baseline dataset by just 5k lines improved the BLEU score by 27 points.
We present the University of Central Florida systems for the LoResMT 2021 Shared Task, participating in the English-Irish and English-Marathi translation pairs. We focused our efforts on constrained track of the task, using transfer learning and subw
ord segmentation to enhance our models given small amounts of training data. Our models achieved the highest BLEU scores on the fully constrained tracks of English-Irish, Irish-English, and Marathi-English with scores of 13.5, 21.3, and 17.9 respectively
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