Shantipriya Parida


2021

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Open Machine Translation for Low Resource South American Languages (AmericasNLP 2021 Shared Task Contribution)
Shantipriya Parida | Subhadarshi Panda | Amulya Dash | Esau Villatoro-Tello | A. Seza Doğruöz | Rosa M. Ortega-Mendoza | Amadeo Hernández | Yashvardhan Sharma | Petr Motlicek
Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

This paper describes the team (“Tamalli”)’s submission to AmericasNLP2021 shared task on Open Machine Translation for low resource South American languages. Our goal was to evaluate different Machine Translation (MT) techniques, statistical and neural-based, under several configuration settings. We obtained the second-best results for the language pairs “Spanish-Bribri”, “Spanish-Asháninka”, and “Spanish-Rarámuri” in the category “Development set not used for training”. Our performed experiments will serve as a point of reference for researchers working on MT with low-resource languages.

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Proceedings of the 8th Workshop on Asian Translation (WAT2021)
Toshiaki Nakazawa | Hideki Nakayama | Isao Goto | Hideya Mino | Chenchen Ding | Raj Dabre | Anoop Kunchukuttan | Shohei Higashiyama | Hiroshi Manabe | Win Pa Pa | Shantipriya Parida | Ondřej Bojar | Chenhui Chu | Akiko Eriguchi | Kaori Abe | Yusuke Oda | Katsuhito Sudoh | Sadao Kurohashi | Pushpak Bhattacharyya
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

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Overview of the 8th Workshop on Asian Translation
Toshiaki Nakazawa | Hideki Nakayama | Chenchen Ding | Raj Dabre | Shohei Higashiyama | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Shantipriya Parida | Ondřej Bojar | Chenhui Chu | Akiko Eriguchi | Kaori Abe | Yusuke Oda | Sadao Kurohashi
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

This paper presents the results of the shared tasks from the 8th workshop on Asian translation (WAT2021). For the WAT2021, 28 teams participated in the shared tasks and 24 teams submitted their translation results for the human evaluation. We also accepted 5 research papers. About 2,100 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

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NLPHut’s Participation at WAT2021
Shantipriya Parida | Subhadarshi Panda | Ketan Kotwal | Amulya Ratna Dash | Satya Ranjan Dash | Yashvardhan Sharma | Petr Motlicek | Ondřej Bojar
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

This paper provides the description of shared tasks to the WAT 2021 by our team “NLPHut”. We have participated in the English→Hindi Multimodal translation task, English→Malayalam Multimodal translation task, and Indic Multi-lingual translation task. We have used the state-of-the-art Transformer model with language tags in different settings for the translation task and proposed a novel “region-specific” caption generation approach using a combination of image CNN and LSTM for the Hindi and Malayalam image captioning. Our submission tops in English→Malayalam Multimodal translation task (text-only translation, and Malayalam caption), and ranks second-best in English→Hindi Multimodal translation task (text-only translation, and Hindi caption). Our submissions have also performed well in the Indic Multilingual translation tasks.

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Multimodal Neural Machine Translation System for English to Bengali
Shantipriya Parida | Subhadarshi Panda | Satya Prakash Biswal | Ketan Kotwal | Arghyadeep Sen | Satya Ranjan Dash | Petr Motlicek
Proceedings of the First Workshop on Multimodal Machine Translation for Low Resource Languages (MMTLRL 2021)

Multimodal Machine Translation (MMT) systems utilize additional information from other modalities beyond text to improve the quality of machine translation (MT). The additional modality is typically in the form of images. Despite proven advantages, it is indeed difficult to develop an MMT system for various languages primarily due to the lack of a suitable multimodal dataset. In this work, we develop an MMT for English-> Bengali using a recently published Bengali Visual Genome (BVG) dataset that contains images with associated bilingual textual descriptions. Through a comparative study of the developed MMT system vis-a-vis a Text-to-text translation, we demonstrate that the use of multimodal data not only improves the translation performance improvement in BLEU score of +1.3 on the development set, +3.9 on the evaluation test, and +0.9 on the challenge test set but also helps to resolve ambiguities in the pure text description. As per best of our knowledge, our English-Bengali MMT system is the first attempt in this direction, and thus, can act as a baseline for the subsequent research in MMT for low resource languages.

2020

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OdiEnCorp 2.0: Odia-English Parallel Corpus for Machine Translation
Shantipriya Parida | Satya Ranjan Dash | Ondřej Bojar | Petr Motlicek | Priyanka Pattnaik | Debasish Kumar Mallick
Proceedings of the WILDRE5– 5th Workshop on Indian Language Data: Resources and Evaluation

The preparation of parallel corpora is a challenging task, particularly for languages that suffer from under-representation in the digital world. In a multi-lingual country like India, the need for such parallel corpora is stringent for several low-resource languages. In this work, we provide an extended English-Odia parallel corpus, OdiEnCorp 2.0, aiming particularly at Neural Machine Translation (NMT) systems which will help translate English↔Odia. OdiEnCorp 2.0 includes existing English-Odia corpora and we extended the collection by several other methods of data acquisition: parallel data scraping from many websites, including Odia Wikipedia, but also optical character recognition (OCR) to extract parallel data from scanned images. Our OCR-based data extraction approach for building a parallel corpus is suitable for other low resource languages that lack in online content. The resulting OdiEnCorp 2.0 contains 98,302 sentences and 1.69 million English and 1.47 million Odia tokens. To the best of our knowledge, OdiEnCorp 2.0 is the largest Odia-English parallel corpus covering different domains and available freely for non-commercial and research purposes.

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Proceedings of the 7th Workshop on Asian Translation
Toshiaki Nakazawa | Hideki Nakayama | Chenchen Ding | Raj Dabre | Anoop Kunchukuttan | Win Pa Pa | Ondřej Bojar | Shantipriya Parida | Isao Goto | Hidaya Mino | Hiroshi Manabe | Katsuhito Sudoh | Sadao Kurohashi | Pushpak Bhattacharyya
Proceedings of the 7th Workshop on Asian Translation

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Overview of the 7th Workshop on Asian Translation
Toshiaki Nakazawa | Hideki Nakayama | Chenchen Ding | Raj Dabre | Shohei Higashiyama | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Shantipriya Parida | Ondřej Bojar | Sadao Kurohashi
Proceedings of the 7th Workshop on Asian Translation

This paper presents the results of the shared tasks from the 7th workshop on Asian translation (WAT2020). For the WAT2020, 20 teams participated in the shared tasks and 14 teams submitted their translation results for the human evaluation. We also received 12 research paper submissions out of which 7 were accepted. About 500 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

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ODIANLP’s Participation in WAT2020
Shantipriya Parida | Petr Motlicek | Amulya Ratna Dash | Satya Ranjan Dash | Debasish Kumar Mallick | Satya Prakash Biswal | Priyanka Pattnaik | Biranchi Narayan Nayak | Ondřej Bojar
Proceedings of the 7th Workshop on Asian Translation

This paper describes the ODIANLP submission to WAT 2020. We have participated in the English-Hindi Multimodal task and Indic task. We have used the state-of-the-art Transformer model for the translation task and InceptionResNetV2 for the Hindi Image Captioning task. Our submission tops in English->Hindi Multimodal task in its track and Odia<->English translation tasks. Also, our submissions performed well in the Indic Multilingual tasks.

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BertAA : BERT fine-tuning for Authorship Attribution
Maël Fabien | Esau Villatoro-Tello | Petr Motlicek | Shantipriya Parida
Proceedings of the 17th International Conference on Natural Language Processing (ICON)

Identifying the author of a given text can be useful in historical literature, plagiarism detection, or police investigations. Authorship Attribution (AA) has been well studied and mostly relies on a large feature engineering work. More recently, deep learning-based approaches have been explored for Authorship Attribution (AA). In this paper, we introduce BertAA, a fine-tuning of a pre-trained BERT language model with an additional dense layer and a softmax activation to perform authorship classification. This approach reaches competitive performances on Enron Email, Blog Authorship, and IMDb (and IMDb62) datasets, up to 5.3% (relative) above current state-of-the-art approaches. We performed an exhaustive analysis allowing to identify the strengths and weaknesses of the proposed method. In addition, we evaluate the impact of including additional features (e.g. stylometric and hybrid features) in an ensemble approach, improving the macro-averaged F1-Score by 2.7% (relative) on average.

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Detection of Similar Languages and Dialects Using Deep Supervised Autoencoder
Shantipriya Parida | Esau Villatoro-Tello | Sajit Kumar | Maël Fabien | Petr Motlicek
Proceedings of the 17th International Conference on Natural Language Processing (ICON)

Language detection is considered a difficult task especially for similar languages, varieties, and dialects. With the growing number of online content in different languages, the need for reliable and robust language detection tools also increased. In this work, we use supervised autoencoders with a bayesian optimizer for language detection and highlights its efficiency in detecting similar languages with dialect variance in comparison to other state-of-the-art techniques. We evaluated our approach on multiple datasets (Ling10, Discriminating between Similar Language (DSL), and Indo-Aryan Language Identification (ILI)). Obtained results demonstrate that SAE are higly effective in detecting languages, up to a 100% accuracy in the Ling10. Similarly, we obtain a competitive performance in identifying similar languages, and dialects, 92% and 85% for DSL ans ILI datasets respectively.

2019

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Abstract Text Summarization: A Low Resource Challenge
Shantipriya Parida | Petr Motlicek
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Text summarization is considered as a challenging task in the NLP community. The availability of datasets for the task of multilingual text summarization is rare, and such datasets are difficult to construct. In this work, we build an abstract text summarizer for the German language text using the state-of-the-art “Transformer” model. We propose an iterative data augmentation approach which uses synthetic data along with the real summarization data for the German language. To generate synthetic data, the Common Crawl (German) dataset is exploited, which covers different domains. The synthetic data is effective for the low resource condition and is particularly helpful for our multilingual scenario where availability of summarizing data is still a challenging issue. The data are also useful in deep learning scenarios where the neural models require a large amount of training data for utilization of its capacity. The obtained summarization performance is measured in terms of ROUGE and BLEU score. We achieve an absolute improvement of +1.5 and +16.0 in ROUGE1 F1 (R1_F1) on the development and test sets, respectively, compared to the system which does not rely on data augmentation.

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Proceedings of the 6th Workshop on Asian Translation
Toshiaki Nakazawa | Chenchen Ding | Raj Dabre | Anoop Kunchukuttan | Nobushige Doi | Yusuke Oda | Ondřej Bojar | Shantipriya Parida | Isao Goto | Hidaya Mino
Proceedings of the 6th Workshop on Asian Translation

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Overview of the 6th Workshop on Asian Translation
Toshiaki Nakazawa | Nobushige Doi | Shohei Higashiyama | Chenchen Ding | Raj Dabre | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Yusuke Oda | Shantipriya Parida | Ondřej Bojar | Sadao Kurohashi
Proceedings of the 6th Workshop on Asian Translation

This paper presents the results of the shared tasks from the 6th workshop on Asian translation (WAT2019) including Ja↔En, Ja↔Zh scientific paper translation subtasks, Ja↔En, Ja↔Ko, Ja↔En patent translation subtasks, Hi↔En, My↔En, Km↔En, Ta↔En mixed domain subtasks and Ru↔Ja news commentary translation task. For the WAT2019, 25 teams participated in the shared tasks. We also received 10 research paper submissions out of which 61 were accepted. About 400 translation results were submitted to the automatic evaluation server, and selected submis- sions were manually evaluated.

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Idiap NMT System for WAT 2019 Multimodal Translation Task
Shantipriya Parida | Ondřej Bojar | Petr Motlicek
Proceedings of the 6th Workshop on Asian Translation

This paper describes the Idiap submission to WAT 2019 for the English-Hindi Multi-Modal Translation Task. We have used the state-of-the-art Transformer model and utilized the IITB English-Hindi parallel corpus as an additional data source. Among the different tracks of the multi-modal task, we have participated in the “Text-Only” track for the evaluation and challenge test sets. Our submission tops in its track among the competitors in terms of both automatic and manual evaluation. Based on automatic scores, our text-only submission also outperforms systems that consider visual information in the “multi-modal translation” task.

2018

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CUNI NMT System for WAT 2018 Translation Tasks
Tom Kocmi | Shantipriya Parida | Ond?ej Bojar
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation