Search Results for author: Koel Dutta Chowdhury

Found 13 papers, 1 papers with code

Tracing Source Language Interference in Translation with Graph-Isomorphism Measures

no code implementations RANLP 2021 Koel Dutta Chowdhury, Cristina España-Bonet, Josef van Genabith

Previous research has used linguistic features to show that translations exhibit traces of source language interference and that phylogenetic trees between languages can be reconstructed from the results of translations into the same language.

Open-Ended Question Answering Translation

Translating away Translationese without Parallel Data

no code implementations28 Oct 2023 Rricha Jalota, Koel Dutta Chowdhury, Cristina España-Bonet, Josef van Genabith

We show how we can eliminate the need for parallel validation data by combining the self-supervised loss with an unsupervised loss.

Binary Classification Language Modelling +3

Towards Debiasing Translation Artifacts

1 code implementation NAACL 2022 Koel Dutta Chowdhury, Rricha Jalota, Cristina España-Bonet, Josef van Genabith

Cross-lingual natural language processing relies on translation, either by humans or machines, at different levels, from translating training data to translating test sets.

Natural Language Inference Translation

EdinSaar@WMT21: North-Germanic Low-Resource Multilingual NMT

no code implementations WMT (EMNLP) 2021 Svetlana Tchistiakova, Jesujoba Alabi, Koel Dutta Chowdhury, Sourav Dutta, Dana Ruiter

We describe the EdinSaar submission to the shared task of Multilingual Low-Resource Translation for North Germanic Languages at the Sixth Conference on Machine Translation (WMT2021).

Machine Translation NMT +1

Understanding Translationese in Multi-view Embedding Spaces

no code implementations COLING 2020 Koel Dutta Chowdhury, Cristina Espa{\~n}a-Bonet, Josef van Genabith

Recent studies use a combination of lexical and syntactic features to show that footprints of the source language remain visible in translations, to the extent that it is possible to predict the original source language from the translation.


How Human is Machine Translationese? Comparing Human and Machine Translations of Text and Speech

no code implementations WS 2020 Yuri Bizzoni, Tom S Juzek, Cristina Espa{\~n}a-Bonet, Koel Dutta Chowdhury, Josef van Genabith, Elke Teich

Some translationese features tend to appear in simultaneous interpreting with higher frequency than in human text translation, but the reasons for this are unclear.

Machine Translation Translation

Understanding the Effect of Textual Adversaries in Multimodal Machine Translation

no code implementations WS 2019 Koel Dutta Chowdhury, Desmond Elliott

It is assumed that multimodal machine translation systems are better than text-only systems at translating phrases that have a direct correspondence in the image.

Multimodal Machine Translation Translation

The RGNLP Machine Translation Systems for WAT 2018

no code implementations PACLIC 2018 Atul Kr. Ojha, Koel Dutta Chowdhury, Chao-Hong Liu, Karan Saxena

This paper presents the system description of Machine Translation (MT) system(s) for Indic Languages Multilingual Task for the 2018 edition of the WAT Shared Task.

Machine Translation Translation

Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data

no code implementations WS 2018 Koel Dutta Chowdhury, Mohammed Hasanuzzaman, Qun Liu

In this paper, we investigate the effectiveness of training a multimodal neural machine translation (MNMT) system with image features for a low-resource language pair, Hindi and English, using synthetic data.

Machine Translation Question Answering +4

ADAPT at IJCNLP-2017 Task 4: A Multinomial Naive Bayes Classification Approach for Customer Feedback Analysis task

no code implementations IJCNLP 2017 Pintu Lohar, Koel Dutta Chowdhury, Haithem Afli, Mohammed Hasanuzzaman, Andy Way

In this paper, we analyse the real world samples of customer feedback from Microsoft Office customers in four languages, i. e., English, French, Spanish and Japanese and conclude a five-plus-one-classes categorisation (comment, request, bug, complaint, meaningless and undetermined) for meaning classification.

Classification General Classification +3

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