Saptarashmi Bandyopadhyay


The University of Maryland, College Park Submission to Large-Scale Multilingual Shared Task at WMT 2021
Saptarashmi Bandyopadhyay | Tasnim Kabir | Zizhen Lian | Marine Carpuat
Proceedings of the Sixth Conference on Machine Translation

This paper describes the system submitted to Large-Scale Multilingual Shared Task (Small Task #2) at WMT 2021. It is based on the massively multilingual open-source model FLORES101_MM100 model, with selective fine-tuning.Our best-performing system reported a 15.72 average BLEU score for the task.


Natural Language Response Generation from SQL with Generalization and Back-translation
Saptarashmi Bandyopadhyay | Tianyang Zhao
Proceedings of the First Workshop on Interactive and Executable Semantic Parsing

Generation of natural language responses to the queries of structured language like SQL is very challenging as it requires generalization to new domains and the ability to answer ambiguous queries among other issues. We have participated in the CoSQL shared task organized in the IntEx-SemPar workshop at EMNLP 2020. We have trained a number of Neural Machine Translation (NMT) models to efficiently generate the natural language responses from SQL. Our shuffled back-translation model has led to a BLEU score of 7.47 on the unknown test dataset. In this paper, we will discuss our methodologies to approach the problem and future directions to improve the quality of the generated natural language responses.

UdS-DFKI@WMT20: Unsupervised MT and Very Low Resource Supervised MT for German-Upper Sorbian
Sourav Dutta | Jesujoba Alabi | Saptarashmi Bandyopadhyay | Dana Ruiter | Josef van Genabith
Proceedings of the Fifth Conference on Machine Translation

This paper describes the UdS-DFKI submission to the shared task for unsupervised machine translation (MT) and very low-resource supervised MT between German (de) and Upper Sorbian (hsb) at the Fifth Conference of Machine Translation (WMT20). We submit systems for both the supervised and unsupervised tracks. Apart from various experimental approaches like bitext mining, model pre-training, and iterative back-translation, we employ a factored machine translation approach on a small BPE vocabulary.


Factored Neural Machine Translation at LoResMT 2019
Saptarashmi Bandyopadhyay
Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages


Content selection as semantic-based ontology exploration
Laura Perez-Beltrachini | Claire Gardent | Anselme Revuz | Saptarashmi Bandyopadhyay
Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web (WebNLG 2016)