Search Results for author: Saptarashmi Bandyopadhyay

Found 7 papers, 2 papers with code

Natural Language Response Generation from SQL with Generalization and Back-translation

no code implementations EMNLP (intexsempar) 2020 Saptarashmi Bandyopadhyay, Tianyang Zhao

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.

Machine Translation NMT +2

UdS-DFKI@WMT20: Unsupervised MT and Very Low Resource Supervised MT for German-Upper Sorbian

no code implementations WMT (EMNLP) 2020 Sourav Dutta, Jesujoba Alabi, Saptarashmi Bandyopadhyay, Dana Ruiter, Josef van Genabith

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).

Translation Unsupervised Machine Translation

Targets in Reinforcement Learning to solve Stackelberg Security Games

no code implementations30 Nov 2022 Saptarashmi Bandyopadhyay, Chenqi Zhu, Philip Daniel, Joshua Morrison, Ethan Shay, John Dickerson

Reinforcement Learning (RL) algorithms have been successfully applied to real world situations like illegal smuggling, poaching, deforestation, climate change, airport security, etc.

reinforcement-learning Reinforcement Learning (RL)

Improving Question Answering with Generation of NQ-like Questions

no code implementations12 Oct 2022 Saptarashmi Bandyopadhyay, Shraman Pal, Hao Zou, Abhranil Chandra, Jordan Boyd-Graber

We demonstrate that in a low resource setting, using the generated data improves the QA performance over the baseline system on both NQ and QB data.

Natural Questions Question Answering

Read, Highlight and Summarize: A Hierarchical Neural Semantic Encoder-based Approach

1 code implementation8 Oct 2019 Rajeev Bhatt Ambati, Saptarashmi Bandyopadhyay, Prasenjit Mitra

In this paper, we propose a method based on extracting the highlights of a document; a key concept that is conveyed in a few sentences.

Abstractive Text Summarization Hard Attention +4

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