Dipanjan Das
Dipanjan Das is a Research Scientist at Google working on learning semantic representations of language. He received a Ph.D. in 2012 from the Language Technologies Institute, School of Computer Science at Carnegie Mellon University. Before that, he completed an undergraduate degree in Computer Science and Engineering in 2005 from the Indian Institute of Technology, Kharagpur. His work on multilingual learning of sequence models received the best paper award at ACL 2011 and a best paper award honorable mention at EMNLP 2013.
See his personal webpage for more information.
See his personal webpage for more information.
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Conditional Generation with a Question-Answering Blueprint
Reinald Kim Amplayo
Fantine Huot
Mirella Lapata
Transactions of the Association for Computational Linguistics (2023) (to appear)
Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation
Fantine Huot
Reinald Kim Amplayo
Mirella Lapata
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations (2023)
Measuring Attribution in Natural Language Generation Models
Iulia Turc
Computational Linguistics, 49 (2023), pp. 777-840
SEAHORSE: A Dataset of Summaries Annotated with Human Ratings in Six Languages
Elizabeth Clark
Shruti Rijhwani
Sebastian Gehrmann
EMNLP 2023, Association for Computational Linguistics (2023)
Query Refinement Prompts for Closed-Book Long-Form Question Answering
Reinald Kim Amplayo
arXiv submission (2022)
The MultiBERTs: BERT Reproductions for Robustness Analysis
Steve Yadlowsky
Jason Wei
Naomi Saphra
Iulia Raluca Turc
2022
QAmeleon: Multilingual QA with Only 5 Examples
Fantine Huot
Sebastian Ruder
Mirella Lapata
Arxiv (2022)
Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
Pat Verga
Jianmo Ni
arXiv (2022)
A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation
Yao Zhao
Mirella Lapata
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022), Association for Computational Linguistics, pp. 21
Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (2021), pp. 704-718
