Search Results for author: Budhaditya Deb

Found 11 papers, 7 papers with code

On Improving Summarization Factual Consistency from Natural Language Feedback

1 code implementation20 Dec 2022 Yixin Liu, Budhaditya Deb, Milagro Teruel, Aaron Halfaker, Dragomir Radev, Ahmed H. Awadallah

In this work, we study whether informational feedback in natural language can be leveraged to improve generation quality and user preference alignment.

Text Generation

Boosting Natural Language Generation from Instructions with Meta-Learning

no code implementations20 Oct 2022 Budhaditya Deb, Guoqing Zheng, Ahmed Hassan Awadallah

Recent work has shown that language models (LMs) trained with multi-task \textit{instructional learning} (MTIL) can solve diverse NLP tasks in zero- and few-shot settings with improved performance compared to prompt tuning.

Meta-Learning Text Generation

Leveraging Locality in Abstractive Text Summarization

1 code implementation25 May 2022 Yixin Liu, Ansong Ni, Linyong Nan, Budhaditya Deb, Chenguang Zhu, Ahmed H. Awadallah, Dragomir Radev

Our experimental results show that our model has a better performance compared with strong baselines with efficient attention modules, and our analysis provides further insights into our locality-aware modeling strategy.

Abstractive Text Summarization Text Generation

An Exploratory Study on Long Dialogue Summarization: What Works and What's Next

1 code implementation10 Sep 2021 Yusen Zhang, Ansong Ni, Tao Yu, Rui Zhang, Chenguang Zhu, Budhaditya Deb, Asli Celikyilmaz, Ahmed Hassan Awadallah, Dragomir Radev

Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series.


Language Scaling for Universal Suggested Replies Model

no code implementations NAACL 2021 Qianlan Ying, Payal Bajaj, Budhaditya Deb, Yu Yang, Wei Wang, Bojia Lin, Milad Shokouhi, Xia Song, Yang Yang, Daxin Jiang

Faced with increased compute requirements and low resources for language expansion, we build a single universal model for improving the quality and reducing run-time costs of our production system.

Continual Learning Cross-Lingual Transfer

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