1 code implementation • EMNLP 2021 • Aman Madaan, Niket Tandon, Dheeraj Rajagopal, Peter Clark, Yiming Yang, Eduard Hovy
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.
no code implementations • 25 May 2022 • Aman Madaan, Dheeraj Rajagopal, Niket Tandon, Yiming Yang, Antoine Bosselut
Conditional set generation learns a mapping from an input sequence of tokens to a set.
1 code implementation • 16 Jan 2022 • Aman Madaan, Niket Tandon, Peter Clark, Yiming Yang
Large LMs such as GPT-3 are powerful, but can commit mistakes that are obvious to humans.
1 code implementation • 16 Dec 2021 • Niket Tandon, Aman Madaan, Peter Clark, Yiming Yang
Our goal is for an LM to continue to improve after deployment, without retraining, using feedback from the user.
1 code implementation • 15 Dec 2021 • Niket Tandon, Aman Madaan, Peter Clark, Keisuke Sakaguchi, Yiming Yang
We present a new dataset, Interscript, containing user feedback on a deployed model that generates complex everyday tasks.
1 code implementation • 24 Oct 2021 • Aman Madaan, Niket Tandon, Dheeraj Rajagopal, Peter Clark, Yiming Yang, Eduard Hovy
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.
no code implementations • AKBC Workshop CSKB 2021 • Aman Madaan, Dheeraj Rajagopal, Niket Tandon, Yiming Yang, Eduard Hovy
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.
no code implementations • 18 Apr 2021 • Aman Madaan, Niket Tandon, Dheeraj Rajagopal, Yiming Yang, Peter Clark, Keisuke Sakaguchi, Ed Hovy
A class of explainable NLP models for reasoning tasks support their decisions by generating free-form or structured explanations, but what happens when these supporting structures contain errors?
1 code implementation • CSRR (ACL) 2022 • Dheeraj Rajagopal, Aman Madaan, Niket Tandon, Yiming Yang, Shrimai Prabhumoye, Abhilasha Ravichander, Peter Clark, Eduard Hovy
Recently, models have been shown to predict the effects of unexpected situations, e. g., would cloudy skies help or hinder plant growth?
no code implementations • ACL (GEM) 2021 • Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, Pawan Sasanka Ammanamanchi, Aremu Anuoluwapo, Antoine Bosselut, Khyathi Raghavi Chandu, Miruna Clinciu, Dipanjan Das, Kaustubh D. Dhole, Wanyu Du, Esin Durmus, Ondřej Dušek, Chris Emezue, Varun Gangal, Cristina Garbacea, Tatsunori Hashimoto, Yufang Hou, Yacine Jernite, Harsh Jhamtani, Yangfeng Ji, Shailza Jolly, Mihir Kale, Dhruv Kumar, Faisal Ladhak, Aman Madaan, Mounica Maddela, Khyati Mahajan, Saad Mahamood, Bodhisattwa Prasad Majumder, Pedro Henrique Martins, Angelina McMillan-Major, Simon Mille, Emiel van Miltenburg, Moin Nadeem, Shashi Narayan, Vitaly Nikolaev, Rubungo Andre Niyongabo, Salomey Osei, Ankur Parikh, Laura Perez-Beltrachini, Niranjan Ramesh Rao, Vikas Raunak, Juan Diego Rodriguez, Sashank Santhanam, João Sedoc, Thibault Sellam, Samira Shaikh, Anastasia Shimorina, Marco Antonio Sobrevilla Cabezudo, Hendrik Strobelt, Nishant Subramani, Wei Xu, Diyi Yang, Akhila Yerukola, Jiawei Zhou
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
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Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
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no code implementations • 22 Oct 2020 • Aman Madaan, Dheeraj Rajagopal, Yiming Yang, Abhilasha Ravichander, Eduard Hovy, Shrimai Prabhumoye
Reasoning about events and tracking their influences is fundamental to understanding processes.
1 code implementation • NAACL 2021 • Aman Madaan, Yiming Yang
We address this challenge by using existing IE/NLP tools to automatically generate a large quantity (89, 000) of system-produced document-graph pairs, and propose a novel formulation of the contextualized graph generation problem as a sequence-to-sequence mapping task.
1 code implementation • ACL 2020 • Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W. black, Shrimai Prabhumoye
This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning.
1 code implementation • 24 Apr 2020 • Aman Madaan, Shruti Rijhwani, Antonios Anastasopoulos, Yiming Yang, Graham Neubig
We propose a method of curating high-quality comparable training data for low-resource languages with monolingual annotators.
no code implementations • 14 May 2016 • Aman Madaan, Sunita Sarawagi
This is owing to the severe mismatch in the distributions of such entities on the web and in the relatively diminutive training data.