1 code implementation • INLG (ACL) 2021 • Vishal Keswani, Harsh Jhamtani
However, such an approach has major limitations – it cannot handle the presence of cycles in the resulting graphs and considers only the binary presence/absence of edges rather than a more granular score.
1 code implementation • EMNLP 2021 • Harsh Jhamtani, Taylor Berg-Kirkpatrick
In this paper, we explore the task of automatically generating natural language descriptions of salient patterns in a time series, such as stock prices of a company over a week.
no code implementations • 16 Nov 2023 • Nikita Moghe, Patrick Xia, Jacob Andreas, Jason Eisner, Benjamin Van Durme, Harsh Jhamtani
To alleviate this, we propose including some of a user's preferences and instructions in natural language -- collectively termed standing instructions -- as additional context for such interfaces.
1 code implementation • 20 Sep 2023 • Kumar Shridhar, Harsh Jhamtani, Hao Fang, Benjamin Van Durme, Jason Eisner, Patrick Xia
To enable exploration in this space, we present SCREWS, a modular framework for reasoning with revisions.
no code implementations • 15 May 2023 • Harsh Jhamtani, Hao Fang, Patrick Xia, Eran Levy, Jacob Andreas, Ben Van Durme
We introduce an approach for equipping a simple language-to-code model to handle complex utterances via a process of hierarchical natural language decomposition.
no code implementations • 20 Dec 2022 • Nathaniel Weir, Ryan Thomas, Randolph D'Amore, Kellie Hill, Benjamin Van Durme, Harsh Jhamtani
We introduce a language generation task grounded in a popular video game environment.
1 code implementation • COLING 2022 • Sedrick Scott Keh, Kevin Lu, Varun Gangal, Steven Y. Feng, Harsh Jhamtani, Malihe Alikhani, Eduard Hovy
To this end, we propose PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation.
1 code implementation • 16 Sep 2022 • Hao Fang, Anusha Balakrishnan, Harsh Jhamtani, John Bufe, Jean Crawford, Jayant Krishnamurthy, Adam Pauls, Jason Eisner, Jacob Andreas, Dan Klein
Satisfying these constraints simultaneously is difficult for the two predominant paradigms in language generation: neural language modeling and rule-based generation.
no code implementations • Findings (NAACL) 2022 • Prakhar Gupta, Harsh Jhamtani, Jeffrey P. Bigham
Target-guided response generation enables dialogue systems to smoothly transition a conversation from a dialogue context toward a target sentence.
1 code implementation • ACL 2022 • Bodhisattwa Prasad Majumder, Harsh Jhamtani, Taylor Berg-Kirkpatrick, Julian McAuley
In this paper, we propose a post-hoc knowledge-injection technique where we first retrieve a diverse set of relevant knowledge snippets conditioned on both the dialog history and an initial response from an existing dialog model.
1 code implementation • 5 Oct 2021 • Harsh Jhamtani, Taylor Berg-Kirkpatrick
In this paper, we explore the task of automatically generating natural language descriptions of salient patterns in a time series, such as stock prices of a company over a week.
1 code implementation • EMNLP 2021 • Harsh Jhamtani, Varun Gangal, Eduard Hovy, Taylor Berg-Kirkpatrick
Humans often employ figurative language use in communication, including during interactions with dialog systems.
1 code implementation • ACL 2021 • Bodhisattwa Prasad Majumder, Taylor Berg-Kirkpatrick, Julian McAuley, Harsh Jhamtani
Humans often refer to personal narratives, life experiences, and events to make a conversation more engaging and rich.
1 code implementation • Findings (ACL) 2021 • Varun Gangal, Harsh Jhamtani, Eduard Hovy, Taylor Berg-Kirkpatrick
Multiple different responses are often plausible for a given open domain dialog context.
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.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
1 code implementation • EMNLP 2020 • Bodhisattwa Prasad Majumder, Harsh Jhamtani, Taylor Berg-Kirkpatrick, Julian McAuley
Existing persona-grounded dialog models often fail to capture simple implications of given persona descriptions, something which humans are able to do seamlessly.
1 code implementation • EMNLP 2020 • Harsh Jhamtani, Peter Clark
The third dataset eOBQA is constructed by adding explanation annotations to the OBQA dataset to test generalization of models trained on eQASC.
Ranked #1 on
Reasoning Chain Explanations
on eQASC
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Harsh Jhamtani, Taylor Berg-Kirkpatrick
Past work on story generation has demonstrated the usefulness of conditioning on a generation plan to generate coherent stories.
1 code implementation • IJCNLP 2019 • Harsh Jhamtani, Sanket Vaibhav Mehta, Jaime Carbonell, Taylor Berg-Kirkpatrick
Existing recurrent neural language models often fail to capture higher-level structure present in text: for example, rhyming patterns present in poetry.
no code implementations • WS 2019 • Nikita Duseja, Harsh Jhamtani
Online social media platforms such as Facebook and Twitter are increasingly facing criticism for polarization of users.
1 code implementation • EMNLP 2018 • Harsh Jhamtani, Taylor Berg-Kirkpatrick
We propose a model that captures visual salience by using a latent variable to align clusters of differing pixels with output sentences.
1 code implementation • ACL 2018 • Harsh Jhamtani, Varun Gangal, Eduard Hovy, Graham Neubig, Taylor Berg-Kirkpatrick
This paper examines the problem of generating natural language descriptions of chess games.
2 code implementations • 23 Nov 2017 • Anant Subramanian, Danish Pruthi, Harsh Jhamtani, Taylor Berg-Kirkpatrick, Eduard Hovy
We propose a novel variant of denoising k-sparse autoencoders that generates highly efficient and interpretable distributed word representations (word embeddings), beginning with existing word representations from state-of-the-art methods like GloVe and word2vec.
1 code implementation • WS 2017 • Harsh Jhamtani, Varun Gangal, Eduard Hovy, Eric Nyberg
Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose.
2 code implementations • 4 Jul 2017 • Harsh Jhamtani, Varun Gangal, Eduard Hovy, Eric Nyberg
Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose.
1 code implementation • EMNLP 2017 • Varun Gangal, Harsh Jhamtani, Graham Neubig, Eduard Hovy, Eric Nyberg
Portmanteaus are a word formation phenomenon where two words are combined to form a new word.
no code implementations • 28 Jun 2017 • Gaurush Hiranandani, Pranav Maneriker, Harsh Jhamtani
Providing appealing brand names to newly launched products, newly formed companies or for renaming existing companies is highly important as it can play a crucial role in deciding its success or failure.