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20172022
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cs.CL2022

Achieving Conversational Goals with Unsupervised Post-hoc Knowledge Injection

Bodhisattwa Prasad Majumder, Harsh Jhamtani, Taylor Berg-Kirkpatrick +1

A limitation of current neural dialog models is that they tend to suffer from a lack of specificity and informativeness in generated responses, primarily due to dependence on train…

cs.CL2021

Truth-Conditional Captioning of Time Series Data

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. A…

cs.CL2021

Investigating Robustness of Dialog Models to Popular Figurative Language Constructs

Harsh Jhamtani, Varun Gangal, Eduard Hovy +1

Humans often employ figurative language use in communication, including during interactions with dialog systems. Thus, it is important for real-world dialog systems to be able to h…

cs.CL2021

Unsupervised Enrichment of Persona-grounded Dialog with Background Stories

Bodhisattwa Prasad Majumder, Taylor Berg-Kirkpatrick, Julian McAuley +1

Humans often refer to personal narratives, life experiences, and events to make a conversation more engaging and rich. While persona-grounded dialog models are able to generate res…

cs.CL2021

Improving Automated Evaluation of Open Domain Dialog via Diverse Reference Augmentation

Varun Gangal, Harsh Jhamtani, Eduard Hovy +1

Multiple different responses are often plausible for a given open domain dialog context. Prior work has shown the importance of having multiple valid reference responses for meanin…

cs.CL2020

Narrative Text Generation with a Latent Discrete Plan

Harsh Jhamtani, Taylor Berg-Kirkpatrick

Past work on story generation has demonstrated the usefulness of conditioning on a generation plan to generate coherent stories. However, these approaches have used heuristics or o…