Search Results for author: Harsh Jhamtani

Found 22 papers, 17 papers with code

Truth-Conditional Captions for Time Series Data

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.

Time Series

Formulating Neural Sentence Ordering as the Asymmetric Traveling Salesman Problem

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.

Combinatorial Optimization Sentence Ordering +1

Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation

no code implementations19 May 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.

Data Augmentation Response Generation

Achieving Conversational Goals with Unsupervised Post-hoc Knowledge Injection

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.

Informativeness

Truth-Conditional Captioning of Time Series Data

1 code implementation5 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.

Time Series

Unsupervised Enrichment of Persona-grounded Dialog with Background Stories

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.

Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering

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.

14 Question Answering +1

Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions

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.

Learning Rhyming Constraints using Structured Adversaries

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.

A Sociolinguistic Study of Online Echo Chambers on Twitter

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.

Learning to Describe Differences Between Pairs of Similar Images

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.

SPINE: SParse Interpretable Neural Embeddings

1 code implementation23 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.

Denoising Word Embeddings

Shakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models

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.

Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models

2 code implementations4 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.

Generating Appealing Brand Names

no code implementations28 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.

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