Search Results for author: Shantanu Agarwal

Found 6 papers, 3 papers with code

Authorship Style Transfer with Policy Optimization

1 code implementation12 Mar 2024 Shuai Liu, Shantanu Agarwal, Jonathan May

Authorship style transfer aims to rewrite a given text into a specified target while preserving the original meaning in the source.

Style Transfer Transfer Learning

VecFusion: Vector Font Generation with Diffusion

no code implementations16 Dec 2023 Vikas Thamizharasan, Difan Liu, Shantanu Agarwal, Matthew Fisher, Michael Gharbi, Oliver Wang, Alec Jacobson, Evangelos Kalogerakis

We present VecFusion, a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions.

Font Generation Vector Graphics

Massively Multi-Lingual Event Understanding: Extraction, Visualization, and Search

no code implementations17 May 2023 Chris Jenkins, Shantanu Agarwal, Joel Barry, Steven Fincke, Elizabeth Boschee

In this paper, we present ISI-Clear, a state-of-the-art, cross-lingual, zero-shot event extraction system and accompanying user interface for event visualization & search.

Natural Language Queries Zero-shot Event Extraction

Impact of Subword Pooling Strategy on Cross-lingual Event Detection

1 code implementation22 Feb 2023 Shantanu Agarwal, Steven Fincke, Chris Jenkins, Scott Miller, Elizabeth Boschee

Taking the task of cross-lingual event detection as a motivating example, we show that the choice of pooling strategy can have a significant impact on the target language performance.

Event Detection Event Extraction +3

Language Model Priming for Cross-Lingual Event Extraction

no code implementations25 Sep 2021 Steven Fincke, Shantanu Agarwal, Scott Miller, Elizabeth Boschee

We show that by enabling the language model to better compensate for the deficits of sparse and noisy training data, our approach improves both trigger and argument detection and classification significantly over the state of the art in a zero-shot cross-lingual setting.

Event Extraction Language Modelling +1

End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs

1 code implementation EMNLP 2021 Dinesh Raghu, Shantanu Agarwal, Sachindra Joshi, Mausam

We propose a novel problem within end-to-end learning of task-oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e. g., car not starting).

Flowchart Grounded Dialog Response Generation Retrieval +1

Cannot find the paper you are looking for? You can Submit a new open access paper.