Search Results for author: Kushal Chawla

Found 15 papers, 2 papers with code

Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks

no code implementations11 Oct 2022 Kushal Chawla, Weiyan Shi, Jingwen Zhang, Gale Lucas, Zhou Yu, Jonathan Gratch

Dialogue systems capable of social influence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios.

Opponent Modeling in Negotiation Dialogues by Related Data Adaptation

1 code implementation Findings (NAACL) 2022 Kushal Chawla, Gale M. Lucas, Jonathan May, Jonathan Gratch

A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training.

Towards Emotion-Aware Agents For Negotiation Dialogues

no code implementations28 Jul 2021 Kushal Chawla, Rene Clever, Jaysa Ramirez, Gale Lucas, Jonathan Gratch

Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making.

Decision Making

Pilot: Winner of the Human-Agent Negotiation Challenge at IJCAI 2020

no code implementations14 Sep 2020 Kushal Chawla, Gale Lucas

This document describes our agent Pilot, winner of the Human-Agent Negotiation Challenge at ANAC, IJCAI 2020.

Exploring Early Prediction of Buyer-Seller Negotiation Outcomes

no code implementations6 Apr 2020 Kushal Chawla, Gale Lucas, Jonathan May, Jonathan Gratch

Agents that negotiate with humans find broad applications in pedagogy and conversational AI.

Language Modelling

Generating summaries tailored to target characteristics

no code implementations18 Dec 2019 Kushal Chawla, Hrituraj Singh, Arijit Pramanik, Mithlesh Kumar, Balaji Vasan Srinivasan

Recently, research efforts have gained pace to cater to varied user preferences while generating text summaries.

Text Summarization

Improving generation quality of pointer networks via guided attention

no code implementations20 Jan 2019 Kushal Chawla, Kundan Krishna, Balaji Vasan Srinivasan

The first shortcoming is the extractive nature of the generated summaries, since the network eventually learns to copy from the input article most of the times, affecting the abstractive nature of the generated summaries.

Abstractive Text Summarization

Forecasting Granular Audience Size for Online Advertising

no code implementations8 Jan 2019 Ritwik Sinha, Dhruv Singal, Pranav Maneriker, Kushal Chawla, Yash Shrivastava, Deepak Pai, Atanu R. Sinha

Orchestration of campaigns for online display advertising requires marketers to forecast audience size at the granularity of specific attributes of web traffic, characterized by the categorical nature of all attributes (e. g. {US, Chrome, Mobile}).

Time Series Time Series Analysis

Investigating how well contextual features are captured by bi-directional recurrent neural network models

no code implementations WS 2017 Kushal Chawla, Sunil Kumar Sahu, Ashish Anand

Our experiments focus on important contextual words as features, which can easily be extended to analyze various other feature types.

Feature Engineering

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