@inproceedings{khosla-2023-information,
title = "Information Extraction and Program Synthesis from Goal-Oriented Dialogue",
author = "Khosla, Sopan",
editor = "Hudecek, Vojtech and
Schmidtova, Patricia and
Dinkar, Tanvi and
Chiyah-Garcia, Javier and
Sieinska, Weronika",
booktitle = "Proceedings of the 19th Annual Meeting of the Young Reseachers' Roundtable on Spoken Dialogue Systems",
month = sep,
year = "2023",
address = "Prague, Czechia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.yrrsds-1.19",
pages = "51--53",
abstract = "My research interests broadly lie in the area of Information Extraction from Spoken Dialogue, with a spacial focus on state modeling, anaphora resolution, program synthesis {\&} planning, and intent classification in goal-oriented conversations. My aim is to create embedded dialogue systems that can interact with humans in a collaborative setup to solve tasks in a digital/non-digital environment. Most of the goal-oriented conversations usually involve experts and a laypersons. The aim for the expert is to consider all the information provided by the layperson, identify the underlying set of issues or intents, and prescribe solutions. While human experts are very good at extracting such information, AI agents (that build up most of the automatic dialog systems today) not so much. Most of the existing assistants (or chatbots) only consider individual utterances and do not ground them in the context of the dialogue. My work in this direction has focused on making these systems more effective at extracting the most relevant information from the dialogue to help the human user reach their end-goal.",
}
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<abstract>My research interests broadly lie in the area of Information Extraction from Spoken Dialogue, with a spacial focus on state modeling, anaphora resolution, program synthesis & planning, and intent classification in goal-oriented conversations. My aim is to create embedded dialogue systems that can interact with humans in a collaborative setup to solve tasks in a digital/non-digital environment. Most of the goal-oriented conversations usually involve experts and a laypersons. The aim for the expert is to consider all the information provided by the layperson, identify the underlying set of issues or intents, and prescribe solutions. While human experts are very good at extracting such information, AI agents (that build up most of the automatic dialog systems today) not so much. Most of the existing assistants (or chatbots) only consider individual utterances and do not ground them in the context of the dialogue. My work in this direction has focused on making these systems more effective at extracting the most relevant information from the dialogue to help the human user reach their end-goal.</abstract>
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%0 Conference Proceedings
%T Information Extraction and Program Synthesis from Goal-Oriented Dialogue
%A Khosla, Sopan
%Y Hudecek, Vojtech
%Y Schmidtova, Patricia
%Y Dinkar, Tanvi
%Y Chiyah-Garcia, Javier
%Y Sieinska, Weronika
%S Proceedings of the 19th Annual Meeting of the Young Reseachers’ Roundtable on Spoken Dialogue Systems
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F khosla-2023-information
%X My research interests broadly lie in the area of Information Extraction from Spoken Dialogue, with a spacial focus on state modeling, anaphora resolution, program synthesis & planning, and intent classification in goal-oriented conversations. My aim is to create embedded dialogue systems that can interact with humans in a collaborative setup to solve tasks in a digital/non-digital environment. Most of the goal-oriented conversations usually involve experts and a laypersons. The aim for the expert is to consider all the information provided by the layperson, identify the underlying set of issues or intents, and prescribe solutions. While human experts are very good at extracting such information, AI agents (that build up most of the automatic dialog systems today) not so much. Most of the existing assistants (or chatbots) only consider individual utterances and do not ground them in the context of the dialogue. My work in this direction has focused on making these systems more effective at extracting the most relevant information from the dialogue to help the human user reach their end-goal.
%U https://aclanthology.org/2023.yrrsds-1.19
%P 51-53
Markdown (Informal)
[Information Extraction and Program Synthesis from Goal-Oriented Dialogue](https://aclanthology.org/2023.yrrsds-1.19) (Khosla, YRRSDS-WS 2023)
ACL