Information Extraction and Program Synthesis from Goal-Oriented Dialogue (2023)

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.

## research interests 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. ## spoken dialogue system (sds) research owing to the fast-paced innovations in language and speech, sdss are likely to transition into becoming useful assistants for the human users. they might go one step further, and be able to interact with their environment to perform tasks and help the user achieve their goal. to get to this stage, however, sds research has to focus on dialog management modules that are capable of accurately modeling user's intents and goals, translating those intents into actionable steps (or programs), executing those steps in their (digital) environment, and deploying remedial measures when needed. all of which will need to happen in a transparent, verifiable, and controlled setting.