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Arunima Chaudhary

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2 papers

1 author row

AAAI Conference 2022 System Paper

A Goal-Driven Natural Language Interface for Creating Application Integration Workflows

Michelle Brachman
Christopher Bygrave
Tathagata Chakraborti
Arunima Chaudhary
Zhining Ding
Casey Dugan
David Gros
Thomas Gschwind

Web applications and services are increasingly important in a distributed internet filled with diverse cloud services and applications, each of which enable the completion of narrowly defined tasks. Given the explosion in the scale and diversity of such services, their composition and integration for achieving complex user goals remains a challenging task for endusers and requires a lot of development effort when specified by hand. We present a demonstration of the Goal Oriented Flow Assistant (GOFA) system, which provides a natural language solution to generate workflows for application integration. Our tool is built on a three-step pipeline: it first uses Abstract Meaning Representation (AMR) to parse utterances; it then uses a knowledge graph to validate candidates; and finally uses an AI planner to compose the candidate flow. We provide a video demonstration of the deployed system as part of our submission.

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AAAI Conference 2021 System Paper

AutoText: An End-to-End AutoAI Framework for Text

Arunima Chaudhary
Alayt Issak
Kiran Kate
Yannis Katsis
Abel Valente
Dakuo Wang
Alexandre Evfimievski
Sairam Gurajada

Building models for natural language processing (NLP) tasks remains a daunting task for many, requiring significant technical expertise, efforts, and resources. In this demonstration, we present AutoText, an end-to-end AutoAI framework for text, to lower the barrier of entry in building NLP models. AutoText combines state-of-the-art AutoAI optimization techniques and learning algorithms for NLP tasks into a single extensible framework. Through its simple, yet powerful UI, non-AI experts (e. g. , domain experts) can quickly generate performant NLP models with support to both control (e. g. , via specifying constraints) and understand learned models.

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