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Michelle Brachman

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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 2022 System Paper

AI Assisted Data Labeling with Interactive Auto Label

  • Michael Desmond
  • Michelle Brachman
  • Evelyn Duesterwald
  • Casey Dugan
  • Narendra Nath Joshi
  • Qian Pan
  • Carolina Spina

We demonstrate an AI assisted data labeling system which applies unsupervised and semi-supervised machine learning to facilitate accurate and efficient labeling of large data sets. Our system (1) applies representative data sampling and active learning in order to seed and maintain a semi-supervised learner that assists the human labeler (2) provides visual labeling assistance and optimizes labeling mechanics using predicted labels (3) seamlessly updates and learns from ongoing human labeling activity (4) captures and presents metrics that indicate the quality of labeling assistance, and (5) provides an interactive auto labeling interface to group, review and apply predicted labels in a scalable manner.

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