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Thomas Gschwind

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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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ICLR Conference 2022 Conference Paper

Attention-based Interpretability with Concept Transformers

  • Mattia Rigotti
  • Christoph Miksovic
  • Ioana Giurgiu
  • Thomas Gschwind
  • Paolo Scotton

Attention is a mechanism that has been instrumental in driving remarkable performance gains of deep neural network models in a host of visual, NLP and multimodal tasks. One additional notable aspect of attention is that it conveniently exposes the ``reasoning'' behind each particular output generated by the model. Specifically, attention scores over input regions or intermediate features have been interpreted as a measure of the contribution of the attended element to the model inference. While the debate in regard to the interpretability of attention is still not settled, researchers have pointed out the existence of architectures and scenarios that afford a meaningful interpretation of the attention mechanism. Here we propose the generalization of attention from low-level input features to high-level concepts as a mechanism to ensure the interpretability of attention scores within a given application domain. In particular, we design the ConceptTransformer, a deep learning module that exposes explanations of the output of a model in which it is embedded in terms of attention over user-defined high-level concepts. Such explanations are \emph{plausible} (i.e.\ convincing to the human user) and \emph{faithful} (i.e.\ truly reflective of the reasoning process of the model). Plausibility of such explanations is obtained by construction by training the attention heads to conform with known relations between inputs, concepts and outputs dictated by domain knowledge. Faithfulness is achieved by design by enforcing a linear relation between the transformer value vectors that represent the concepts and their contribution to the classification log-probabilities. We validate our ConceptTransformer module on established explainability benchmarks and show how it can be used to infuse domain knowledge into classifiers to improve accuracy, and conversely to extract concept-based explanations of classification outputs. Code to reproduce our results is available at: \url{https://github.com/ibm/concept_transformer}.

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