This repository contains the code and sample data for running the method proposed in the following paper: Naoki Otani, Michael Gamon, Sujay Kumar Jauhar, Mei Yang, Sri Raghu Malireddi, and Oriana Riva. 2022. LITE: Intent-based Task Representation Learning Using Weak Supervision. In Proc. of NAACL-HLT.

Directories:

• DataPreprocessing/: code and sample data for data preprocessing
• TaskReprLearning/: code and data for training an encoder
• Evaluation/: code for running downstream experiments

See README.md in each directory for more details.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

If you use the code or data in your work, please cite the following paper:

Naoki Otani, Michael Gamon, Sujay Kumar Jauhar, Mei Yang, Sri Raghu Malireddi, and Oriana Riva. 2022. LITE: Intent-based Task Representation Learning Using Weak Supervision. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Seattle, Washington, July. Association for Computational Linguistics.

@inproceedings{otani-etal-2022-lite,
title = "{LITE}: {I}ntent-based Task Representation Learning Using Weak Supervision.",
author = "Otani, Naoki  and
          Gamon, Michael  and
          Jauhar, Sujay Kumar  and
          Yang, Mei  and
          Malireddi, Sri Raghu  and
          Riva, Oriana",
          booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
          month = jul,
          year = "2022",
          address = "Seattle, Washington",
          publisher = "Association for Computational Linguistics",
}