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1. Intent Detection with WikiHow

   Zhang, Li; Lyu, Qing; Callison-Burch, Chris (January 2020, Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing)

   Modern task-oriented dialog systems need to reliably understand users’ intents. Intent detection is even more challenging when moving to new domains or new languages, since there is little annotated data. To address this challenge, we present a suite of pretrained intent detection models which can predict a broad range of intended goals from many actions because they are trained on wikiHow, a comprehensive instructional website. Our models achieve state-of-the-art results on the Snips dataset, the Schema-Guided Dialogue dataset, and all 3 languages of the Facebook multilingual dialog datasets. Our models also demonstrate strong zero- and few-shot performance, reaching over 75% accuracy using only 100 training examples in all datasets. 

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2. Recognizing Intent in Collaborative Manipulation

   https://doi.org/10.1145/3577190.3614174  

   Rysbek, Zhanibek; Oh, Ki-Hwan; Zefran, Milos (October 2023, ACM)
3. INTENT: Interactive Tensor Transformation Synthesis

   https://doi.org/10.1145/3526113.3545653  

   Zhou, Zhanhui; Tang, Man To; Pan, Qiping; Tan, Shangyin; Wang, Xinyu; Zhang, Tianyi (October 2022, Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology)
4. Provenance for Intent-Based Networking

   Ujcich, Benjamin E.; Bates, Adam; Sanders, William H. (January 2020, Proceedings of the IEEE Conference on Network Softwarization)

   Intent-based networking (IBN) promises to simplify the network management and automated orchestration of high-level policies in future networking architectures such as software-defined networking (SDN). However, such abstraction and automation creates new network visibility challenges. Existing SDN network forensics and diagnostics tools operate at a lower level of network abstraction, which makes intent-level reasoning difficult. We present PROVINTENT, a framework extension for SDN control plane tools that accounts for intent semantics. PROVINTENT records the provenance and evolution of intents as the network’s state and apps’ requests change over time and enables reasoning at multiple abstractions. We define an intent provenance model, we implement a proof-of-concept tool, and we evaluate the efficacy of PROVINTENT’s explanatory capabilities by using a representative intent-driven network application. 

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5. Inferring Visualization Intent from Conversation

   Li, Haotian; et, al (October 2024, Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM ’24),)