Bootstrapping Conversational Agents With Weak Supervision. Mallinar, N., Shah, A., Ugrani, R., Gupta, A., Gurusankar, M., Ho, T. K., Liao, Q. V., Zhang, Y., Bellamy, R. K. E., Yates, R., Desmarais, C., & McGregor, B.
Bootstrapping Conversational Agents With Weak Supervision [link]Paper  abstract   bibtex   
Many conversational agents in the market today follow a standard bot development framework which requires training intent classifiers to recognize user input. The need to create a proper set of training examples is often the bottleneck in the development process. In many occasions agent developers have access to historical chat logs that can provide a good quantity as well as coverage of training examples. However, the cost of labeling them with tens to hundreds of intents often prohibits taking full advantage of these chat logs. In this paper, we present a framework called \textbackslashtextit\search, label, and propagate\ (SLP) for bootstrapping intents from existing chat logs using weak supervision. The framework reduces hours to days of labeling effort down to minutes of work by using a search engine to find examples, then relies on a data programming approach to automatically expand the labels. We report on a user study that shows positive user feedback for this new approach to build conversational agents, and demonstrates the effectiveness of using data programming for auto-labeling. While the system is developed for training conversational agents, the framework has broader application in significantly reducing labeling effort for training text classifiers.
@article{mallinarBootstrappingConversationalAgents2018,
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1812.06176},
  primaryClass = {cs},
  title = {Bootstrapping {{Conversational Agents With Weak Supervision}}},
  url = {http://arxiv.org/abs/1812.06176},
  abstract = {Many conversational agents in the market today follow a standard bot development framework which requires training intent classifiers to recognize user input. The need to create a proper set of training examples is often the bottleneck in the development process. In many occasions agent developers have access to historical chat logs that can provide a good quantity as well as coverage of training examples. However, the cost of labeling them with tens to hundreds of intents often prohibits taking full advantage of these chat logs. In this paper, we present a framework called \textbackslash{}textit\{search, label, and propagate\} (SLP) for bootstrapping intents from existing chat logs using weak supervision. The framework reduces hours to days of labeling effort down to minutes of work by using a search engine to find examples, then relies on a data programming approach to automatically expand the labels. We report on a user study that shows positive user feedback for this new approach to build conversational agents, and demonstrates the effectiveness of using data programming for auto-labeling. While the system is developed for training conversational agents, the framework has broader application in significantly reducing labeling effort for training text classifiers.},
  urldate = {2019-04-16},
  date = {2018-12-14},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language},
  author = {Mallinar, Neil and Shah, Abhishek and Ugrani, Rajendra and Gupta, Ayush and Gurusankar, Manikandan and Ho, Tin Kam and Liao, Q. Vera and Zhang, Yunfeng and Bellamy, Rachel K. E. and Yates, Robert and Desmarais, Chris and McGregor, Blake},
  file = {/home/dimitri/Nextcloud/Zotero/storage/GHW24QAZ/Mallinar et al. - 2018 - Bootstrapping Conversational Agents With Weak Supe.pdf;/home/dimitri/Nextcloud/Zotero/storage/9VGL3XXM/1812.html}
}
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