William Li


2020

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Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution
David Q. Sun | Hadas Kotek | Christopher Klein | Mayank Gupta | William Li | Jason D. Williams
Proceedings of the 28th International Conference on Computational Linguistics

This paper develops and implements a scalable methodology for (a) estimating the noisiness of labels produced by a typical crowdsourcing semantic annotation task, and (b) reducing the resulting error of the labeling process by as much as 20-30% in comparison to other common labeling strategies. Importantly, this new approach to the labeling process, which we name Dynamic Automatic Conflict Resolution (DACR), does not require a ground truth dataset and is instead based on inter-project annotation inconsistencies. This makes DACR not only more accurate but also available to a broad range of labeling tasks. In what follows we present results from a text classification task performed at scale for a commercial personal assistant, and evaluate the inherent ambiguity uncovered by this annotation strategy as compared to other common labeling strategies.

2013

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Making Speech-Based Assistive Technology Work for a Real User
William Li | Don Fredette | Alexander Burnham | Bob Lamoureux | Marva Serotkin | Seth Teller
Proceedings of the Fourth Workshop on Speech and Language Processing for Assistive Technologies

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Probabilistic Dialogue Modeling for Speech-Enabled Assistive Technology
William Li | Jim Glass | Nicholas Roy | Seth Teller
Proceedings of the Fourth Workshop on Speech and Language Processing for Assistive Technologies