Sara Evensen


2020

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Ruler: Data Programming by Demonstration for Document Labeling
Sara Evensen | Chang Ge | Cagatay Demiralp
Findings of the Association for Computational Linguistics: EMNLP 2020

Data programming aims to reduce the cost of curating training data by encoding domain knowledge as labeling functions over source data. As such it not only requires domain expertise but also programming experience, a skill that many subject matter experts lack. Additionally, generating functions by enumerating rules is not only time consuming but also inherently difficult, even for people with programming experience. In this paper we introduce Ruler, an interactive system that synthesizes labeling rules using span-level interactive demonstrations over document examples. Ruler is a first-of-a-kind implementation of data programming by demonstration (DPBD). This new framework aims to relieve users from the burden of writing labeling functions, enabling them to focus on higher-level semantic analysis, such as identifying relevant signals for the labeling task. We compare Ruler with conventional data programming through a user study conducted with 10 data scientists who were asked to create labeling functions for sentiment and spam classification tasks. Results show Ruler is easier to learn and to use, and that it offers higher overall user-satisfaction while providing model performances comparable to those achieved by conventional data programming.

2018

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HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments
Akari Asai | Sara Evensen | Behzad Golshan | Alon Halevy | Vivian Li | Andrei Lopatenko | Daniela Stepanov | Yoshihiko Suhara | Wang-Chiew Tan | Yinzhan Xu
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)