Daniel Chen


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AutoAspect: Automatic Annotation of Tense and Aspect for Uniform Meaning Representations
Daniel Chen | Martha Palmer | Meagan Vigus
Proceedings of The Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop

We present AutoAspect, a novel, rule-based annotation tool for labeling tense and aspect. The pilot version annotates English data. The aspect labels are designed specifically for Uniform Meaning Representations (UMR), an annotation schema that aims to encode crosslingual semantic information. The annotation tool combines syntactic and semantic cues to assign aspects on a sentence-by-sentence basis, following a sequence of rules that each output a UMR aspect. Identified events proceed through the sequence until they are assigned an aspect. We achieve a recall of 76.17% for identifying UMR events and an accuracy of 62.57% on all identified events, with high precision values for 2 of the aspect labels.


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Linguist vs. Machine: Rapid Development of Finite-State Morphological Grammars
Sarah Beemer | Zak Boston | April Bukoski | Daniel Chen | Princess Dickens | Andrew Gerlach | Torin Hopkins | Parth Anand Jawale | Chris Koski | Akanksha Malhotra | Piyush Mishra | Saliha Muradoglu | Lan Sang | Tyler Short | Sagarika Shreevastava | Elizabeth Spaulding | Testumichi Umada | Beilei Xiang | Changbing Yang | Mans Hulden
Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

Sequence-to-sequence models have proven to be highly successful in learning morphological inflection from examples as the series of SIGMORPHON/CoNLL shared tasks have shown. It is usually assumed, however, that a linguist working with inflectional examples could in principle develop a gold standard-level morphological analyzer and generator that would surpass a trained neural network model in accuracy of predictions, but that it may require significant amounts of human labor. In this paper, we discuss an experiment where a group of people with some linguistic training develop 25+ grammars as part of the shared task and weigh the cost/benefit ratio of developing grammars by hand. We also present tools that can help linguists triage difficult complex morphophonological phenomena within a language and hypothesize inflectional class membership. We conclude that a significant development effort by trained linguists to analyze and model morphophonological patterns are required in order to surpass the accuracy of neural models.


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Proceedings of the Natural Legal Language Processing Workshop 2019
Nikolaos Aletras | Elliott Ash | Leslie Barrett | Daniel Chen | Adam Meyers | Daniel Preotiuc-Pietro | David Rosenberg | Amanda Stent
Proceedings of the Natural Legal Language Processing Workshop 2019