Raymond Ptucha

Also published as: Ray Ptucha


2020

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Fully Convolutional ASR for Less-Resourced Endangered Languages
Bao Thai | Robert Jimerson | Raymond Ptucha | Emily Prud’hommeaux
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

The application of deep learning to automatic speech recognition (ASR) has yielded dramatic accuracy increases for languages with abundant training data, but languages with limited training resources have yet to see accuracy improvements on this scale. In this paper, we compare a fully convolutional approach for acoustic modelling in ASR with a variety of established acoustic modeling approaches. We evaluate our method on Seneca, a low-resource endangered language spoken in North America. Our method yields word error rates up to 40% lower than those reported using both standard GMM-HMM approaches and established deep neural methods, with a substantial reduction in training time. These results show particular promise for languages like Seneca that are both endangered and lack extensive documentation.

2018

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Sensing and Learning Human Annotators Engaged in Narrative Sensemaking
McKenna Tornblad | Luke Lapresi | Christopher Homan | Raymond Ptucha | Cecilia Ovesdotter Alm
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

While labor issues and quality assurance in crowdwork are increasingly studied, how annotators make sense of texts and how they are personally impacted by doing so are not. We study these questions via a narrative-sorting annotation task, where carefully selected (by sequentiality, topic, emotional content, and length) collections of tweets serve as examples of everyday storytelling. As readers process these narratives, we measure their facial expressions, galvanic skin response, and self-reported reactions. From the perspective of annotator well-being, a reassuring outcome was that the sorting task did not cause a measurable stress response, however readers reacted to humor. In terms of sensemaking, readers were more confident when sorting sequential, target-topical, and highly emotional tweets. As crowdsourcing becomes more common, this research sheds light onto the perceptive capabilities and emotional impact of human readers.

2017

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Understanding the Semantics of Narratives of Interpersonal Violence through Reader Annotations and Physiological Reactions
Alexander Calderwood | Elizabeth A. Pruett | Raymond Ptucha | Christopher Homan | Cecilia Ovesdotter Alm
Proceedings of the Workshop Computational Semantics Beyond Events and Roles

Interpersonal violence (IPV) is a prominent sociological problem that affects people of all demographic backgrounds. By analyzing how readers interpret, perceive, and react to experiences narrated in social media posts, we explore an understudied source for discourse about abuse. We asked readers to annotate Reddit posts about relationships with vs. without IPV for stakeholder roles and emotion, while measuring their galvanic skin response (GSR), pulse, and facial expression. We map annotations to coreference resolution output to obtain a labeled coreference chain for stakeholders in texts, and apply automated semantic role labeling for analyzing IPV discourse. Findings provide insights into how readers process roles and emotion in narratives. For example, abusers tend to be linked with violent actions and certain affect states. We train classifiers to predict stakeholder categories of coreference chains. We also find that subjects’ GSR noticeably changed for IPV texts, suggesting that co-collected measurement-based data about annotators can be used to support text annotation.

2016

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Generating Clinically Relevant Texts: A Case Study on Life-Changing Events
Mayuresh Oak | Anil Behera | Titus Thomas | Cecilia Ovesdotter Alm | Emily Prud’hommeaux | Christopher Homan | Raymond Ptucha
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology

2015

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An Analysis of Domestic Abuse Discourse on Reddit
Nicolas Schrading | Cecilia Ovesdotter Alm | Ray Ptucha | Christopher Homan
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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#WhyIStayed, #WhyILeft: Microblogging to Make Sense of Domestic Abuse
Nicolas Schrading | Cecilia Ovesdotter Alm | Raymond Ptucha | Christopher Homan
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies