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Creators/Authors contains: "Perez-Rosas, Veronica"

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  1. ; ; ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available January 1, 2026
  2. ; ; ; (, ELRA and ICCL)
    In this paper, we study the problem of multi-reward reinforcement learning to jointly optimize for multiple text qualities for natural language generation. We focus on the task of counselor reflection generation, where we optimize the generators to simultaneously improve the fluency, coherence, and reflection quality of generated counselor responses. We introduce two novel bandit methods, DynaOpt and C-DynaOpt, which rely on the broad strategy of combining rewards into a single value and optimizing them simultaneously. Specifically, we employ non-contextual and contextual multi-arm bandits to dynamically adjust multiple reward weights during training. Through automatic and manual evaluations, we show that our proposed techniques, DynaOpt and C-DynaOpt, outperform existing naive and bandit baselines, showcasing their potential for enhancing language models. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; (, Nature Human Behaviour)
    Full Text Available 
  4. ; ; ; (, Association for Computational Linguistics)
    Full Text Available 
  5. ; ; ; (, IEEE intelligent systems)
    We explore the use of longitudinal dialog data for two dialog prediction tasks: next message prediction and response time prediction. We show that a neural model using personal data that leverages a combination of message content, style matching, time features, and speaker attributes leads to the best results for both tasks, with error rate reductions of up to 15\% compared to a classifier that relies exclusively on message content and to a classifier that does not use personal data. 
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    Full Text Available 
  6. ; ; ; (, Proceedings of the 20th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing))
    We examine a large dialog corpus obtained from the conversation history of a single individual with 104 conversation partners. The corpus consists of half a million instant messages, across several messaging platforms. We focus our analyses on seven speaker attributes, each of which partitions the set of speakers, namely: gender; relative age; family member; romantic partner; classmate; co-worker; and native to the same country. In addition to the content of the messages, we examine conversational aspects such as the time messages are sent, messaging frequency, psycholinguistic word categories, linguistic mirroring, and graph-based features reflecting how people in the corpus mention each other. We present two sets of experiments predicting each attribute using (1) short context windows; and (2) a larger set of messages. We find that using all features leads to gains of 9-14% over using message text only. 
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    Accepted Manuscript Full Text Available