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Coreference-aware Surprisal Predicts Brain Response
Recent evidence supports a role for coreference processing in guiding human expectations about upcoming words during reading, based on covariation between reading times and word surprisal estimated by a coreference-aware semantic processing model (Jaffe et al. 2020). The present study reproduces and elaborates on this finding by (1) enabling the parser to process subword information that might better approximate human morphological knowledge, and (2) extending evaluation of coreference effects from self-paced reading to human brain imaging data.
Coreference information guides human expectations during natural reading
Models of human sentence processing effort tend to focus on costs associated with retrieving structures and discourse referents from memory (memory-based) and/or on costs associated with anticipating upcoming words and structures based on contextual cues (expectation-based) (Levy, 2008).
Learning to Answer Subjective, Specific Product-Related Queries using Customer Reviews by Adversarial Domain Adaptation
Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that meet their personal needs and preferences.
Combining CNNs and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System
For medical students, virtual patient dialogue systems can provide useful training opportunities without the cost of employing actors to portray standardized patients.
A Corpus of Word-Aligned Asked and Anticipated Questions in a Virtual Patient Dialogue System
We present a corpus of virtual patient dialogues to which we have added manually annotated gold standard word alignments.
