@inproceedings{12f45bbb0cab4033b48f786564a47c11,
title = "Evidentials in causal premise semantics: theoretical and experimental investigation",
abstract = "We formalize the causal component of Davis & Hara{\textquoteright}s (2014) analysis of Japanese evidentiality, which defines “indirect evidence” as an observation of the effect state of the cause-effect dependency. The analysis correctly predicts that uttering p-youda only commits the speaker to {\textquoteleft}if p, q must be true{\textquoteright} but not to the prejacent p, and successfully derives the asymmetry between the prejacent p and the evidence source q. Also, the results of the rating study and the corpus study show that the interpretation and the distribution of evidentials are subject to the cause-effect dependencies.",
keywords = "Causal network, Causal premise semantics, Causality, Corpus study, Evidentiality, Implicature, Modality, Naturalness rating experiment",
author = "Yurie Hara and Naho Orita and Hiromu Sakai",
note = "Funding Information: Acknowledgement. This research was supported by the project “Cognitive Neuroscience of Linguistic Variation in Pragmatic Inference” at the National Institute of Japanese Language and Linguistics (PI: Hiromu Sakai, Waseda University). Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 9th JSAI International Symposium on Artificial Intelligence, JSAI-isAI 2017 ; Conference date: 13-11-2017 Through 15-11-2017",
year = "2018",
doi = "10.1007/978-3-319-93794-6_20",
language = "English",
isbn = "9783319937939",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "282--298",
editor = "Koji Mineshima and Kazuhiro Kojima and Ken Satoh and Sachiyo Arai and Daisuke Bekki and Yuiko Ohta",
booktitle = "New Frontiers in Artificial Intelligence - JSAI-isAI Workshops, JURISIN, SKL, AI-Biz, LENLS, AAA, SCIDOCA, kNeXI, Revised Selected Papers",
}