In recent years, the importance of dialogue understanding systems has been increasing. However, it is difficult for computers to deeply understand our daily conversations because we frequently use emotional expressions in conversations. This is partially because there are no large-scale corpora focusing on the detailed relationships between emotions and utterances. In this paper, we propose a dialogue corpus constructed based on our knowledge base, called the Japanese Feature Change Knowledge Base (JFCKB). In JFCKB and the proposed corpus, the feature changes (mainly emotions) of arguments in event sentences (or utterances) and those of the event sentence recognizers (or utterance recognizers) are associated with the event sentences (or utterances). The feature change information of arguments in utterances and those of the utterance recognizers, replies to the utterances, and the reasonableness of the replies were gathered through crowdsourcing tasks. We conducted an experiment to investigate whether a machine learning method can recognize the reasonableness of a given conversation. Experimental result suggested the usefulness of our proposed corpus.
@InProceedings{NAKAMURA18.346, author = {Tetsuaki Nakamura and Daisuke Kawahara}, title = "{JDCFC: A Japanese Dialogue Corpus with Feature Changes}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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