Annotating a Driving Experience Corpus with Behavior and Subjectivity

1 Annotating a Driving Experience Corpus with Behavior and Subjectivity Ritsuko Iwai1,2, Daisuke Kawahara1, Takatsune Kumada1,2, Sadao Kurohashi1 1Graduate School of Informatics, Kyoto University Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan 2RIKEN BSI TOYOTA Collaboration Center, RIKEN Hirosawa, Wako-shi, Saitama, 351-198, Japan ritsuko.iwai@riken.jp, {dk, t.kumada, kuro}@i.kyoto-u.ac.jp Abstract Personality is the important internal framework that we use when we communicate with the others; thus, it is critical for vehicle-driver communication. Evaluating an individual’s personality requires associating behavior and subjectivity. To develop the vehicle-driver communication system that will be most applicable to actual driving settings, a corpus is necessary that includes colloquial and daily experiential expressions along with their emotions, polarity, and sentiments. In addition, the corpus must include a wide range of subjective measures, such as human judgements, perceptions, and cognitions during car driving. Thus, we construct a driving experience corpus (DEC) that constitutes 253 blog articles (7,831 sentences) with the following four manually annotated tags: (1) driving experience (DE), (2) other’s behavior (OB), (3) self-behavior (SB) and (4) subjectivity (SJ). In this paper, we describe the guidelines, corpus specification and agreement analysis between annotators. We identified three difficulties: the extended self, important information, and voice in mind. We conducted automatic annotation experiment on the corpus using Conditional Random Fields (CRF). The results indicated F- Scores of .768, .478, .534, and .749 for DE, OB, SB, and SJ, respectively, on the test set. Our error analysis reveals difficulties in interpreting nominatives and recognizing facts. 1 Introduction Given the drastic improvements in the field of machine learning, agents and robots, systems share or play a role in tasks that used to be fully controlled by humans. Many companies are, for example, competing to realize automatic driving and are conducting test driving on roads. However, it is critical that systems understand individual human differences and predict their behavior, especially under the condition that a miss may bring about a serious fatal accident. Systems must catch the attention of the driver and make him take the appropriate action. Thus, it is important for systems to communicate with their users in accordance with their individual differences. Tapus et al. (2008), for example, demonstrated that participants were more engaged in rehabilitation exercises with robots that behaved and gave messages similarly to the personalities of the participants. We regard it as important that a system communicates with its user via a language based on his/her personality to bridge the inconsistencies between the system’s requirements and the user’s attention and actions. A personality is defined as an “abstraction used to describe and explain the coherent patterning over time and space of affects, cognitions, desires and the resulting behaviors that an individual experiences and expresses” (Revelle & Condon, 2015, p. 70). We consciously or unconsciously evaluate others’ personalities, predict their next behaviors or utterances and sometimes adjust our communication style as a result. Thus, PACLIC 32 222 32nd Pacific Asia Conference on Language, Information and Computation Hong Kong, 1-3 December 2018 Copyright 2018 by the authors 2 understanding the human personality …

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