A human-human train timetable dialogue corpus

Abstract This paper describes progress in a development of the human-human dialogue corpus. The corpus contains transcribed user’sphone calls to a train timetable information center. The phonecalls consist of inquiries regarding their train traveler’s plans.The corpus is based on dialogues’s transcription of user’s in-quiries that were previously collected for a train timetable in-formation center. We enriched this transcription by dialogue acttags. The dialogue act tags comprehend abstract semantic an-notation. The corpus comprises a recorded speech of both op-erators and users, orthographic transcription, normalized tran-scription, normalized transcription with named entities, and di-alogue act tags with abstract semantic annotation. A combina-tion of a dialogue act tagset and a abstract semantic annotationis proposed. A technique of dialogue act tagging and abstractsemantic annotation is described and used. 1. Introduction Statistical approach to spoken language understanding (SLU)and dialogue management (DM) gains on popularity. Whereastraditional methods use hand-crafted rules, statistical techniqueslearn from data. As a result, we need a suitable spoken dialoguecorpus to train a statistical model of a spoken dialogue system(SDS). Although Stuttle in [1] argues that for successful incorpo-ration of techniques in to SDS is needed a specially collectedhuman-human (HH) corpus, we believe that regular HH cor-pus that is reasonably large can suppress all disadvantages men-tioned in [1], e.g. almost errorless communication in terms ofASR. Therefore, we proceeded to extend our current dialoguecorpus [2] in terms of coverage of sentences and modify se-mantic annotation to lower time needed to semantically anno-tate whole dialogue. Furthermore, we decided to add dialogueact tags to our corpus in order to study divers user’s dialoguestrategies.We acquired the HH dialogue corpus because it does notsuffer from ﬁxed policy of a human-computer (HC) dialoguesystem. A dialogue policy is a mapping from dialogue states(situations) to system actions. From HC dialogue corpus witha ﬁxed policy, the statistical methods are not able to learn a ro-bust model. Consequently, we would not be able to successfullyoperate dialogues. [1]In addition, general HH dialogues are structurally similarto mixed-initiative (user-driven) dialogues. Intuitively, in HHdialogues there is no restriction on participant roles, e.g. who ismoving a conversation ahead at a given point or selecting a newtopic for conversation. Likewise, in mixed-initiative dialoguesusers have weaker constraints about what can be said. For theseresons, DM is much more complicated in mixed-initiative SDSthan in system-initiative (system-driven) SDS. Our objective isto prepare a spoken dialogue corpus that can be used for devel-oping more sophisticated dialogue management techniques.To cover needs of both SLU and DM, we need to providenot only dialogue management related information (dialogueacts) but also need a structured meaning of these moves. Morespeciﬁcally, we need semantic information related to given ap-plication domain. We use an abstract semantic annotation ac-cording to [3]; this annotation is necessary for all utterances.Important question is how to obtain this annotation. The anno-tation has to be simple and unambiguous for sake of quality andconsistency.The rest of the paper is organized as follows. Section 2 de-scribes a train timetable dialogue corpus. Section 3 details ourdialogue act tagging scheme and the three dimension of our dia-logue act tagset. Section 4 elaborates on the annotation process.Finally, section 5 concludes the paper.