Kiyonori Ohtake

Statistical dialog management applied to WFST-based dialog systems

We have proposed an expandable dialog scenario description and platform to manage dialog systems using a weighted finite-state transducer (WFST) in which user concept and system action tags are input and output of the transducer, respectively. In this paper, we apply this framework to statistical dialog management in which a dialog strategy is acquired from a corpus of human-to-human conversation for hotel reservation. A scenario WFST for dialog management was automatically created from an N-gram model of a tag sequence that was annotated in the corpus with Interchange Format (IF). Additionally, a word-to-concept WFST for spoken language understanding (SLU) was obtained from the same corpus. The acquired scenario WFST and SLU WFST were composed together and then optimized. We evaluated the proposed WFST-based statistic dialog management in terms of correctness to detect the next system actions and have confirmed the automatically acquired dialog scenario from a corpus can manage dialog reasonably on the WFST-based dialog management platform.

Modeling spoken decision support dialogue and optimization of its dialogue strategy

This article presents a user model for user simulation and a system state representation in spoken decision support dialogue systems. When selecting from a group of alternatives, users apply different decision-making criteria with different priorities. At the beginning of the dialogue, however, users often do not have a definite goal or criteria in which they place value, thus they can learn about new features while interacting with the system and accordingly create new criteria. In this article, we present a user model and dialogue state representation that accommodate these patterns by considering the users knowledge and preferences. To estimate the parameters used in the user model, we implemented a trial sightseeing guidance system, collected dialogue data, and trained a user simulator. Since the user parameters are not observable from the system, the dialogue is modeled as a partially observable Markov decision process (POMDP), and a dialogue state representation was introduced based on the model. We then optimized its dialogue strategy so that users can make better choices. The dialogue strategy is evaluated using a user simulator trained from a large number of dialogues collected using a trial dialogue system.

Annotating Dialogue Acts to Construct Dialogue Systems for Consulting

This paper introduces a new corpus of consulting dialogues, which is designed for training a dialogue manager that can handle consulting dialogues through spontaneous interactions from the tagged dialogue corpus. We have collected 130 h of consulting dialogues in the tourist guidance domain. This paper outlines our taxonomy of dialogue act annotation that can describe two aspects of an utterances: the communicative function (speech act), and the semantic content of the utterance. We provide an overview of the Kyoto tour guide dialogue corpus and a preliminary analysis using the dialogue act tags.

Weighted Finite State Transducer Based Statistical Dialog Management

We proposed a dialog system using a weighted finite-state transducer (WFST) in which user concept and system action tags are input and output of the transducer, respectively. The WFST-based platform for dialog management enables us to combine various statistical models for dialog management (DM), user input understanding and system action generation, and then search the best system action in response to user inputs among multiple hypotheses. To test the potential of the WFST-based DM platform using statistical models, we constructed a dialog system using a human-to-human spoken dialog corpus for hotel reservation, which is annotated with Interchange Format (IF). A scenario WFST and a spoken language understanding (SLU) WFST were obtained from the corpus and then composed together and optimized. We evaluated the detection accuracy of the system next action tags using Mean Reciprocal Ranking (MRR). Finally, we constructed a full WFST-based dialog system by composing SLU, scenario and sentence generation (SG) WFSTs. Humans read the system responses in natural language and judged the quality of the responses. We confirmed that the WFST-based DM platform was capable of handling various spoken language and scenarios when the user concept and system action tags are consistent and distinguishable.

Dialogue strategy optimization to assist user's decision for spoken consulting dialogue systems

This paper addresses a user model and dialogue state definition in spoken consulting dialogue systems that help users in making decision. When selecting from a set of alternatives, users have various decision criteria for making decision. Users often do not have a definite goal or criteria for selection, and thus they may find not only what kind of information the system can provide but their own preference or factors that they should emphasize. In this paper, we model such consulting dialogue as partially observable Markov decision process (POMDP). We then present an optimization of dialogue strategy to help users make better decisions.

Detecting transliterated orthographic variants via two similarity metrics

We propose a detection method for orthographic variants caused by transliteration in a large corpus. The method employs two similarities. One is string similarity based on edit distance. The other is contextual similarity by a vector space model. Experimental results show that the method performed a 0.889 F-measure in an open test.

Analysis of Selective Strategies to Build a Dependency-Analyzed Corpus

This paper discusses sampling strategies for building a dependency-analyzed corpus and analyzes them with different kinds of corpora. We used the Kyoto Text Corpus, a dependency-analyzed corpus of newspaper articles, and prepared the IPAL corpus, a dependency-analyzed corpus of example sentences in dictionaries, as a new and different kind of corpus. The experimental results revealed that the length of the test set controlled the accuracy and that the longest-first strategy was good for an expanding corpus, but this was not the case when constructing a corpus from scratch.

Dialogue Acts Annotation to Construct Dialogue Systems for Consulting

This chapter introduces a new corpus of consulting dialogues designed for training a dialogue manager that can handle consulting dialogues through spontaneous interactions from the tagged dialogue corpus. We have collected more than 150 hours of consulting dialogues in the tourist guidance domain. This chapter outlines our taxonomy of dialogue act (DA) annotation that can describe two aspects of an utterance: the communicative function (speech act (SA)), and the semantic content of the utterance. We provide an overview of the Kyoto tour guide dialogue corpus and a preliminary analysis using the DA tags.Wealso show a result of a preliminary experiment for SA tagging via Support Vec-tor Machines (SVMs). In addition, we mention the usage of our corpus for the spoken dialogue system that is being developed.

Dialogue act annotation for consulting dialogue corpus

This paper introduces a new corpus of consulting dialogues, which is designed for training a dialogue manager that can handle consulting dialogues through spontaneous interactions from the tagged dialogue corpus. We have collected 130 h of consulting dialogues in the tourist guidance domain. This paper outlines our taxonomy of dialogue act annotation that can describe two aspects of an utterances: the communicative function (speech act), and the semantic content of the utterance. We provide an overview of the Kyoto tour guide dialogue corpus and a preliminary analysis using the dialogue act tags.

A Statistical Approach to Expandable Spoken Dialog Systems using WFSTs

We have proposed an efficient approach to manage a dialog system using a weighted finite-state transducer (WFST) in which users¿ concept and system¿s action tags are input and output of the transducer, respectively. A WFST for dialog management was automatically created using a corpus annotated with inter-change format (IF) consisting of dialog acts and argument which is an interlingua for machine translation. A word-to-concept WFST for spoken language understanding (SLU) was created using the same corpus. The scenario and SLU WFSTs acquired from the corpus were composed together and then optimized. We have confirmed the WFST automatically trained using the annotated corpus can manage dialog reasonably.