Shohei Tanaka

Nara Institute of Science and Technology(NAIST)
Takayama-cho 8916-5
Ikoma-shi, Nara-kenJapantanaka.shohei.tj7@is.naist.jp
https://shohei-ta-ds7.github.io/

1

Research interests

My current research interest is thoughtful dialogue
agents that respond with thoughtful actions to ambiguous user requests. The agents assume to be used in tasks
that users sometimes can not clearly verbalize their requests such as sightseeing navigation or hotel reception.
I also interested in how dialogue systems use external
knowledge such as event causality. I have been studying
these topics to develop a dialogue agent that can support
users in diverse situations as well as thoughtful humanconcierges.
1.1

Level
Q1
Q2
Q3
Q4

Definition
The actual, but unexpressed request
The conscious, within-brain description of therequest
The formal statement of the request
The request as presented to the dialogue agent

Table 1: Levels of ambiguity in requests (queries)(Taylor, 1968)

Ambiguous user requests and thoughtful system
actions

Existing task-oriented dialogue systems assume that user
intentions are clarified and uttered in an explicit manner;
however, users often do not know what they want to request. User requests in such cases are ambiguous. Taylor(1968) categorizes user states in information search into
four levels according to their clarity, as shown in Table
1. Most of the existing task-oriented dialogue systems(Madotto et al., 2018) convert explicit user requests (Q3)
into machine readable expressions (Q4). Future dialoguesystems need to take appropriate actions even in situations such as Q1 and Q2, where the users are not able toclearly verbalize their requests (Yoshino et al., 2017).
Figure 1 shows an example dialogue between a user
and a developed dialogue agent. The user utterance “It ishot today!” is not verbalized as a request for a specificfunction. The dialogue agent responds with a thoughtful action, “Shall I search for a cafe around here?” andsearches for a cafe. I collected a corpus and developed amodel that classifies ambiguous user requests into corresponding system actions (Tanaka et al., 2021). The corpus collected in our study assumes cases where the user
requests are ambiguous, such as Q1 and Q2 in Table 1.
However, the dialogue agent decides action candidates
based on only one-turn user request. The agent can notdecide one action with multi-turn dialogue when multiple actions could be regarded as thoughtful. For the user
request in Figure 1, not only “cafe” but also “rest area” isFigure 1: Example of thoughtful dialogue
thoughtful as system action. As future work, I will construct a agent that can deal with such cases by collecting
a multi-turn corpus and updating the system architecture.
1.2 Conversational response re-ranking based onevent causality
While a variety of dialogue models such as the neural conversational model (NCM) (Vinyals and Le, 2015)
have been researched widely, such dialogue models often
generate simple and dull responses due to the limitationof their ability to take dialogue context into account. It
is very difficult for these models to generate coherent responses to a dialogue history.
I tackled this problem with a new architecture by incorporating event causality relations between response candidates and a dialogue history (Tanaka et al., 2019). Typical event causality relations are cause-effect relations between two events, such as “be stressed out" precedes “relieve stress." The proposed method could select coherent
and diverse responses to some extent.
As future work, I will apply event causality to taskoriented dialogue systems to estimate user intentions
more accurately.

2

Spoken dialogue system (SDS) research

In this section, I will briefly present my opinions andpoint of views of current and future SDSs research forthe following questions.
2.1

Where do you think the field of dialogue
research will be in 5 to 10 years?

Tasks in more diverse and real situations become the research trends. Current task-oriented dialogue systems assume specific situations such as movie recommendationor sightseeing navigation. I think practical dialogue systems as partners for people need to work well in more diverse situations. Conventional end2end training does notwork well because the task range is too diverse. The system have to imitate the deduction or estimation processof human beings with commonsense knowledge.
2.2

What are the most important things for users ofSDSs

I think that users regards accuracy of SDS responses touser requests as most important point. Although neural network based systems can generate responses indiverse situations, their responses sometimes does notmake sense. The users will stop use the systems in suchcases. Then, SDS developers have to ensure accuracy
even if the developed systems could not deal with diverse
situations.
2.3

Is there a difference between SDS research inacademia and industry?

One of the most different point between academia and industry is the purpose for developing SDSs. I have been
studying SDSs for publishing papers which bring the related areas a profit. The system need to be for not making
money within few years, but changing the world whichSDSs are used in after ten years. If I develop SDSs ina company, the developed system bring specific users aprofit. The development has also important value for me
because I can immediately understand that I changed theworld even if the area is spedific.

3

Suggested topics for discussion

In this section, I suggest three topics for discussion in thediscussion panels during the event.
• Corpus collection: What is the best method to collect a corpus for difficult tasks such as suggesting
thoughtful actions to ambiguous user requests. Simple WoZ method will fail because the tasks are too
difficult for general people.
• How human beings estimate intentions of a person
who they are talking to. What kind of knowledge isused in the process?

• Will dialogue systems be able to work as well as
human concierges in sightseeing navigation or hotel
reception within ten years? What problems should
be solved to establish the goal?

References
Andrea Madotto, Chien-Sheng Wu, and Pascale Fung.
2018. Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog
Systems. In ACL. pages 1468–1478.
Shohei Tanaka, Koichiro Yoshino, Katsuhito Sudoh, andSatoshi Nakamura. 2019. Conversational response reranking based on event causality and role factored tensor event embedding. In NLP for ConvAI. Florence,Italy, pages 51–59.
Shohei Tanaka, Koichiro Yoshino, Katsuhito Sudoh, andSatoshi Nakamura. 2021. ARTA: Collection and Classification of Ambiguous Requests and Thoughtful Actions. In SIGDIAL. Singapore.
Robert S. Taylor. 1968. Question-negotiation and information seeking in libraries. College & Research Libraries 29(3):178–194.
Oriol Vinyals and Quoc V. Le. 2015. A Neural Conversational Model. In ICML.
Koichiro Yoshino, Yu Suzuki, and Satoshi Nakamura.
2017. Information navigation system with discovering user interests. In SIGDIAL. Saarbrücken, Germany,pages 356–359.

Biographical sketch
Shohei Tanaka earned B.E. fromNagoya Institute of Technology
at 2018, M.E. from Nara Institute of Science and Technology at 2020. He has been aPh.D. candidate at Nara Institute of Science and Technology
since 2020. He has been engaged in research on natural language processing, especially on
dialogue systems and event causality. He received a best
paper award of the first workshop on NLP for conversational AI. He is a member of the Association for Computational Linguistics.