The Second AAAI Workshop on Reasoning and Learning for Human-Machine Dialogues (DEEP-DIAL 2019)
Partially sponsored by AI Journal
Natural conversation is a hallmark of intelligent systems. Unsurprisingly, dialog systems have been a key sub-area of AI for decades. Their most recent form, chatbots, which can engage people in natural conversation and are easy to build in software, have been in the news a lot lately. There are many platforms to create dialogs quickly for any domain, based on simple rules. Further, there is a mad rush by companies to release chatbots to show their AI capabilities and gain market valuation. However, beyond basic demonstration, there is little experience in how they can be designed and used for real-world applications that need decision making under constraints (e.g., sequential decision making). The workshop will thus be timely in helping chatbots realize their full potential.
Furthermore, there is an upcoming interest and need for innovation in Human-Technology-Interaction, as addressed in the context of Companion Technology. Here, the aim is to implement technical systems that smartly adapt their functionality to their users’ individual needs and requirements and are even able to solve problems in close co-operation with human users. To this end, they need to enter into a dialog and convincingly explain their suggestions and decision-making behavior.
From research side, statistical and machine learning methods are well entrenched for language understanding and entity detection. However, the wider problem of dialog management is unaddressed with mainstream tools supporting rudimentary rule-based processing. There is an urgent need to highlight the crucial role of reasoning methods such as constraints satisfaction, planning and scheduling, and learning working together with them, that can play to build an end-to-end conversation system that evolves over time. From the practical side, conversation systems need to be designed for working with people in a manner that they can explain their reasoning, convince humans about making choices among alternatives, and stand up to ethical standards demanded in real life settings.
Thus, recognizing the need for more research attention, the proposers of the current workshop organized the highly successful DEEP-DIAL18 workshop at AAAI 2018 (Photos). The event brought together over 100 AI researchers from around the world to discuss a bouquet of research topics around human-machine dialogs. The program included 4 invited talks, 7 reviewed full paper presentations and 4 lightening talks accompanied by posters, and a topical panel discussion. Some glimpses from last year can be found here.
Topics of Interest Inlcude:
- Design considerations for dialog systems
- Evaluation of dialog systems, metrics
- Open domain dialog and chat systems
- Task-oriented dialogs
- Style, voice and personality in spoken dialogue and written text
- Novel Methods for NL Generation for dialogs
- Early experiences with implemented dialog systems
- Mixed-initiative dialogs where a partner is a combination of agent and human
- Hybrid methods
- Domain model acquisition, especially from unstructured text
- Plan recognition in natural conversation
- Planning and reasoning in the context of dialog systems
- Handling uncertainity
- Optimal dialog strategies
- Learning to reason
- Learning for dialog management
- End2end models for conversation
- Explaining dialog policy
- Responsible chatting
- Ethical issues with learning and reasoning in dialog systems
- Corpora, Tools and Methodology for Dialogue Systems
- Securing one’s chat
The intended audience students, academic researchers and practitioners with an industrial background from the AI sub-areas of dialog systems, learning, reasoning, planning, HCI, ethics and knowledge representation.
Manuscripts due: November 5, 2018
Notification of acceptance: November 17, 2018
Camera-ready manuscript: November 26, 2018
Workshop: January 27, 2019
Papers must be formatted in AAAI two-column, camera-ready style (AAAI style files are at: http://www.aaai.org/Publications/Templates/AuthorKit18.zip).
Regular research papers, which present a significant contribution, may be no longer than 7 pages, where page 7 must contain only references, and no other text whatsoever.
Short papers, which describe a position on the topic of the workshop or a demonstration/tool, may be no longer than 4 pages, references included.
Submission Site: https://easychair.org/conferen
- Biplav Srivastava , IBM Research, USA
- Susanne Biundo , University of Ulm, Germany
- Ullas Nambiar , Zenlabs , Zensar, India
- Imed Zitouni , Microsoft AI+R, USA
- Pavan Kapanipathi, IBM TJ Watson Research Center
- Mitesh Vasa, IBM
- Matthew Peveler, Rensselaer Polytechnic Institute
- Q. Vera Liao, IBM
- Madian Khabsa, Apple
- Debdoot Mukherjee, Myntra
- Seyyed Hadi Hashemi, University of Amsterdam
- Sumant Kulkarni, Zenlabs, Zensar Technologies
- Julia Kiseleva, Microsoft Research AI
- Kyle Williams, Microsoft
- Rahul Jha, University of Michigan
- Srikanth Tamilselvam, IBM Global Business Services
- Adi Botea, IBM
- Walter Lasecki, University of Michigan, Computer Science & Engineering
- Atriya Sen, Rensselaer Polytechnic Institute
- Chinnadhurai Sankar and Sujith Ravi. Conditional Utterance Generation With Discrete Dialog Attributes In Open-Domain Dialog Systems
- Parag Agrawal, Anshuman Suri and Tulasi Menon. A Trustworthy, Responsible and Interpretable System to Handle Chit-Chat in Conversational Bots http://arxiv.org/abs/1811.07600
- Ryo Nakamura, Katsuhito Sudoh, Koichiro Yoshino and Satoshi Nakamura. Another Diversity-Promoting Objective Function for Neural Dialogue Generation,?https://arxiv.org/abs/1811.08100v1
- Philip Cohen. Back to the Future for Dialogue Research -- A Position Paper.
- Mengting Wan and Xin Chen. Beyond "How may I help you?'': Assisting Customer Service Agents with Proactive Responses, http://arxiv.org/abs/1811.10686.
- Libby Ferland, Thomas Huffstutler, Jacob Rice, Joan Zheng, Shi Ni and Maria Gini. Evaluating Older Users' Experiences with Commercial Dialogue Systems: Implications for Future Design and Development
- Amit Sangroya, Aishwarya Chhabra and C. Anantaram. Learning Latent Beliefs and Performing Epistemic Reasoning for Efficient and Meaningful Dialog Management, http://arxiv.org/abs/1811.10238
- Hisao Katsumi, Takuya Hiraoka, Koichiro Yoshino, Kazeto Yamamoto, Shota Motoura, Kunihiko Sadamasa and Satoshi Nakamura, Optimization of Information-Seeking Dialogue Strategy for Argumentation-Based Dialogue System, http://arxiv.org/abs/1811.10728
- Teakgyu Hong, Oh-Woog Kwon and Young-Kil Kim, An End-to-End Trainable Task-oriented Dialog System with Human Feedback
- Trung Ngo Trong and Kristiina Jokinen, What Should We Talk about? – Models for Topics, Laughter and Body Movements in First Encounters
- Xiang Kong, Bohan Li, Graham Neubig and Eduard Hovy, An Adversarial Approach to High-Quality, Sentiment-Controlled Neural Dialogue Generation
- Adi Botea, Christian Muise, Shubham Agarwal, Oznur Alkan, Ondrej Bajgar, Elizabeth Daly, Akihiro Kishimoto, Luis Lastras, Radu Marinescu, Josef Ondrej, Pablo Pedemonte and Miroslav Vodolan, Generating Dialogue Agents via Automated Planning
Demo contest/student support
Call for Applications for Support to Attend the Second AAAI Workshop on Reasoning and Learning for Human-Machine Dialogues (DEEP-DIAL 2019)
- [Preferred] By demonstrating a TAsk-oriented Data-driven conversation bot/agent (called TADBot or just chatbot) that works with open data. A 1-page description needs to be submitted. See further details about demonstration below, or
- submitting a 1-page description of your research interest relevant to conversation systems in general and the demonstration setting in particular.
- Ability of chatbot to answer queries related to subject matter (e.g., train stations in preferred scenario)
- Ability of chatbot to handle users of different backgrounds leading to dialogs of different lengths (e.g., exact terms, partial matches, switching intents)
- Ability of chatbot to handle multiple turns
- Ability to handle abusive and discriminatory language
- Response time and error handling
- Any special feature. E.g., Ability to handle mixture of languages, showing multi-modal response like maps or graphs when appropriate
- Team name and members. Identify student members and also indicate if support is needed for student to attend DEEP-DIAL 19 workshop
- Information about open dataset
- A demonstration video of using the chatbot
- Link to source code on github
- URL of actual chatbot that can be tested
- Details of implemented approach. Link to a paper is allowed.
- Source code should be made available on github
- Chatbot should be available publicly for demonstration for at least 3 months. For example, hosted on any cloud platform.
- Data used by chatbot should be open and hence downloadable for free.
- Prize-based support: 1st prize - $700, 2nd prize - $500
- Student support (up to two) - $ 500
- Loebner Challenge, https://en.wikipedia.org/wiki/Loebner_Prize
- Conversational Intelligence challenge, http://convai.io/
- Open Data, https://en.wikipedia.org/wiki/Open_data
- United Nations Standard Products and Services Code® (UNSPSC®), http://www.unspsc.org/
- NYC Transit Subway Entrance And Exit Data, Data: https://data.ny.gov/Transportation/NYC-Transit-Subway-Entrance-And-Exit-Data/i9wp-a4ja ; Data dictionary: https://data.ny.gov/api/assets/6D60FF9E-9143-40CF-8FC9-9D237C6AC864?download=true
- P. Pasupat and P. Liang. Compositional semantic parsing on semi-structured tables. In Proc. of ACL, 2015
- Till Haug, Octavian-Eugen Ganea and Paulina Grnarova, Neural Multi-Step Reasoning for Question Answering on Semi-Structured Tables, arXiv:1702.06589v2, March, 2018,
- Xuchen Yao, Benjamin Van Durme Information Extraction over Structured Data: Question Answering with Freebase, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics , pages 956–966, Baltimore, Maryland, USA, June 23-25 2014
- Yanchao Hao, Yuanzhe Zhang, Kang Liu, Shizhu He, Zhanyi Liu, Hua Wu, and Jun Zhao, An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global, Knowledge, Proc. ACL, 2017.
- Biplav Srivastava, Decision-support for the Masses by Enabling Conversations with Open Data, At https://arxiv.org/abs/1809.06723, Sep 2018.