Online, November 19-20 (exact details TBD)
- The 1st edition of the workshop was co-located with International Conference on the Theory of Information Retrieval (ICTIR 2017).
- The 2nd edition of the workshop was co-located with the Conference on Emperical Methods in Natural Language Processing (EMNLP 2018).
- The 3rd edition (special half-day edition) of the workshop was co-located with The Web Conference 2019 (TheWebConf 2019).
- The 4th edition of the workshop was co-located with the International Joint Conoference on Artificial Intelligence (IJCAI 2019).
More and more information is found and consumed in a conversational form
rather than using traditional search engines. Chatbots, personal assistants
in our phones and eyes-free devices are being used increasingly more for
different purposes, including information retrieval and exploration. On the
other side, information retrieval empowers dialogue systems to answer
questions and to get context for assisting the user in her tasks. With the
recent success of deep learning in different areas of natural language
processing, this appears to be the right foundation to power search
conversationalization. Yet, we believe more can be done for theory and
practice of conversation-based search and search-based dialogues.
This workshop aims to bring together ML and NLP researchers on one hand
and Web Search/IR specialists on the other hand to lay the groundwork for collaboration
on search-oriented conversational AI and establish future directions.
Topics of Interest
- Surfacing search results in form of a dialogue (how to present information that search gives us in a form of a dialogue? Which model to use for dialogue-state tracking?
- Evaluation of search-oriented conversational AI: despite early attempts at computing dialogue system’s quality in a scaleable way, this is still an open challenge.
- From conversational AI to personal assistants (how to maintain a stable and consistent assistant behavior)
- Personalization for conversational AI and for its evaluation (what aspects and knowledge is needed for personalized experiences)
- Deep Learning for conversational search: how to make it more grounded to the knowledge of the Web
- Reinforcement Learning for conversational AI
- Voice as input (voice interactions with a personal assistant: how it will affect existing models?)
- Specialized applications and uses cases for conversational search (specialized domains in health, finance, travel, etc…)
- Balance and bias for more inclusive conversational AI systems