Overview - Conversational Assistance 2020

Proceedings | Data | Results | Runs | Participants

CAsT 2020 is the second year of the Conversational Assistance Track and builds on the lessons from the first year. Teams tried a wide range of techniques to address conversational search challenges. Some methods used proven techniques such as query difficulty prediction and query expansion. Given the text understanding challenges in the task, teams also used traditional NLP models that incorporate coreference resolution. One important development was the application of generative query models and ranking models using pre-trained neural language models. The results showed that both traditional and neural techniques provided complementary effectiveness.

Track coordinator(s):

• Jeffrey Dalton, University of Glasgow
• Chenyan Xiong, Microsoft Research
• Jamie Callan, Carnegie Mellon University

Track Web Page: https://www.treccast.ai/

---