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The Conversational Assistance Track (CAsT) is a new track for TREC 2019 tofacilitate Conversational Information Seeking (CIS) research and to create alarge-scale reusable test collection for conversational search systems. Thedocument corpus is 38,426,252 passages from the TREC Complex Answer Retrieval(CAR) and Microsoft MAchine Reading COmprehension (MARCO) datasets. Eightyinformation seeking dialogues (30 train, 50 test) are an average of 9 to 10questions long. Relevance assessments are provided for 30 training topics and20 test topics. This year 21 groups submitted a total of 65 runs using varyingmethods for conversational query understanding and ranking. Methods includetraditional retrieval based methods, feature based learning-to-rank, neuralmodels, and knowledge enhanced methods. A common theme through the runs is theuse of BERT-based neural reranking methods. Leading methods also employeddocument expansion, conversational query expansion, and generative languagemodels for conversational query rewriting (GPT-2). The results show a gapbetween automatic systems and those using the manually resolved utterances,with a 35% relative improvement of manual rewrites over the best automaticsystem.

TREC CAsT 2019: The Conversational Assistance Track Overview