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

TREC 2019 的新领域 Conversational Assistance Track 旨在推进会话式信息检索研究并创建一个可重复使用的大规模测试集；使用 CAR 和 MARCO 数据集中的 38426252 个段落作为文献语料库，进行了 80 个信息检索对话（30 个训练集和 50 个测试集）。