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）。

TREC CAsT 2019：对话辅助任务概述

TREC CAsT 2019: The Conversational Assistance Track Overview

The Deep Learning Track is a new track for TREC 2019, with the goal of
studying ad hoc ranking in a large data regime. It is the first track with
large human-labeled training sets, introducing two sets corresponding to two
tasks, each with rigorous TREC-style blind evaluation and reusable test sets.
The document retrieval task has a corpus of 3.2 million documents with 367
thousand training queries, for which we generate a reusable test set of 43
queries. The passage retrieval task has a corpus of 8.8 million passages with
503 thousand training queries, for which we generate a reusable test set of 43
queries. This year 15 groups submitted a total of 75 runs, using various
combinations of deep learning, transfer learning and traditional IR ranking
methods. Deep learning runs significantly outperformed traditional IR runs.
Possible explanations for this result are that we introduced large training
data and we included deep models trained on such data in our judging pools,
whereas some past studies did not have such training data or pooling.

TREC 2019 新推出的 Deep Learning Track 使用大量标注数据，对包含三百万文档和五十万训练查询的 Ad Hoc 排名展开研究，结果 Deep Learning 方法在 15 组提交的 75 个运行中取得了优异的表现。