The Podcast Track is new at the Text Retrieval Conference (TREC) in 2020. The
podcast track was designed to encourage research into podcasts in the
information retrieval and NLP research communities. The track consisted of two
shared tasks: segment retrieval and summarization, both based on a dataset of
over 100,000 podcast episodes (metadata, audio, and automatic transcripts)
which was released concurrently with the track. The track generated
considerable interest, attracted hundreds of new registrations to TREC and
fifteen teams, mostly disjoint between search and summarization, made final
submissions for assessment. Deep learning was the dominant experimental
approach for both search experiments and summarization. This paper gives an
overview of the tasks and the results of the participants' experiments. The
track will return to TREC 2021 with the same two tasks, incorporating slight
modifications in response to participant feedback.

本文介绍了 2020 年 Text Retrieval Conference (TREC) 的 Podcast Track，包括检索和摘要两个任务，提供了各个参与者的实验结果。此 Track 将在 2021 年 TREC 继续进行，略作修改以响应参与者的反馈意见。

TREC 2020 Podcasts 赛道概述

TREC 2020 Podcasts Track Overview

Interactive Information Retrieval (IIR) and Reinforcement Learning (RL) share
many commonalities, including an agent who learns while interacts, a long-term
and complex goal, and an algorithm that explores and adapts. To successfully
apply RL methods to IIR, one challenge is to obtain sufficient relevance labels
to train the RL agents, which are infamously known as sample inefficient.
However, in a text corpus annotated for a given query, it is not the relevant
documents but the irrelevant documents that predominate. This would cause very
unbalanced training experiences for the agent and prevent it from learning any
policy that is effective. Our paper addresses this issue by using domain
randomization to synthesize more relevant documents for the training. Our
experimental results on the Text REtrieval Conference (TREC) Dynamic Domain
(DD) 2017 Track show that the proposed method is able to boost an RL agent's
learning effectiveness by 22\% in dealing with unseen situations.

本论文利用领域随机化方法针对强化学习与交互式信息检索技术之间的样本低效性问题，提高其学习效率并在 TREC Dynamic Domain (DD) 2017 跑道实验中，有效地提高 RL 智能体在处理未见过情况时的效能 22%。