Choppy：为分级列表截断而截断Transformer
Choppy: Cut Transformer For Ranked List Truncation
摘要：
信息检索中的工作传统上集中在排序和相关性上：给定一个查询，返回一些按与用户的相关性排序的结果。然而，确定返回多少结果的问题，即如何最佳地截断排名的结果列表，尽管在一系列应用中至关重要，但受到的关注较少。这种截断是在结果的总体相关性或有用性与处理更多结果的用户成本之间的平衡行为。在这项工作中，我们提出了Choppy，一个基于广泛成功的Transformer架构的无截断模型，排名列表截断问题。该模型只需要结果的相关性分数，使用强大的多头注意力机制来直接优化任何用户定义的IR度量。我们展示了Choppy对最近最先进方法的改进。
英文摘要：
Work in information retrieval has traditionally focused on ranking and relevance: given a query, return some number of results ordered by relevance to the user. However, the problem of determining how many results to return, i.e. how to optimally truncate the ranked result list, has received less attention despite being of critical importance in a range of applications. Such truncation is a balancing act between the overall relevance, or usefulness of the results, with the user cost of processing more results. In this work, we propose Choppy, an assumption-free model based on the widely successful Transformer architecture, to the ranked list truncation problem. Needing nothing more than the relevance scores of the results, the model uses a powerful multi-head attention mechanism to directly optimize any user-defined IR metric. We show Choppy improves upon recent state-of-the-art methods.
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