Due to the ambiguity and vagueness of a user query, it is essential to identify the query facets for the clarification of user intents. Existing work on query facet generation has achieved compelling performance by sequentially predicting the next facet given previously generated facets based on pre-trained language generation models such as BART. Given a query, there are mainly two types of training objectives to guide the facet generation models. One is to generate the default sequence of ground-truth facets, and the other is to enumerate all the permutations of ground-truth facets and use the sequence that has the minimum loss for model updates. The second is permutation-invariant while the first is not. In this paper, we aim to conduct a systematic comparative study of various types of training objectives, with different properties of not only whether it is permutation-invariant but also whether it conducts sequential prediction and whether it can control the count of output facets. To this end, we propose another three training objectives of different aforementioned properties. For comprehensive comparisons, besides the commonly used evaluation that measures the matching with ground-truth facets, we also introduce two diversity metrics to measure the diversity of the generated facets. Based on an open-domain query facet dataset, i.e., MIMICS, we conduct extensive analyses and show the pros and cons of each method, which could shed light on model training for clarification facet generation. The code can be found at \url{https://github.com/ShiyuNee/Facet-Generation}

翻译：由于用户查询存在歧义和模糊性，识别查询方面对于澄清用户意图至关重要。现有查询方面生成工作通过基于预训练语言生成模型（如BART）顺序预测已生成方面后的下一个方面，取得了令人瞩目的性能。给定查询时，主要有两种训练目标指导方面生成模型：一种是生成默认顺序的真实方面序列，另一种是枚举真实方面的所有排列，使用损失最小的序列进行模型更新。后者具有排列不变性，而前者则没有。本文旨在系统比较多种训练目标，这些目标不仅具有是否排列不变性的不同属性，还涉及是否进行顺序预测以及是否控制输出方面数量。为此，我们提出另外三种具有上述不同属性的训练目标。为进行全面比较，除评估与真实方面匹配程度的常用指标外，我们还引入两种多样性指标衡量生成方面的多样性。基于开放域查询方面数据集MIMICS，我们通过大量分析展示了每种方法的优缺点，这可为澄清方面生成的模型训练提供启示。代码可在\url{https://github.com/ShiyuNee/Facet-Generation}获取。