Search result diversification is a beneficial approach to overcome under-specified queries, such as those that are ambiguous or multi-faceted. Existing approaches often rely on massive query logs and interaction data to generate a variety of possible query intents, which then can be used to re-rank documents. However, relying on user interaction data is problematic because one first needs a massive user base to build a sufficient log; public query logs are insufficient on their own. Given the recent success of causal language models (such as the Text-To-Text Transformer (T5) model) at text generation tasks, we explore the capacity of these models to generate potential query intents. We find that to encourage diversity in the generated queries, it is beneficial to adapt the model by including a new Distributional Causal Language Modeling (DCLM) objective during fine-tuning and a representation replacement during inference. Across six standard evaluation benchmarks, we find that our method (which we call IntenT5) improves search result diversity and attains (and sometimes exceeds) the diversity obtained when using query suggestions based on a proprietary query log. Our analysis shows that our approach is most effective for multi-faceted queries and is able to generalize effectively to queries that were unseen in training data.
翻译:搜索结果多样化是克服诸如模棱两可或多面性等特定问题的一种有益办法。 现有办法往往依靠大量查询日志和互动数据来产生各种可能的查询意图,然后可以用来重新排序文件。 但是,依靠用户互动数据是有问题的,因为首先需要庞大的用户基础来建立足够的日志;公共查询日志本身是不够的。鉴于在文本生成任务中因果语言模型(如文本到文本变换器(T5)模型)最近的成功,我们探索这些模型产生潜在查询意图的能力。我们发现,为了鼓励生成查询的多样化,我们发现,通过在微调和推断过程中采用新的分布式causal语言建模(DCLM)目标来调整模型是有益的。在六个标准评价基准中,我们发现,我们的方法(我们称之为IntenT5)改进了搜索结果的多样性,在使用基于专有查询日志的查询建议时获得的多样性(有时超过)。我们的分析表明,我们的方法对于多面查询最为有效,因此,在一般的查询中,我们的方法能够有效地进行多面查询。