CEQE：用于查询扩展的上下文化嵌入
CEQE: Contextualized Embeddings for Query Expansion
摘要：
在这项工作中，我们利用上下文敏感的语言模型的最新进展，以提高查询扩展的任务。诸如ELMo和BERT之类的上下文化单词表示模型正在迅速取代静态嵌入模型。我们提出了一个新的模型，上下文嵌入查询扩展（CEQE），利用查询为中心的上下文嵌入向量。我们研究的行为产生的上下文表示的查询扩展在ad-hoc文档检索。我们进行我们的实验概率检索模型，以及结合神经排名模型。我们在两个标准的TREC集合上评估CEQE：Robust和深度学习。我们发现，CEQE在多个集合上优于基于静态嵌入的扩展方法（平均精度在Robust上高达18%，在深度学习上高达31%），并且还改进了经过验证的概率伪相关反馈（PRF）模型。我们进一步发现，多次通过扩展和重新排序的结果在持续的收益与CEQE为基础的方法优于其他方法的有效性。结合神经和基于CEQE的扩展评分的最终模型在Robust上的P@20和AP中分别获得了高达5%和2%的收益，超过了最先进的基于transformer的重新排名模型Birch。
英文摘要：
In this work we leverage recent advances in context-sensitive language models to improve the task of query expansion. Contextualized word representation models, such as ELMo and BERT, are rapidly replacing static embedding models. We propose a new model, Contextualized Embeddings for Query Expansion (CEQE), that utilizes query-focused contextualized embedding vectors. We study the behavior of contextual representations generated for query expansion in ad-hoc document retrieval. We conduct our experiments on probabilistic retrieval models as well as in combination with neural ranking models. We evaluate CEQE on two standard TREC collections: Robust and Deep Learning. We find that CEQE outperforms static embedding-based expansion methods on multiple collections (by up to 18% on Robust and 31% on Deep Learning on average precision) and also improves over proven probabilistic pseudo-relevance feedback (PRF) models. We further find that multiple passes of expansion and reranking result in continued gains in effectiveness with CEQE-based approaches outperforming other approaches. The final model incorporating neural and CEQE-based expansion score achieves gains of up to 5% in P@20 and 2% in AP on Robust over the state-of-the-art transformer-based re-ranking model, Birch.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
