Deep Relevance Ranking Using Enhanced Document-Query Interactions

Abstract

We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016). Unlike DRMM, which uses context-insensitive encodings of terms and query-document term interactions, we inject rich context-sensitive encodings throughout our models, inspired by PACRR's (Hui et al., 2017) convolutional n-gram matching features, but extended in several ways including multiple views of query and document inputs. We test our models on datasets from the BIOASQ question answering challenge (Tsatsaronis et al., 2015) and TREC ROBUST 2004 (Voorhees, 2005), showing they outperform BM25-based baselines, DRMM, and PACRR.

In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), Brussels, Belgium, 2018

References in corpus (4)

• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• Convolutional Neural Network Architectures for Matching Natural Language Sentences
• A Deep Relevance Matching Model for Ad-hoc Retrieval
• End-to-End Neural Ad-hoc Ranking with Kernel Pooling

Cited by in corpus (5)

• Helping results assessment by adding explainable elements to the deep relevance matching model
• Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations
• Modeling Document Interactions for Learning to Rank with Regularized Self-Attention
• Pre-trained Language Model based Ranking in Baidu Search
• Can Embeddings Adequately Represent Medical Terminology? New Large-Scale Medical Term Similarity Datasets Have the Answer!