Deep Relevance Ranking Using Enhanced Document-Query Interactions

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... (read more)

PDF Abstract EMNLP 2018 PDF EMNLP 2018 Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Ad-Hoc Information Retrieval TREC Robust04 POSIT-DRMM-MV MAP 0.271 # 10
[email protected] 0.389 # 10
[email protected] 0.464 # 6
Ad-Hoc Information Retrieval TREC Robust04 PACRR MAP 0.258 # 11
[email protected] 0.374 # 13
[email protected] 0.445 # 9

Methods used in the Paper


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