Deep Relevance Ranking Using Enhanced Document-Query Interactions

Ryan McDonald, George Brokos, Ion Androutsopoulos


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.
Anthology ID:
D18-1211
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1849–1860
Language:
URL:
https://aclanthology.org/D18-1211
DOI:
10.18653/v1/D18-1211
Bibkey:
Cite (ACL):
Ryan McDonald, George Brokos, and Ion Androutsopoulos. 2018. Deep Relevance Ranking Using Enhanced Document-Query Interactions. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1849–1860, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Deep Relevance Ranking Using Enhanced Document-Query Interactions (McDonald et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1211.pdf
Attachment:
 D18-1211.Attachment.zip
Video:
 https://vimeo.com/306041612
Data
BioASQRobust04