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Title: Alignment Rationale for Query-Document Relevance
Deep neural networks are widely used for text pair classification tasks such as as adhoc retrieval. These deep neural networks are not inherently interpretable and require additional efforts to get rationale behind their decisions. Existing explanation models are not yet capable of inducing alignments between the query terms and the document terms -- which part of the document rationales are responsible for which part of the query? In this paper, we study how the input perturbations can be used to infer or evaluate alignments between the query and document spans, which best explain the black-box ranker’s relevance prediction. We use different perturbation strategies and accordingly propose a set of metrics to evaluate the faithfulness of alignment rationales to the model. Our experiments show that defined metrics based on substitution-based perturbation are more successful in preferring higher-quality alignments, compared to the deletion-based metrics.  more » « less
Award ID(s):
1813662
NSF-PAR ID:
10357771
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 22)
Page Range / eLocation ID:
2489 to 2494
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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