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Alignment Rationale for Query-Document Relevance

Published: 07 July 2022 Publication History

Abstract

Deep neural networks are widely used for text pair classification tasks such as as adhoc information 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 the defined metrics based on substitution-based perturbation are more successful in preferring higher-quality alignments, compared to the deletion-based metrics.

References

[1]
Samuel Carton, Anirudh Rathore, and Chenhao Tan. 2020. Evaluating and Characterizing Human Rationales. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Association for Computational Linguistics, Online, 9294--9307. https://doi.org/10.18653/v1/2020.emnlp-main.747
[2]
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M Voorhees. [n.d.]. OVERVIEW OF THE TREC 2019 DEEP LEARNING TRACK. ( [n.,d.]).
[3]
Zhuyun Dai and Jamie Callan. 2019. Deeper text understanding for IR with contextual neural language modeling. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 985--988.
[4]
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace. 2020. ERASER: A Benchmark to Evaluate Rationalized NLP Models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, Online, 4443--4458. https://doi.org/10.18653/v1/2020.acl-main.408
[5]
Zeon Trevor Fernando, Jaspreet Singh, and Avishek Anand. 2019. A Study on the Interpretability of Neural Retrieval Models Using DeepSHAP. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (SIGIR'19). 1005--1008.
[6]
Peter Hase, Harry Xie, and Mohit Bansal. 2021. The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations. Advances in Neural Information Processing Systems, Vol. 34 (2021).
[7]
Marti A Hearst. 1995. Tilebars: Visualization of term distribution information in full text information access. In Proceedings of the SIGCHI conference on Human factors in computing systems. 59--66.
[8]
Orland Hoeber and Xue Dong Yang. 2006. The Visual Exploration ofWeb Search Results Using HotMap. Tenth International Conference on Information Visualisation (IV'06) (2006), 157--165.
[9]
Zhongtao Jiang, Yuanzhe Zhang, Zhao Yang, Jun Zhao, and Kang Liu. 2021. Alignment Rationale for Natural Language Inference. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 5372--5387.
[10]
Youngwoo Kim, Myungha Jang, and James Allan. 2020. Explaining text matching on neural natural language inference. ACM Transactions on Information Systems (TOIS), Vol. 38, 4 (2020), 1--23.
[11]
Youngwoo Kim, Razieh Rahimi, Hamed Bonab, and James Allan. 2021. Query-Driven Segment Selection for Ranking Long Documents .Association for Computing Machinery, New York, NY, USA, 3147--3151. https://doi.org/10.1145/3459637.3482101
[12]
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. In CoCo@ NIPS .
[13]
Yifan Qiao, Chenyan Xiong, Zhenghao Liu, and Zhiyuan Liu. 2019. Understanding the Behaviors of BERT in Ranking. arXiv preprint arXiv:1904.07531 (2019).
[14]
Razieh Rahimi, Youngwoo Kim, Hamed Zamani, and James Allan. 2021. Explaining Documents' Relevance to Search Queries. arXiv preprint arXiv:2111.01314 (2021).
[15]
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD '16). Association for Computing Machinery, New York, NY, USA, 1135--1144. https://doi.org/10.1145/2939672.2939778
[16]
Procheta Sen, Debasis Ganguly, Manisha Verma, and Gareth J.F. Jones. 2020. The Curious Case of IR Explainability: Explaining Document Scores within and across Ranking Models . 2069--2072.
[17]
Jaspreet Singh and Avishek Anand. 2018. Posthoc Interpretability of Learning to Rank Models using Secondary Training Data. In Workshop on ExplainAble Recommendation and Search (EARS 2018) at SIGIR 2018 .
[18]
Jaspreet Singh and Avishek Anand. 2019. Exs: Explainable search using local model agnostic interpretability. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining . 770--773.
[19]
Jaspreet Singh, Megha Khosla, Wang Zhenye, and Avishek Anand. 2021. Extracting per Query Valid Explanations for Blackbox Learning-to-Rank Models. In Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval. Association for Computing Machinery, New York, NY, USA, 203--210. https://doi.org/10.1145/3471158.3472241
[20]
Manisha Verma and Debasis Ganguly. 2019. LIRME: Locally Interpretable Ranking Model Explanation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (SIGIR'19). 1281--1284.
[21]
Andrew Yates, Rodrigo Nogueira, and Jimmy Lin. 2021. Pretrained Transformers for Text Ranking: BERT and Beyond .Association for Computing Machinery, New York, NY, USA, 1154--1156. https://doi.org/10.1145/3437963.3441667
[22]
Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, and Shaoping Ma. 2020. An Analysis of BERT in Document Ranking .Association for Computing Machinery, New York, NY, USA, 1941--1944. https://doi.org/10.1145/3397271.3401325
[23]
Honglei Zhuang, Xuanhui Wang, Michael Bendersky, Alexander Grushetsky, Yonghui Wu, Petr Mitrichev, Ethan Sterling, Nathan Bell, Walker Ravina, and Hai Qian. 2021. Interpretable Ranking with Generalized Additive Models. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 499--507.

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  1. Alignment Rationale for Query-Document Relevance

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    cover image ACM Conferences
    SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2022
    3569 pages
    ISBN:9781450387323
    DOI:10.1145/3477495
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    Published: 07 July 2022

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    Author Tags

    1. document search
    2. neural network explanation
    3. query highlighting
    4. text alignment
    5. textual matching
    6. token-level explanation

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