Explaining Recurrent Neural Network Predictions in Sentiment Analysis
Open accessAbstract
Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions.In the present work, we extend the usage of LRP to recurrent neural networks.We propose a specific propagation rule applicable to multiplicative connections as they arise in recurrent network architectures such as LSTMs and GRUs.We apply our technique to a word-based bi-directional LSTM model on a five-class sentiment prediction task, and evaluate the resulting LRP relevances both qualitatively and quantitatively, obtaining better results than a gradient-based related method which was used in previous work.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Learning to Forget: Continual Prediction with LSTM
Gers, F. A., Schmidhuber, J., & Cummins, F. (2000) · Neural Computation · doi:10.1162/089976600300015015 · Cited by 5,869 · see citing works
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524 · see citing works
Self-citationGevrey, M., Dimopoulos, I. F., & Lek, S. (2003) · Ecological Modelling · doi:10.1016/s0304-3800(02)00257-0 · Cited by 1,251 · see citing works
Evaluating the Visualization of What a Deep Neural Network Has Learned
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., & Muller, K.-R. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2599820 · Cited by 1,164 · see citing works
Self-citationUse of some sensitivity criteria for choosing networks with good generalization ability
Dimopoulos, Y., Bourret, P., & Lek, S. (1995) · Neural Processing Letters · doi:10.1007/bf02309007 · Cited by 267 · see citing works
Flagged first, then by citations
Research works printed but not linked: 16
- Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2016a. Explaining Predictions of Non-Linear Classifiers in NLP. In Proceedings of the ACL 2016 Workshop on Representation Learning for NLP. Association for Computational Linguistics, pages 1-7.
- Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2016b. "What is Relevant in a Text Document?": An In- terpretable Machine Learning Approach. arXiv 1612.07843. · arXiv
- Kyunghyun Cho, Bart van Merrienboer, Caglar Gul- cehre, Dzmitry Bahdanau, Fethi Bougares, Hol- ger Schwenk, and Yoshua Bengio. 2014. Learn- ing Phrase Representations using RNN Encoder- Decoder for Statistical Machine Translation. In Pro- ceedings of the 2014 Conference on Empirical Meth- ods in Natural Language Processing (EMNLP). Association for Computational Linguistics, pages 1724-1734.
- Misha Denil, Alban Demiraj, and Nando de Freitas. 2014. Extraction of Salient Sentences from Labelled Documents. arXiv 1412.6815. · arXiv
- Will Landecker, Michael D. Thomure, Luís M. A. Bet- tencourt, Melanie Mitchell, Garrett T. Kenyon, and Steven P. Brumby. 2013. Interpreting Individual Classifications of Hierarchical Networks. In IEEE Symposium on Computational Intelligence and Data Mining (CIDM). pages 32-38.
- Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2016. The Layer-wise Relevance Propaga- tion Toolbox for Artificial Neural Networks. Jour- nal of Machine Learning Research 17(114):1-5.
- Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Juraf- sky. 2016a. Visualizing and Understanding Neural Models in NLP. In Proceedings of the 2016 Con- ference of the North American Chapter of the Asso- ciation for Computational Linguistics: Human Lan- guage Technologies (NAACL-HLT). pages 681-691.
- Jiwei Li, Will Monroe, and Dan Jurafsky. 2016b. Un- derstanding Neural Networks through Representa- tion Erasure. arXiv 1612.08220. · arXiv
- Saif M. Mohammad. 2017. Challenges in Sentiment Analysis. In Erik Cambria, Dipankar Das, Sivaji Bandyopadhyay, and Antonio Feraco, editors, A Practical Guide to Sentiment Analysis. Springer In- ternational Publishing, pages 61-83.
- Grégoire Montavon, Wojciech Samek, and Klaus- Robert Müller. 2017b. Methods for Interpreting and Understanding Deep Neural Networks. arXiv 1706.07979. · arXiv
- James Murdoch and Arthur Szlam. 2017. Automatic Rule Extraction from Long Short Term Memory Networks. In International Conference on Learning Representations Conference Track (ICLR).
- Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, and Veselin Stoyanov. 2016. SemEval- 2016 Task 4: Sentiment Analysis in Twitter. In Pro- ceedings of the 10th International Workshop on Se- mantic Evaluation (SemEval-2016). Association for Computational Linguistics, pages 1-18.
- Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why Should I Trust You?": Ex- plaining the Predictions of Any Classifier. In Pro- ceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Min- ing. pages 1135-1144.
- Mike Schuster and Kuldip K. Paliwal. 1997. Bidirec- tional Recurrent Neural Networks. IEEE Transac- tions on Signal Processing 45(11):2673-2681.
- Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013. Recursive Deep Mod- els for Semantic Compositionality Over a Sentiment Treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Pro- cessing (EMNLP). Association for Computational Linguistics, pages 1631-1642.
- Matthew D. Zeiler and Rob Fergus. 2014. Visualiz- ing and Understanding Convolutional Networks. In Computer Vision ECCV 2014: 13th European Con- ference. pages 818-833.
Cited by
Sorted by citations
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works
Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.011 · Cited by 2,665 · see citing works
Self-citationA Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
Tjoa, E., & Guan, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027314 · Cited by 2,524 · see citing works
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3060483 · Cited by 1,477 · see citing works
“Other neural network architectures have a more conventional structure but still require a nontrivial adaptation of existing explanation methods, for example, extensions of LRP have been proposed to deal with the special LSTM blocks in recurrent neural networks [9], [11] or to handle attention units in the context of n…”
BackgroundMethodSelf-citationSamek, W., Wiegand, T., & Müller, K.-R. (2017) · arXiv · doi:10.48550/arxiv.1708.08296 · Cited by 1,080 · see citing works
Self-citationHuman-in-the-loop machine learning: a state of the art
Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., & Fernández-Leal, Á. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10246-w · Cited by 1,137 · see citing works
Layer-Wise Relevance Propagation: An Overview
Montavon, G., Binder, A., Lapuschkin, S., Samek, W., & Müller, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-28954-6_10 · Cited by 825 · see citing works
Self-citationTowards Explainable Artificial Intelligence
Samek, W., & Müller, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-28954-6_1 · Cited by 650 · see citing works
Self-citationA Review of Explainable Artificial Intelligence in Healthcare
Sadeghi, Z., Alizadehsani, R., CIFCI, M. A., Kausar, S., Rehman, R., Mahanta, P., Bora, P. K., Almasri, A., Alkhawaldeh, R. S., Hussain, S., et al. (2024) · Computers and Electrical Engineering · doi:10.1016/j.compeleceng.2024.109370 · Cited by 598 · see citing works
On Interpretability of Artificial Neural Networks: A Survey
Fan, F., Xiong, J., Li, M., & Wang, G. (2021) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2021.3066428 · Cited by 573 · see citing works
Unmasking Clever Hans predictors and assessing what machines really learn
Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K. (2019) · RePEc: Research Papers in Economics · Cited by 580 · see citing works
Self-citationAnalysis Methods in Neural Language Processing: A Survey
Belinkov, Y., & Glass, J. R. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00254 · Cited by 485 · see citing works
Explainable AI Methods - A Brief Overview
Holzinger, A., Saranti, A., Molnar, C., Biecek, P., & Samek, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-04083-2_2 · Cited by 480 · see citing works
Self-citationNazar, M., Alam, M. M., Yafi, E., & Su'ud, M. M. (2021) · IEEE Access · doi:10.1109/access.2021.3127881 · Cited by 312 · see citing works
Benchmarking and survey of explanation methods for black box models
Bodria, F., Giannotti, F., Guidotti, R., Naretto, F., Pedreschi, D., & Rinzivillo, S. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00933-9 · Cited by 353 · see citing works
Social media sentiment analysis based on COVID-19
Nemes, L., & Kiss, A. (2020) · Journal of Information and Telecommunication · doi:10.1080/24751839.2020.1790793 · Cited by 269 · see citing works
Transformer-CNN: Swiss knife for QSAR modeling and interpretation
Karpov, P., Godin, G., & Tetko, I. V. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00423-w · Cited by 261 · see citing works
"What is relevant in a text document?": An interpretable machine learning approach
Arras, L., Horn, F., Montavon, G., Müller, K., & Samek, W. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181142 · Cited by 268 · see citing works
Self-citationNeupane, S., Ables, J., Anderson, W. S., Mittal, S., Rahimi, S., Banicescu, I., & Seale, M. A. (2022) · IEEE Access · doi:10.1109/access.2022.3216617 · Cited by 293 · see citing works
A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets
Kaur, H., Ahsaan, S. U., Alankar, B., & Chang, V. I. C. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10135-7 · Cited by 227 · see citing works
Explainable AI for Bioinformatics: Methods, Tools and Applications
Karim, M. R., Islam, T., Shajalal, M., Beyan, O., Lange, C., Cochez, M., Rebholz‐Schuhmann, D., & Decker, S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad236 · Cited by 209 · see citing works
CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations
Arras, L., Osman, A., & Samek, W. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.11.008 · Cited by 189 · see citing works
Self-citationFrom attribution maps to human-understandable explanations through Concept Relevance Propagation
Achtibat, R., Dreyer, M., Eisenbraun, I., Bosse, S., Wiegand, T., Samek, W., & Lapuschkin, S. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00711-8 · Cited by 181 · see citing works
Self-citationLove, P. E. D., Fang, W., Matthews, J., Porter, S. R., Luo, H., & Ding, L. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102024 · Cited by 191 · see citing works
Wani, N. A., Kumar, R., Mamta, ., Bedi, J., & Rida, I. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102472 · Cited by 171 · see citing works
NeuralHydrology – Interpreting LSTMs in Hydrology
Kratzert, F., Herrnegger, M., Klotz, D., Hochreiter, S., & Klambauer, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-28954-6_19 · Cited by 160 · see citing works
Bastings, J., & Filippova, K. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.14 · Cited by 168 · see citing works
Classification of Explainable Artificial Intelligence Methods through Their Output Formats
Vilone, G., & Longo, L. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3030032 · Cited by 157 · see citing works
Explainability-driven model improvement for SOH estimation of lithium-ion battery
Wang, F., Zhao, Z., Zhai, Z., Shang, Z., Yan, R., & Chen, X. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.109046 · Cited by 145 · see citing works
CRNN-Based Multiple DoA Estimation Using Acoustic Intensity Features for Ambisonics Recordings
Perotin, L., Serizel, R., Emmanuel Vincent, A., & Guérin, A. (2019) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2900164 · Cited by 145 · see citing works
Exploring Interpretable LSTM Neural Networks over Multi-Variable DataPreprint
Guo, T., Lin, T., & Antulov-Fantulin, N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12034 · Cited by 143 · see citing works
Towards Faithful Model Explanation in NLP: A Survey
Lyu, Q., Apidianaki, M., & Callison-Burch, C. (2024) · Computational Linguistics · doi:10.1162/coli_a_00511 · Cited by 143 · see citing works
Levels of explainable artificial intelligence for human-aligned conversational explanations
Dazeley, R., Vamplew, P. W., Foale, C., Young, C., Aryal, S., & Cruz, F. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103525 · Cited by 122 · see citing works
Interpreting and Explaining Deep Neural Networks for Classification of Audio SignalsPreprint
Becker, S., Ackermann, M. R., Lapuschkin, S., Müller, K., & Samek, W. (2018) · arXiv (Cornell University) · Cited by 124 · see citing works
Self-citationRUDDER: Return Decomposition for Delayed RewardsPreprint
Arjona-Medina, J. A., Gillhofer, M., Widrich, M., Unterthiner, T., Brandstetter, J., & Hochreiter, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.07857 · Cited by 122 · see citing works
From Clustering to Cluster Explanations via Neural Networks
Kauffmann, J., Esders, M., Ruff, L., Montavon, G., Samek, W., & Müller, K. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3185901 · Cited by 124 · see citing works
Self-citationDeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali Language
Karim, M. R., Dey, S. K., Islam, T., Sarker, S., Menon, M. H., Hossain, K., Hossain, M. A., & Decker, S. (2021) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa53316.2021.9564230 · Cited by 114 · see citing works
A unified view of gradient-based attribution methods for Deep Neural NetworksPreprint
Ancona, M., Ceolini, E., Öztireli, C., & Groß, M. H. (2017) · arXiv (Cornell University) · doi:10.3929/ethz-b-000237705 · Cited by 120 · see citing works
Social Media Hate Speech Detection Using Explainable Artificial Intelligence (XAI)
Mehta, H., & Passi, K. (2022) · Algorithms · doi:10.3390/a15080291 · Cited by 113 · see citing works
Explaining and Interpreting LSTMs
Arras, L., Arjona-Medina, J., Widrich, M., Montavon, G., Gillhofer, M., Müller, K.-R., Hochreiter, S., & Samek, W. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-28954-6_11 · Cited by 103 · see citing works
Self-citationUnderstanding Individual Decisions of CNNs via Contrastive Backpropagation
Gu, J., Yang, Y., & Tresp, V. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20893-6_8 · Cited by 101 · see citing works
Explanation-Guided Training for Cross-Domain Few-Shot Classification
Sun, J., Lapuschkin, S., Samek, W., Zhao, Y., Cheung, N. M., & Binder, A. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412941 · Cited by 96 · see citing works
Self-citationAydın, C. R., & Güngör, T. (2020) · IEEE Access · doi:10.1109/access.2020.2990306 · Cited by 98 · see citing works
A Practical Tutorial on Explainable AI Techniques
Bennetot, A., Donadello, I., El-Qadi, A., Dragoni, M., Frossard, T., Wagner, B., Saranti, A., Tulli, S., Trocan, M., Chatila, R., et al. (2024) · ACM Computing Surveys · doi:10.1145/3670685 · Cited by 101 · see citing works
A Deep Learning Approach for Sentiment Analysis of COVID-19 Reviews
Singh, C., Imam, T., Wibowo, S., & Grandhi, S. (2022) · Applied Sciences · doi:10.3390/app12083709 · Cited by 98 · see citing works
Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis
Wu, Z., & Ong, D. C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17659 · Cited by 98 · see citing works
Analyzing Neuroimaging Data Through Recurrent Deep Learning Models
Thomas, A. W., Heekeren, H. R., Müller, K., & Samek, W. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01321 · Cited by 93 · see citing works
Self-citationSelf-Attention-Based BiLSTM Model for Short Text Fine-Grained Sentiment Classification
Xie, J., Chen, B., Gu, X., Liang, F., & Xu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2957510 · Cited by 92 · see citing works
Prediction of stock values changes using sentiment analysis of stock news headlines
Nemes, L., & Kiss, A. (2021) · Journal of Information and Telecommunication · doi:10.1080/24751839.2021.1874252 · Cited by 97 · see citing works
Explainability in AI-based behavioral malware detection systems
Galli, A., Gatta, V. L., Moscato, V., Postiglione, M., & Sperlí, G. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103842 · Cited by 106 · see citing works
Explainable Artificial Intelligence: a Systematic Review
Vilone, G., & Longo, L. (2020) · arXiv · doi:10.48550/arxiv.2006.00093 · Cited by 269 · see citing works
Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference
Ghaeini, R., Fern, X., & Tadepalli, P. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1537 · Cited by 91 · see citing works
Decoding subjective emotional arousal from EEG during an immersive virtual reality experience
Hofmann, S., Klotzsche, F., Mariola, A., Nikulin, V. V., Villringer, A., & Gaebler, M. (2021) · eLife · doi:10.7554/elife.64812 · Cited by 93 · see citing works
Explaining Therapy Predictions with Layer-Wise Relevance Propagation in Neural Networks
Yang, Y., Tresp, V., Wunderle, M., & Fasching, P. A. (2018) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2018.00025 · Cited by 88 · see citing works
Alzahrani, M. E., Aldhyani, T. H. H., Alsubari, S. N., Althobaiti, M. M., & Fahad, A. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3840071 · Cited by 88 · see citing works
Explainable AI: A review of applications to neuroimaging data
Farahani, F. V., Fiok, K., Lahijanian, B., Karwowski, W., & Douglas, P. K. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.906290 · Cited by 85 · see citing works
Explainable AI: current status and future directionsPreprint
Gohel, P., Singh, P., & Mohanty, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07045 · Cited by 148 · see citing works
Enhancing corrosion-resistant alloy design through natural language processing and deep learning
Sasidhar, K. N., Siboni, N. H., Mianroodi, J. R., Rohwerder, M., Neugebauer, J., & Raabe, D. (2023) · Science Advances · doi:10.1126/sciadv.adg7992 · Cited by 80 · see citing works
Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement
Poerner, N., Schütze, H., & Roth, B. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1032 · Cited by 83 · see citing works
Townsend, J., Chaton, T., & Monteiro, J. M. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2944672 · Cited by 70 · see citing works
Learning Memory-Based Control for Human-Scale Bipedal Locomotion
Siekmann, J., Valluri, S., Dao, J., Bermillo, L., Duan, H., Fern, A., & HURST, J. W. (2020) · Robotics: Science and Systems · doi:10.15607/rss.2020.xvi.031 · Cited by 78 · see citing works
AudioMNIST: Exploring Explainable Artificial Intelligence for audio analysis on a simple benchmark
Becker, S., Vielhaben, J., Ackermann, M., Müller, K., Lapuschkin, S., & Samek, W. (2023) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2023.11.038 · Cited by 124 · see citing works
Self-citationEvaluating Recurrent Neural Network Explanations
Arras, L., Osman, A., Müller, K., & Samek, W. (2019) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w19-4813 · Cited by 79 · see citing works
Self-citationToxic Comment Detection in Online Discussions
Risch, J., & Krestel, R. (2020) · Algorithms for intelligent systems · doi:10.1007/978-981-15-1216-2_4 · Cited by 79 · see citing works
A multi-branch deep neural network model for failure prognostics based on multimodal data
Yang, Z., Baraldi, P., & Zio, E. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.01.007 · Cited by 71 · see citing works
Explaining CNN and RNN Using Selective Layer-Wise Relevance Propagation
Jung, Y., Han, S. H., & Choi, H. (2021) · IEEE Access · doi:10.1109/access.2021.3051171 · Cited by 70 · see citing works
Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
Ullah, I., Rios, A., Gala, V., & McKeever, S. (2021) · Applied Sciences · doi:10.3390/app12010136 · Cited by 81 · see citing works
Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis
Monselise, M., Chang, C., Ferreira, G., Yang, R., & Yang, C. C. (2021) · Journal of Medical Internet Research · doi:10.2196/30765 · Cited by 68 · see citing works
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and BeyondPreprint
Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Müller, K. (2020) · arXiv (Cornell University) · Cited by 70 · see citing works
Self-citationNonlinear inflation forecasting with recurrent neural networks
Almosova, A., & Andresen, N. (2022) · Journal of Forecasting · doi:10.1002/for.2901 · Cited by 67 · see citing works
Causal inference multi-agent reinforcement learning for traffic signal control
Yang, S., Yang, B., Zeng, Z., & Kang, Z. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.02.009 · Cited by 66 · see citing works
Towards a Robust Deep Neural Network against Adversarial Texts: A Survey
Wang, W., Wang, R., Wang, L., Wang, Z., & Ye, A. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3117608 · Cited by 144 · see citing works
Human-grounded Evaluations of Explanation Methods for Text Classification
Lertvittayakumjorn, P., & Toni, F. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1523 · Cited by 61 · see citing works
Agarwal, P., Tamer, M. I., & Budman, H. M. (2021) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2021.107467 · Cited by 60 · see citing works
Towards a Robust Deep Neural Network in Texts: A SurveyPreprint
Wang, W., Wang, R., Wang, L., Wang, Z., & Ye, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07285 · Cited by 58 · see citing works
Counterfactual Causal-Effect Intervention for Interpretable Medical Visual Question Answering
Cai, L., Fang, H., Xu, N., & Ren, B. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3425533 · Cited by 56 · see citing works
Improving healthcare access management by predicting patient no-show behaviour
Barrera, D., Brailsford, S., Bravo, C., & Smith, H. K. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113398 · Cited by 55 · see citing works
On Attribution of Recurrent Neural Network Predictions via Additive Decomposition
Du, M., Liu, N., Yang, F., Ji, S., & Hu, X. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313545 · Cited by 57 · see citing works
Interpretability of deep neural networks: A review of methods, classification and hardware
Antamis, T., Drosou, A., Vafeiadis, T., Nizamis, A., Ioannidis, D., & Tzovaras, D. K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128204 · Cited by 55 · see citing works
Explainable Sentiment Analysis with Applications in Medicine
Zucco, C., Liang, H., Fatta, G. D., & Cannataro, M. (2018) · 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621359 · Cited by 59 · see citing works
El–Sappagh, S., Alonso, J. M., Abuhmed, T., Ali, F., & Bugarín, A. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10415-5 · Cited by 54 · see citing works
Fiok, K., Karwowski, W., Gutiérrez-Franco, E., & Wilamowski, M. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115771 · Cited by 49 · see citing works
Chang-Brahim, I., Koppensteiner, L. J., Beltrame, L., Bodner, G., Saranti, A., Salzinger, J., Fanta‐Jende, P., Sulzbachner, C., Bruckmüller, F., Trognitz, F., et al. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1319938 · Cited by 56 · see citing works
DRAU: Dual Recurrent Attention Units for Visual Question Answering
Osman, A., & Samek, W. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.05.001 · Cited by 54 · see citing works
Self-citationEvaluating eXplainable artificial intelligence tools for hard disk drive predictive maintenance
Ferraro, A., Galli, A., Moscato, V., & Sperlí, G. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10354-7 · Cited by 49 · see citing works
Vergaray, A. D., Rueda, N. D. G., Sánchez, M. S. A., Robles-Espiritu, W., & Quiñones, M. E. C. (2024) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2024.100267 · Cited by 73 · see citing works
Towards robust explanations for deep neural networks
Dombrowski, A.-K., Anders, C. J., Müller, K., & Kessel, P. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108194 · Cited by 52 · see citing works
Self-citationSAM: The Sensitivity of Attribution Methods to Hyperparameters
Bansal, N., Agarwal, C., & Nguyen, A. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00870 · Cited by 48 · see citing works
Layer-wise relevance propagation for interpreting LSTM-RNN decisions in predictive maintenance
Wu, H., Huang, A., & Sutherland, J. W. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-07911-9 · Cited by 45 · see citing works
Interpretation of NLP models through input marginalization
Kim, S., Yi, J., Kim, E., & Yoon, S. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.255 · Cited by 45 · see citing works
Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning
Salminen, J. O., Mustak, M., Corporan, J., Jung, S., & Jansen, B. J. J. (2022) · Journal of Interactive Marketing · doi:10.1177/10949968221095556 · Cited by 47 · see citing works
A novel normalized recurrent neural network for fault diagnosis with noisy labels
Nie, X., & Xie, G. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01608-8 · Cited by 41 · see citing works
Interpreting Deep Learning Models for Knowledge Tracing
Lu, Y., Wang, D., Chen, P., Meng, Q., & Yu, S. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-022-00297-z · Cited by 40 · see citing works
Koeppe, A., Bamer, F., Selzer, M. A., Nestler, B., & Markert, B. A. (2022) · Frontiers in Materials · doi:10.3389/fmats.2021.824958 · Cited by 40 · see citing works
Contextual Sentiment Neural Network for Document Sentiment Analysis
Ito, T., Tsubouchi, K., Sakaji, H., Yamashita, T., & Izumi, K. (2020) · Data Science and Engineering · doi:10.1007/s41019-020-00122-4 · Cited by 39 · see citing works
Reverse-engineering Recurrent Neural Network solutions to a hierarchical inference task for micePreprint
Schaeffer, R., Khona, M., Meshulam, L., & Fiete, I. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.09.142745 · Cited by 37 · see citing works
XFlag: Explainable Fake News Detection Model on Social Media
Chien, S.-Y. J., Yang, C., & Yu, F. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2062113 · Cited by 40 · see citing works
Liu, H., Wu, Y., Yu, Z., & Zhang, N. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00120 · Cited by 37 · see citing works
On Guaranteed Optimal Robust Explanations for NLP Models
Malfa, E. L., Michelmore, R., Zbrzezny, A. M., Paoletti, N., & Kwiatkowska, M. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/366 · Cited by 37 · see citing works
- Authors
- Arras, L., Montavon, G., Müller, K.-R., & Samek, W.
- Affiliations
- Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute (DE)
- Technische Universität Berlin (DE)
- Freie Universität Berlin (DE)
- Venue
- Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Published
- 2017
- Pages
- 159-168
- OpenAlex
- W2639206954
- Cited by
- 361
- References
- 5
- Citations by year
- 2026: 6 · 2025: 22 · 2024: 42 · 2023: 49 · 2022: 47 · 2021: 63 · 2020: 58 · 2019: 42 · 2018: 26 · 2017: 6
- FWCI
- 16.46
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Deutsche Forschungsgemeinschaft
- Bundesministerium für Bildung und Forschung
- Institute for Information and Communications Technology Promotion (2017-0-00451)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- layer-wise relevance propagation, explainable AI, recurrent neural networks, sentiment analysis, LSTM, GRU, bidirectional LSTM
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
