@inproceedings{sajjad-etal-2021-fine,
title = "Fine-grained Interpretation and Causation Analysis in Deep {NLP} Models",
author = "Sajjad, Hassan and
Kokhlikyan, Narine and
Dalvi, Fahim and
Durrani, Nadir",
editor = "Kondrak, Greg and
Bontcheva, Kalina and
Gillick, Dan",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorials",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-tutorials.2",
doi = "10.18653/v1/2021.naacl-tutorials.2",
pages = "5--10",
abstract = "Deep neural networks have constantly pushed the state-of-the-art performance in natural language processing and are considered as the de-facto modeling approach in solving complex NLP tasks such as machine translation, summarization and question-answering. Despite the proven efficacy of deep neural networks at-large, their opaqueness is a major cause of concern. In this tutorial, we will present research work on interpreting fine-grained components of a neural network model from two perspectives, i) fine-grained interpretation, and ii) causation analysis. The former is a class of methods to analyze neurons with respect to a desired language concept or a task. The latter studies the role of neurons and input features in explaining the decisions made by the model. We will also discuss how interpretation methods and causation analysis can connect towards better interpretability of model prediction. Finally, we will walk you through various toolkits that facilitate fine-grained interpretation and causation analysis of neural models.",
}
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<abstract>Deep neural networks have constantly pushed the state-of-the-art performance in natural language processing and are considered as the de-facto modeling approach in solving complex NLP tasks such as machine translation, summarization and question-answering. Despite the proven efficacy of deep neural networks at-large, their opaqueness is a major cause of concern. In this tutorial, we will present research work on interpreting fine-grained components of a neural network model from two perspectives, i) fine-grained interpretation, and ii) causation analysis. The former is a class of methods to analyze neurons with respect to a desired language concept or a task. The latter studies the role of neurons and input features in explaining the decisions made by the model. We will also discuss how interpretation methods and causation analysis can connect towards better interpretability of model prediction. Finally, we will walk you through various toolkits that facilitate fine-grained interpretation and causation analysis of neural models.</abstract>
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%0 Conference Proceedings
%T Fine-grained Interpretation and Causation Analysis in Deep NLP Models
%A Sajjad, Hassan
%A Kokhlikyan, Narine
%A Dalvi, Fahim
%A Durrani, Nadir
%Y Kondrak, Greg
%Y Bontcheva, Kalina
%Y Gillick, Dan
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorials
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F sajjad-etal-2021-fine
%X Deep neural networks have constantly pushed the state-of-the-art performance in natural language processing and are considered as the de-facto modeling approach in solving complex NLP tasks such as machine translation, summarization and question-answering. Despite the proven efficacy of deep neural networks at-large, their opaqueness is a major cause of concern. In this tutorial, we will present research work on interpreting fine-grained components of a neural network model from two perspectives, i) fine-grained interpretation, and ii) causation analysis. The former is a class of methods to analyze neurons with respect to a desired language concept or a task. The latter studies the role of neurons and input features in explaining the decisions made by the model. We will also discuss how interpretation methods and causation analysis can connect towards better interpretability of model prediction. Finally, we will walk you through various toolkits that facilitate fine-grained interpretation and causation analysis of neural models.
%R 10.18653/v1/2021.naacl-tutorials.2
%U https://aclanthology.org/2021.naacl-tutorials.2
%U https://doi.org/10.18653/v1/2021.naacl-tutorials.2
%P 5-10
Markdown (Informal)
[Fine-grained Interpretation and Causation Analysis in Deep NLP Models](https://aclanthology.org/2021.naacl-tutorials.2) (Sajjad et al., NAACL 2021)
ACL
- Hassan Sajjad, Narine Kokhlikyan, Fahim Dalvi, and Nadir Durrani. 2021. Fine-grained Interpretation and Causation Analysis in Deep NLP Models. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorials, pages 5–10, Online. Association for Computational Linguistics.