# Interpretable and Steerable Sequence Learning via Prototypes

arXiv: https://arxiv.org/abs/1907.09728 · HTML: https://mlsanity.com/paper/1907.09728/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 30 of 72)

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- [This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks](https://mlsanity.com/paper/2105.02968/) (2021)
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- [Explanation-Based Human Debugging of NLP Models: A Survey](https://mlsanity.com/paper/2104.15135/) (2021)
- [Concept-level Debugging of Part-Prototype Networks](https://mlsanity.com/paper/2205.15769/) (2022)
- [TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop](https://mlsanity.com/paper/2503.01013/) (2025)
- [ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts](https://mlsanity.com/paper/2301.12276/) (2023)
- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
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- [A Unified Understanding of Deep NLP Models for Text Classification](https://mlsanity.com/paper/2206.09355/) (2022)
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- [ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis](https://mlsanity.com/paper/2306.04948/) (2023)
- [Example-based Explanations with Adversarial Attacks for Respiratory Sound Analysis](https://mlsanity.com/paper/2203.16141/) (2022)
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- [Exploring the Long Short-Term Dependencies to Infer Shot Influence in Badminton Matches](https://mlsanity.com/paper/2109.06431/) (2021)
- [Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes](https://mlsanity.com/paper/2211.13638/) (2022)
- [Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities](https://mlsanity.com/paper/2401.07871/) (2024)
- [Causality-Inspired Taxonomy for Explainable Artificial Intelligence](https://mlsanity.com/paper/2208.09500/) (2022)
- [Inductive Granger Causal Modeling for Multivariate Time Series](https://mlsanity.com/paper/2102.05298/) (2021)
- [Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks](https://mlsanity.com/paper/2407.01979/) (2024)
- [Large Language Models are Interpretable Learners](https://mlsanity.com/paper/2406.17224/) (2024)
- [Multi-Stage Prototype Learning for Interpretable Time Series Classification](https://mlsanity.com/paper/2106.09636/) (2021)
- [This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text](https://mlsanity.com/paper/2210.08500/) (2022)

## Abstract (arXiv)

One of the major challenges in machine learning nowadays is to provide predictions with not only high accuracy but also user-friendly explanations. Although in recent years we have witnessed increasingly popular use of deep neural networks for sequence modeling, it is still challenging to explain the rationales behind the model outputs, which is essential for building trust and supporting the domain experts to validate, critique and refine the model. We propose ProSeNet, an interpretable and steerable deep sequence model with natural explanations derived from case-based reasoning. The prediction is obtained by comparing the inputs to a few prototypes, which are exemplar cases in the problem domain. For better interpretability, we define several criteria for constructing the prototypes, including simplicity, diversity, and sparsity and propose the learning objective and the optimization procedure. ProSeNet also provides a user-friendly approach to model steering: domain experts without any knowledge on the underlying model or parameters can easily incorporate their intuition and experience by manually refining the prototypes. We conduct experiments on a wide range of real-world applications, including predictive diagnostics for automobiles, ECG, and protein sequence classification and sentiment analysis on texts. The result shows that ProSeNet can achieve accuracy on par with state-of-the-art deep learning models. We also evaluate the interpretability of the results with concrete case studies. Finally, through user study on Amazon Mechanical Turk (MTurk), we demonstrate that the model selects high-quality prototypes which align well with human knowledge and can be interactively refined for better interpretability without loss of performance.