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Understanding Hidden Memories of Recurrent Neural Networks

Yao Ming, Shaozu Cao, Ruixiang Zhang, Zhen Li, Yuanzhe Chen, Yangqiu Song, Huamin Qu

arXiv:1710.10777v1cs.CLcs.AI

TL;DR

RNNs achieve strong NLP performance, but their internal mechanisms and sequence-modeling behavior remain insufficiently understood. The paper presents RNNVis, a visual analytics system that interprets hidden states through expected responses and co-clusters hidden units with words; case studies and expert reviews demonstrate its effectiveness for understanding and comparing RNN models.

  • Problem

    The mechanisms behind RNN effectiveness, including how internal memory updates produce sequence behavior, remain insufficiently understood.

  • Method

    RNNVis interprets hidden-state units using expected responses to input words and models hidden-unit–word relations as a weighted bipartite graph for co-clustering and visualization.

  • Results

    Case studies and expert interviews demonstrated RNNVis’s effectiveness for understanding and comparing different RNN models and verified the completeness of its design requirements.

  • Takeaways & Limitations

    Interactive visualizations of expected responses, memory chips, and word clouds allow domain users to explore and understand RNNs’ internal behavior.

  • Takeaways & Limitations

    RNNVis is limited to text-based RNN models, while co-clustering efficiency and quality can cause interaction delays.

Abstract

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Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.

1 INTRODUCTION

RNNs are effective but difficult to understand internally, especially how their hidden memories work. RNNVis addresses this gap with visual analytics for interpreting, structuring, and comparing hidden states.

  • Motivation: RNNs perform well across NLP applications, but their internal hidden-state behavior remains difficult for humans to understand.The challenge is intensified by many nonlinear hidden-state updates and large numbers of parameters.
  • Related limitations: Existing RNN analyses have interpreted some individual units or explored selected patterns, but usually address only limited aspects of model behavior.Prior work identified units associated with language context and supported expert exploration of selected hidden states.
  • Proposed system: RNNVis interprets hidden-state units through their expected responses to input words and organizes hidden states and words into co-clusters.The system represents relations as a weighted bipartite graph, then visualizes clusters as memory chips and word clouds.
  • Proposed system: The system also provides glyph-based sequence visualization to analyze sentence-level hidden-state behavior.Its design includes system construction, expected-response analysis, and sequence visualization.
  • Evaluation: Case studies analyze hidden mechanisms in RNNs with different architectures using the proposed system.The case studies are presented as a major contribution of the work.

2 RELATED WORK

Related work has used performance changes, attention, sequence projections, heat maps, and visual analytics to study neural models. RNNVis builds on co-clustering and comparative visualization to examine hidden-state and word relations more directly.

  • RNN analysis approaches: Performance-based studies compare architectures by altering components and measuring relative performance changes, revealing overall differences rather than internal mechanisms.Examples include studies of LSTM components, GRU-versus-LSTM performance, and automated architecture searches.
  • RNN analysis approaches: Attention-based models can improve task performance while providing interpretive relationships, but they require an attention mechanism and are task-specific.Prior examples include machine translation and image captioning.
  • Visualization for machine learning: Visualization has increasingly supported machine-learning analysis, debugging, presentation, and human-in-the-loop feature or model workflows.These applications span visual classification, optimization, and feature engineering.
  • Visualization for RNNs: RNN visualizations have projected sequence history or used heat maps, but these approaches generally provide overall analysis rather than detailed hidden-state exploration.This leaves the internal behavior of individual hidden states less directly examined.
  • Co-clustering and comparative visualization: RNNVis formulates hidden-state units and discrete inputs as a bipartite graph and uses co-clustering plus juxtaposition and superposition for comparison.Its visualizations represent clustered hidden states and words while supporting detail-, sentence-, and overview-level comparisons.

3 BACKGROUNDS

RNNs process sequences by updating a hidden state from each input, with variants and input-output schemes suited to different tasks. The background introduces vanilla recurrence, softmax classification, memory interpretation, and common sequence mappings.

  • Recurrent neural networks: A vanilla RNN maintains a time-varying hidden state and updates it from the current input and previous hidden state.The model processes sequential inputs x(0), …, x(T), with x(t) in R^m and h(t) in R^n.
  • Recurrent neural networks: With tanh activation, the hidden-state values are constrained to the range (−1,1).The recurrence uses weight matrices W and V together with a nonlinear activation function f.
  • Softmax output: After sequence processing, a probability distribution over K classes can be computed for classification using the final hidden state and an output projection.This is the softmax-output setting described for step T.
  • RNN memory: RNN memory is updated step by step as inputs arrive, and outputs depend on the current memory.This motivates treating hidden states as memory-like structures in the paper’s analysis.
  • Variants and input-output schemes: The paper considers LSTM and GRU variants, along with multi-layer models, and distinguishes sequence-to-one, sequence-to-sequence, and synced sequence-to-sequence schemes.These schemes support tasks such as sentiment analysis, document classification, machine translation, language modeling, and video classification.

4 SYSTEM DESIGN

RNNVis was designed through requirements analysis and iterative collaboration with deep-learning and NLP experts. Its central focus is intuitive hidden-state interpretation for model diagnosis and architecture exploration.

  • Design process: The system-design section frames the requirements for interpreting and analyzing RNN hidden states and points to the proposed techniques and visual designs.The techniques are presented in Section 5 and the visual designs in Section 6.
  • Design process: RNNVis was developed through iterative discussions with two domain experts in deep learning, natural language understanding, and text mining.Those discussions determined algorithm-design and visual-encoding choices.
  • Design goals: The system aims to provide intuitive interpretations of hidden states, support RNN diagnosis, and inform better architecture designs.These goals motivated the formulation of specific system requirements from expert discussions and literature review.

R1 Clearly interpret the information captured by hidden

Existing visualizations either select a few hidden units or map hidden-state values directly, but they do not explain the semantic information captured by each unit.

  • Current methods provide limited hidden-state views by selecting a few units or directly mapping values to visualizations.Because interpretable information is distributed across hidden states, direct visualization does not explain each hidden unit.

R2 Provide the overall information distribution in hidden

RNNVis addresses the need for an overview of how information is distributed and correlated across hidden states while supporting sequence-level and model-comparison analysis.

  • Experts need an overview of how semantic information is differentiated and correlated across hidden states.
  • Sequence-level analysis is needed because word-level visualization cannot reveal how internal memory updates produce sequence behavior.
  • Experts also require concise statistics, including hidden-state distributions and gate activations, for quantitative analysis.
  • Comparing architectures should expose internal reasons for performance differences and mechanisms supporting LSTMs’ long-term dependencies.
  • RNNVis combines a model manager, RNN evaluator, and interactive visualization module for managing, evaluating, and exploring RNN models.

5 RNN EVALUATOR

The RNN evaluator explains hidden units through expected responses to words, organizes word–unit relations through co-clustering, and summarizes sequence-level hidden-state behavior with aggregate measurements.

  • 5.1 Interpreting hidden states: RNNVis uses expected hidden-state updates to interpret individual units with words or characters, illustrated using a word-level model.
  • 5.1 Interpreting hidden states: The expected response stabilizes a word’s influence measure by averaging hidden-state updates across varying preceding histories.
  • 5.1 Interpreting hidden states: A larger absolute expected response indicates that a word is more salient or important to a hidden-state unit.
  • 5.1 Interpreting hidden states: For each unit, the explanation consists of m words with the largest absolute expected responses.
  • 5.1 Interpreting hidden states: For LSTMs, expected response is computed from cell-state updates because the cell state is considered to maintain long-term memory.
  • 5.1 Interpreting hidden states: In the PTB example, “he” and “she” produce similar response distributions, whereas “for” produces a substantially different pattern.
  • 5.2 Co-clustering Hidden States and Words: Word–unit relations are represented as weighted edges in a bipartite graph and jointly clustered across word and hidden-unit spaces.
  • 5.2 Co-clustering Hidden States and Words: Co-clustered word clouds and memory chips provide an overview in which word-cluster semantics also interpret corresponding hidden-state clusters.

6 VISUALIZATION DESIGN

RNNVis combines memory chips, word clouds, linked co-clusters, and glyph-based sequence nodes to support overview, interpretation, and sentence-level analysis of RNN hidden states. Interactive comparisons extend across hidden-state units and words, sentences, and models.

  • Main View: RNNVis organizes hidden state clusters as memory chips and word clusters as word clouds in a linked three-part main view.Memory cells are packed into rectangular chips, while word clouds summarize cluster interpretations and support navigation.
  • Main View: Each hidden unit is rendered by its estimated expected response to a selected word, using a blue-to-orange scale for response values.The selected word is represented by its embedding x, and each hidden unit h_j receives an estimated expected response s(x)_j.
  • Main View: Cluster-to-cluster edges aggregate word–hidden-state connections, with edge width encoding average correlation and color distinguishing negative from positive weights.Spectral co-clustering pairs hidden-state and word clusters, allowing corresponding clusters to be aligned and reducing visual clutter.
  • Sequence Visualization: A glyph-based sequence visualization represents sentence words as nodes and encodes aggregate and updated information for hidden-state clusters.Bars show aggregate information, colored hats show updated information, and links indicate the absolute average updated information between words and clusters.
  • Sequence Visualization: Experts preferred the composite bar glyph because pie charts cannot compare aggregate and updated information simultaneously, while variable-width bars obscure small clusters.The selected glyph preserves a consistent cluster order and uses color to distinguish increases or decreases in positive and negative information.
  • Layout and Interaction: Alternative layouts were rejected because t-SNE plus force-directed links does not scale, whereas arc-based layouts complicate comparative analysis and association with hidden states.RNNVis instead provides interactive exploration and three comparison levels: unit/word, sentence, and model.

7 EVALUATION

RNNVis was evaluated through language-modeling and sentiment-analysis case studies plus expert reviews. The studies used visual comparisons to examine hidden-state behavior, diagnose dataset imbalance, and assess system effectiveness and usability.

  • Evaluation design: The evaluation combined two RNN case studies with one-to-one interviews with domain experts.The case studies examined language modeling and sentiment analysis.
  • 7.2 Case Study: Sentiment Analysis: Co-clustering grouped words with similar linguistic functions, including prepositions and modal verbs, while response distributions for word pairs showed similar functions.Examples included “with”, “for”, “by”, “as” and “would”, “could”, “should”, “will”.
  • 7.1 Case Study: Language Modeling: In language modeling, RNNVis showed that vanilla RNN responses were stronger and more intensive than LSTM responses, whereas LSTM responses were sparser.The authors relate this observation to LSTM advantages for long-term dependencies.
  • 7.1 Case Study: Language Modeling: GRU hidden-state response distributions were closer to LSTM distributions than to vanilla RNN distributions, providing an empirical explanation for their similar reported performance.The paper states that this finding agrees with a recent empirical result.
  • 7.2 Case Study: Sentiment Analysis: GRU co-clustering separated negative and positive words, with hidden units using positive and negative values to encode opposite sentiments.The upper memory chip reacted positively to negative words, while the lower chip reacted positively to positive words.
  • 7.2 Case Study: Sentiment Analysis: 91.52% validation accuracy and 91.91% test accuracy were achieved after oversampling the imbalanced sentiment dataset to create a balanced dataset.The retrained model’s visualization included stronger negative and positive sentiment words, and “worst” and “excellent” had nearly opposite response patterns.
  • 7.3 Expert Reviews: Experts correctly inferred model-performance differences, agreed that RNNVis was effective and usable, and found its cognitive load acceptable after introduction and guidance.One expert initially mistook memory chips for network layers, indicating a need for clearer introductory documentation.
  • 7.3 Expert Reviews: Experts suggested programmable visualizations, inference results during sequence visualization, simpler presentation views, and extensions to additional applications and advanced RNN models.Suggested extensions included machine reading, attention mechanisms, and memory networks.

8 CONCLUSION

The paper presents RNNVis, a visual analytics method for understanding and diagnosing text-based RNNs. Its expected-response technique, co-clustering views, and evaluation support interactive exploration and comparison of hidden-state behavior, while several scope and efficiency limits remain.

  • RNNVis interprets information stored in RNN hidden states through their expected responses to textual inputs.
  • The system combines memory chips and word clouds in interactive co-clustering visualizations for exploring, understanding, and comparing different RNN models.
  • Glyph-based sequence visualization analyzes sentence-level RNN behavior using aggregate information.
  • Two case studies and expert interviews demonstrated the effectiveness of RNNVis for understanding and comparing RNN models and verified the completeness of the design requirements.
  • RNNVis is currently limited to text-based RNN analysis, while co-clustering efficiency and quality can cause interaction delays.Future work includes audio representations, quantitative model measurements, online deployment, and support for memory networks or attention models.

A.1 Long Short-Term Memory

This appendix describes LSTM, GRU, and stacked multi-layer RNN architectures. It explains how gates regulate memory updates and how deeper models propagate representations across layers.

  • A.1 Long Short-Term Memory: LSTM maintains a hidden state and a separate cell state, with input, forget, and output gates controlling their updates.The gate values range from 0 to 1 and represent update, forgetting, and output proportions.
  • A.2 Gated Recurrent Unit: GRU uses one hidden state vector and two gates, update and reset, to control hidden-memory updates.
  • A.2 Gated Recurrent Unit: GRU updates its hidden state by interpolating between the previous hidden state and a candidate hidden state using the update-gate ratio.
  • A.3 Multi-layer Models: Stacking recurrent layers gives each time step a hidden-state vector for every layer, with each layer receiving the previous layer’s representation.For the first layer, the model input x(t) serves as h^0(t).

APPENDIX B CASE STUDY: THE LANGUAGE OF SHAKESPEARE

RNNVis was used to study a Shakespeare language model by comparing ancient and modern word usage and examining hidden-state representations across layers. The case studies found similar responses for most old–new word pairs, while layer 1 formed clearer and sparser word representations than layer 0.

  • Training a language model on plain texts: The Shakespeare case study used RNNVis to validate hypotheses about hidden-state behavior and demonstrate support for new models and datasets.The dataset contained Shakespeare’s work, and the system provided a pipeline for tokenization and model configuration.
  • A mixture of the old and the new: The model grouped ancient and modern auxiliary verbs such as “hath,” “dost,” and “art” with “have,” “do,” and “are,” indicating similar learned usage.The comparison concerns words with different spellings in Shakespeare’s mixture of old and new language.
  • A mixture of the old and the new: Most ancient–modern word pairs had similar response distributions, except “thou” and “you.”The reported anomaly may reflect different historical usages: “thou” was singular and informal, whereas “ye” and “you” were plural, formal, and polite.
  • Comparing different layers in one model: Layer 0 had more balanced memory-chip sizes, while layer 1 produced more consistent word-cloud colors and clearer word-function distinctions.The comparison interprets layer 0 as fuzzier and layer 1 as clearer in treating different word usages.
  • Comparing different layers in one model: Layer 1 showed sparser responses to “thou,” a property described as helpful in the final language-modeling layer before softmax projection.The layer comparison explains how multi-layer RNNs use hidden states to formulate learned representations.
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