InterpreT: An Interactive Visualization Tool for Interpreting Transformers
With the increasingly widespread use of Transformer-based models for NLU/NLP tasks, there is growing interest in understanding the inner workings of these models, why they are so effective at a wide range of tasks, and how they can be further tuned and improved. To contribute towards this goal of enhanced explainability and comprehension, we present InterpreT, an interactive visualization tool for interpreting Transformer-based models. In addition to providing various mechanisms for investigating general model behaviours, novel contributions made in InterpreT include the ability to track and visualize token embeddings through each layer of a Transformer, highlight distances between certain token embeddings through illustrative plots, and identify task-related functions of attention heads by using new metrics. InterpreT is a task agnostic tool, and its functionalities are demonstrated through the analysis of model behaviours for two disparate tasks: Aspect Based Sentiment Analysis (ABSA) and the Winograd Schema Challenge (WSC).
Code (0)
등록된 구현이 없습니다.
Tasks
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Sentiment AnalysisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers
Breakthroughs in transformer-based models have revolutionized not only the NLP field, but also vision and multimodal systems. However, although visualization and interpretability tools have become available for NLP model…
Question AnsweringVisual Commonsense ReasoningVisual Question AnsweringVisual Question Answering (VQA)Interactive Visualization for Debugging RL
Visualization tools for supervised learning allow users to interpret, introspect, and gain an intuition for the successes and failures of their models. While reinforcement learning practitioners ask many of the same ques…
VISIT: Visualizing and Interpreting the Semantic Information Flow of Transformers
Recent advances in interpretability suggest we can project weights and hidden states of transformer-based language models (LMs) to their vocabulary, a transformation that makes them more human interpretable. In this pape…
WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings
Machine learning models often learn latent embedding representations that capture the domain semantics of their training data. These embedding representations are valuable for interpreting trained models, building new mo…
NavigateNeuralDivergence: Exploring and Understanding Neural Networks by Comparing Activation Distributions
As deep neural networks are increasingly used in solving high-stake problems, there is a pressing need to understand their internal decision mechanisms. Visualization has helped address this problem by assisting with int…