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VisBERT: Hidden-State Visualizations for Transformers
WWW '20: The Web Conference 2020 Taipei Taiwan April, 2020, pp.207-211, (2020)
- Understanding black-box models is an increasingly prominent area of research. While the performance of neural networks has been steadily improving in nearly every domain, the ability to understand how they work, and how they come to the conclusions they draw is only improving slowly.
- Instead of the attention values, the authors follow the work in  and visualize the hidden states between each BERT layer, and with that the token representations, as they are transformed through the network.
- VisBERT2, an interactive web tool for interpretable visualization of hidden-states within BERT models fine-tuned on Question Answering.
- Understanding black-box models is an increasingly prominent area of research
- In order for large neural networks to be confidently deployed in safety-critical applications, features like transparency, interpretability and explainability are paramount
- One such class of black-box models are Transformer models, Bidirectional Encoder Representations from Transformers (BERT) in particular. These models have become the state-of-the-art for many different NLP tasks in recent months
- VisBERT2, an interactive web tool for interpretable visualization of hidden-states within BERT models fine-tuned on Question Answering
- For each task we provide a separate fine-tuned BERT model
- VisBERT establishes a novel method to analyze the behavior of BERT models, in particular regarding the Question Answering task
- Visualizations of the inference process of unseen examples from three diverse Question Answering datasets, including three BERT models fine-tuned on these sets.
- The presented tool allows users to test the abilities and shortcomings of own Question Answering models on arbitrary samples.
- Each encoder block includes a multi-headed self-attention module, which transforms each token using the entire input context, normalization, and a Feed-Forward network at the end, which outputs the token representations used by the subsequent layer.
- The authors can observe the changing token relations that the model forms throughout the inference process.
- To that end the authors use the hidden states after each Transformer encoder block, which contains a vector for each token with a dimensionality of 768 (BERT-base) or 1024 (BERT-large).
- The authors further categorize the tokens based on affiliation to question, supporting facts or predicted answer in order to facilitate interpretability.
- In addition to the included datasets, the tool can be extended to other Question Answering tasks.
- By using the layer-slider on top of the graph, the user is able to go through all layers of the model and observe the changes within the token representations.
- This allows users to find out which QA model (SQuAD, HotpotQA or bAbI) fits a specific question type best and produces the right result.
- A user can add distracting facts to the context and check whether the model is still able to follow the same inference path.
- The authors' tool allows to observe resulting changes in the prediction, and within the hidden states of a model.
- VisBERT establishes a novel method to analyze the behavior of BERT models, in particular regarding the Question Answering task.
- The authors establish this behaviour on three diverse Question Answering datasets and make all three models available for users to make their own analyses on their own data, as well as the code to reproduce this visualization.
- The authors' tool can be extended to other BERT models, fine-tuned on different QA datasets or even other NLP tasks entirely, and to other Transformer based models like GPT-2 .
- Our work is funded by the European Unions Horizon 2020 research and innovation programme under grant agreement 732328 (FashionBrain), by the German Federal Ministry of Education and Research (BMBF) under grant agreement 01UG1735BX (NOHATE) and by the German Federal Ministry of Economic Affairs and Energy (BMWi) under grant agreements 01MD19013D (Smart-MD), 01MD19003E (PLASS) and 01MK2008D (Servicemeister)
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