A Unified Understanding of Deep NLP Models for Text Classification

@article{Li2022AUU,
  title={A Unified Understanding of Deep NLP Models for Text Classification},
  author={Zhuguo Li and Xiting Wang and Weikai Yang and Jing Wu and Zhengyan Zhang and Zhiyuan Liu and Maosong Sun and Hui Zhang and Shixia Liu},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  year={2022},
  volume={PP},
  pages={1-14}
}
The rapid development of deep natural language processing (NLP) models for text classification has led to an urgent need for a unified understanding of these models proposed individually. Existing methods cannot meet the need for understanding different models in one framework due to the lack of a unified measure for explaining both low-level (e.g., words) and high-level (e.g., phrases) features. We have developed a visual analysis tool, DeepNLPVis, to enable a unified understanding of NLP… 

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References

SHOWING 1-10 OF 50 REFERENCES

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

TLDR
A benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models, which favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks.

Attention Flows: Analyzing and Comparing Attention Mechanisms in Language Models

TLDR
The visualization, Attention Flows, is designed to support users in querying, tracing, and comparing attention within layers, across layers, and amongst attention heads in Transformer-based language models, and to help users gain insight on how a classification decision is made.

Visualizing and Understanding Neural Models in NLP

TLDR
Four strategies for visualizing compositionality in neural models for NLP, inspired by similar work in computer vision, including LSTM-style gates that measure information flow and gradient back-propagation, are described.

Towards a Deep and Unified Understanding of Deep Neural Models in NLP

We define a unified information-based measure to provide quantitative explanations on how intermediate layers of deep Natural Language Processing (NLP) models leverage information of input words. Our

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

TLDR
A Sentiment Treebank that includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality, and introduces the Recursive Neural Tensor Network.

Deep Contextualized Word Representations

TLDR
A new type of deep contextualized word representation is introduced that models both complex characteristics of word use and how these uses vary across linguistic contexts, allowing downstream models to mix different types of semi-supervision signals.

Learning Structured Representation for Text Classification via Reinforcement Learning

TLDR
Results show that the proposed reinforcement learning method can learn task-friendly representations by identifying important words or task-relevant structures without explicit structure annotations, and thus yields competitive performance.

Understanding Hidden Memories of Recurrent Neural Networks

TLDR
This paper presents a visual analytics method for understanding and comparing RNN models for NLP tasks, and proposes a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN’s hidden state at the sentence-level.

Deep Learning Based Text Classification: A Comprehensive Review

TLDR
A comprehensive review of more than 150 deep learning based models for text classification developed in recent years are provided, and their technical contributions, similarities, and strengths are discussed.