An Information-Theoretic Approach to Analyze NLP Classification Tasks
This work provides a tool for analyzing input importance in NLP tasks, which is incremental as it applies existing information theory to specific domains.
The authors introduced an information-theoretic framework to analyze input influence in NLP classification tasks, finding that in multiple-choice reading comprehension, context influence decreases on harder datasets, and in sentiment classification, semantic meaning dominates linguistic realization by over 80%.
Understanding the importance of the inputs on the output is useful across many tasks. This work provides an information-theoretic framework to analyse the influence of inputs for text classification tasks. Natural language processing (NLP) tasks take either a single element input or multiple element inputs to predict an output variable, where an element is a block of text. Each text element has two components: an associated semantic meaning and a linguistic realization. Multiple-choice reading comprehension (MCRC) and sentiment classification (SC) are selected to showcase the framework. For MCRC, it is found that the context influence on the output compared to the question influence reduces on more challenging datasets. In particular, more challenging contexts allow a greater variation in complexity of questions. Hence, test creators need to carefully consider the choice of the context when designing multiple-choice questions for assessment. For SC, it is found the semantic meaning of the input text dominates (above 80\% for all datasets considered) compared to its linguistic realisation when determining the sentiment. The framework is made available at: https://github.com/WangLuran/nlp-element-influence