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

翻译：理解输入对输出的重要性在许多任务中都十分有用。本文提出了一种基于信息理论的框架，用于分析文本分类任务中输入的影响。自然语言处理任务通常以单个或多个文本元素作为输入来预测输出变量，其中每个文本元素是一个文本块。每个文本元素包含两个组成部分：相关的语义含义和语言实现形式。我们选取多项选择阅读理解（MCRC）和情感分类（SC）任务来展示该框架。对于MCRC任务，研究发现，在更具挑战性的数据集上，上下文对输出的影响相比问题的影响有所减弱。具体而言，更具挑战性的上下文允许问题复杂度出现更大变化。因此，测试设计者在设计多项选择题评估时需要仔细考虑上下文的选择。对于SC任务，研究发现确定情感倾向时，输入文本的语义含义主导（在所有考虑的数据集中占比超过80%），而非其语言实现形式。该框架的代码已开源：https://github.com/WangLuran/nlp-element-influence