论文标题
在NLP中进行解释：通过单词属性分析和计算单词显着性
Towards Explainability in NLP: Analyzing and Calculating Word Saliency through Word Properties
论文作者
论文摘要
在自然语言处理中的广泛使用黑框模型给对决策基础的理解，预测结果的可信度以及改善模型性能带来了巨大挑战。文本样本中的单词具有反映其语义和上下文信息的属性，例如语音，位置等。这些属性可能与显着性一词具有一定的关系，这有助于研究模型预测的解释性。在本文中，我们探讨了“显着性”一词和属性一词之间的关系。根据分析结果，我们进一步建立了一个映射模型SEQ2SALITY，从文本样本中的单词及其属性到基于序列标记的概念的显着性值。此外，我们建立了一个名为PRSALM的新数据集，该数据集包含文本示例中的每个单词，单词属性和一词显着值。进行实验评估以分析具有不同特性的单词的显着性。验证了SEQ2Sality模型的有效性。
The wide use of black-box models in natural language processing brings great challenges to the understanding of the decision basis, the trustworthiness of the prediction results, and the improvement of the model performance. The words in text samples have properties that reflect their semantics and contextual information, such as the part of speech, the position, etc. These properties may have certain relationships with the word saliency, which is of great help for studying the explainability of the model predictions. In this paper, we explore the relationships between the word saliency and the word properties. According to the analysis results, we further establish a mapping model, Seq2Saliency, from the words in a text sample and their properties to the saliency values based on the idea of sequence tagging. In addition, we establish a new dataset called PrSalM, which contains each word in the text samples, the word properties, and the word saliency values. The experimental evaluations are conducted to analyze the saliency of words with different properties. The effectiveness of the Seq2Saliency model is verified.
