KW - ATTN : Knowledge Infused Attention for Accurate and Interpretable Text Classification (2021)

Abstract: Text classification has wide-ranging applications in various domains. While neural network approaches have drastically advanced performance in text classification, they tend to be powered by a large amount of training data, and interpretability is often an issue. As a step towards better accuracy and interpretability especially on small data, in this paper we present a new knowledge-infused attention mechanism, called KW-ATTN (KnoWledge-infused ATTentioN) to incorporate high-level concepts from external knowledge bases into Neural Network models. We show that KW-ATTN outperforms baseline models using only words as well as other approaches using concepts by classification accuracy, which indicates that high-level concepts help model prediction. Furthermore, crowdsourced human evaluation suggests that additional concept information helps interpretability of the model.

## introduction text classification is a fundamental natural language processing (nlp) task which has wideranging applications such as topic classification @xcite , fake news detection @xcite , and medical text classification @xcite . the current state-of-the-art approaches for text classification use neural network (nn) models. when these techniques are applied to real data in various domains, there are two problems. first, neural approaches tend to require large training data, but it is often the case that large training data or pretrained embeddings are not available in domain-specific applications. second, when text classification is applied in real life, not only the accuracy, but also the interpretability or explainability of the model is important. as a way to improve interpretability as well as accuracy, incorporating high-level concept information can be useful. high-level concepts could help interpretation of model results because concepts summarize individual words. the concept "medication" would be not only easier to interpret than the words "ibuprofen" or "topiramate" but also contributes to understanding the words better. in addition, higher-level concepts can make raw words with low frequency more predictive. for instance, the words "hockey" and "archery" might not occur in a corpus frequently enough to be considered important by a model, but knowing that they belong to the concept "athletics" could give more predictive power to the less frequent individual words depending on the task, because the frequency of the concept "athletics" would be higher than individual words. in this paper we present a new approach that incorporates high-level concept information from external knowledge sources into nn models. we devise a novel attention mechanism, kw-attn, that allows the network to separately and flexibly attend to the words and/or concepts occurring in a text, so that attended concepts can offer information for predictions in addition to the information a model learns from texts or a pretrained model. we test kw-attn on two different tasks: patient need detection in the healthcare domain and topic classification in general domains. data is annotated with high level concepts from external knowledge bases: babelnet @xcite and umls (unified medical language system) @xcite . we also conduct experiments and analyses to evaluate how high-level concept information helps with interpretability of resultant classifications as well as accuracy. our results indicate that kw-attn improves both classification accuracy and interpretability. our contribution is threefold: (1) we propose a novel attention mechanism that exploits highlevel concept information from external knowledge bases, designed for providing an additional layer of interpretation using attention. this attention mechanism can be plugged in different architectures and applied in any domain for which we have a knowledge resource and a corresponding tagger. section 2 relates prior work to ours. section 3 explains our method. section 4 evaluates our model on two different tasks in terms of classification accuracy. section 5 describes our human evaluation on interpretability. section 6 concludes. ## human evaluation on interpretability we use human evaluation to see whether additional high-level concept information given by kw-attn can be beneficial for interpretation. we compare top-ranked attended words/concepts by kw-attn with top-ranked attended words by attn. we use amazon mechanical turk (mturk). since we use crowdsourcing, we conduct evaluation only on the yahoo reviews dataset for topic classification, which covers general domains. ## conclusion we presented a new attention mechanism, kw-attn, which extends a nn model by incorporating high-level concepts. our experiments showed that using high-level concept information improves predictive power by helping the data sparseness problem in small data. furthermore, in our crowdsourcing experiments, we found significant improvement on the confidence of human evaluators on predictions, suggesting that our new attention mechanism provides benefits in explaining the predictions. high-level concepts provide an additional layer of information above raw words that can assist in understanding predictions. additionally, our attention mechanism can distinguish between the importance of words vs. concepts, providing further information. we are optimistic that kw-attn can be applied widely.