Does BERT Learn as Humans Perceive? Understanding Linguistic Styles through Lexica (2021)

Abstract: People convey their intention and attitude through linguistic styles of the text that they write. In this study, we investigate lexicon usages across styles throughout two lenses: human perception and machine word importance, since words differ in the strength of the stylistic cues that they provide. To collect labels of human perception, we curate a new dataset, Hummingbird, on top of benchmarking style datasets. We have crowd workers highlight the representative words in the text that makes them think the text has the following styles: politeness, sentiment, offensiveness, and five emotion types. We then compare these human word labels with word importance derived from a popular fine-tuned style classifier like BERT. Our results show that the BERT often finds content words not relevant to the target style as important words used in style prediction, but humans do not perceive the same way even though for some styles (e.g., positive sentiment and joy) human- and machine-identified words share significant overlap for some styles.

## introduction to express their interpersonal goal and attitude, people often use different styles in their communication. the style of a text can be as important as its literal meaning for effective communication @xcite . nlp researchers have built many models to identify different styles in text, including politeness @xcite , emotion @xcite , and sentiment @xcite . recently, transformerbased @xcite pretrained language models, such as bert @xcite , have achieved impressive performance on many nlp tasks, including stylistic studies. however, explaining what these deep learning models learn remains in this work, we attempt to understand style variation through the contrasting words identified by humans and bert as determining a style. given the subjective nature of styles, we are interested in capturing human's inherent perception of stylistic cues in the text and compare this with the bert's "perception". specifically, we investigate the extent to which bert's word importance, as estimated using shapley value-based attribution scores @xcite , aligns with human perception in stylistic text classification. when humans identify styles in a text, specific words play an important role in recognizing the style, such as hedges for identifying politeness @xcite . we call such words stylistic cues. for example, in figure 1(a), humans perceive the words "understand," "like," and "accept" as strong stylistic cues for politeness. but does the bert model learn the same words as indicative? it turns out that although the model learns that the word "accept" is an important feature for classifying the text as polite, it disagrees with humans for "understand" and "like" by identifying these words as signals for impoliteness. this leads to a concern that lexical explanation from bert could be unreliable and motivates us to investigate more deeply into the lexical cues used by humans and bert. since styles overlap significantly @xcite , we cover multiple styles: politeness, sentiment, offensiveness, anger, disgust, fear, joy, and sadness. our contributions are as follows: ## collection of human and bert's importance scores on stylistic words while there are many datasets with stylistic labels, to the best of our knowledge, there is no available dataset of stylistic texts with human labels on the individual words that drive the human perception. therefore, on top of existing benchmark style datasets, we we develop hummingbird, a new dataset with human-identified stylistic words in those stylistic sentences . dataset we use the following datasets for our style classification tasks and as a starting point for collecting human perception scores on lexical level: stanfordpoliteness (danescu- @xcite , sentiment treebank @xcite , @xcite 's tweet dataset for offensiveness, and semeval 2018's dataset for emotion classification @xcite . human perception scores to collect human perception scores, we first pick 500 stylisticallydiverse texts from the four style datasets by the following method. first, we fine-tune bert on the training sets of the exiting datasets using the original train/dev/test splits. the models' performance is shown in to encourage that the chosen texts exhibit diverse styles, we sort them based on their probability scores and compute the standard deviation of these scores across the eight styles, following @xcite . we then select the 50 most polite texts, 50 most impolite texts, 50 positive texts, 50 negative texts, 100 offensive texts, and 200 emotional texts (40 from each emotion style), resulting in total 500 texts from the four different style datasets. we hired 622 workers to annotate them with human perception on prolific foot_0 from november to december 2020. we required the workers to be in the united states and payed them an average of $9.6/hour. each worker was asked what styles they perceive each of the texts to exhibit. if they think the text has certain styles, workers then highlight the words in the text which they believe make them think the text had those styles (pink highlights in figure 1 ). three workers label the same pair of sentence and style, and we take majority voting for the style labels. politeness positive sentiment joy ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ lovely hilarious disappointed delightful deep shocking excited moved movies * delightful thank scenes * lovely thanks scare love share managing loving moved suffers smart fun move entertaining performances referring smart good hi # solid deftly absolutely great congrats documentary trouble clear optimism excited best wow # perfect smile baseball * ## human-bert agreement through lexical analysis we study how similar human perception and bert's word importance are, within each style (intra-style) and across styles (multi-styles). ## conclusion we showed that bert's word importances for style prediction, as calculated using integrated gradients, correspond only very loosely with the word importances given by human annotators. these differences likely result from several factors: 1) word-importances computed for words which appear rarely in the text tend to be noisy. 2) bert, as a contextual pretrained model, take more context into account for deciding the style of the text while human intuitively choose the most obvious "stylistic" words to judge the style of the text. 3) styles are subjective matter, so human annotators may have different perception toward the style of a sentence. future directions this work also provides a public dataset as a first step for researchers to further investigate these issues. we plan to scale up our data collection in their size and style types including other higher-level of styles such as sarcasm and humor. we also explore a possibility of informing bert to pay more attention on humanannotated lexica. limitations we acknowledge that while the inter-annotator agreement for the sentence-level style is quite high, there is a huge variation for the word-level agreement. as a caveat, the annotators could be unreliable. we do find that annotators label different words as being important than those that drive bert predictions. note that we do not claim that bert is "wrong" and humans are "always reliable"; only that they are different. bert's important words can help the model predict correctly, but they are perceived as stylistic features as humans do. studying this difference is our major goal of this paper. we believe that if a word is perceived as "stylistic" by the majority of people, this word can be regarded as an important cue for the model. learning this variability of human perception on styles could be an interesting future work using hummingbird.