Less is More: Attention Supervision with Counterfactuals for Text Classification (2020)

Abstract: We aim to leverage human and machine intelligence together for attention supervision. Specifically, we show that human annotation cost can be kept reasonably low, while its quality can be enhanced by machine self-supervision. Specifically, for this goal, we explore the advantage of counterfactual reasoning, over associative reasoning typically used in attention supervision. Our empirical results show that this machine-augmented human attention supervision is more effective than existing methods requiring a higher annotation cost, in text classification tasks, including sentiment analysis and news categorization.

## introduction the practical importance of attention mechanism has been well-established, for both (a) improving nlp models @xcite , and also (b) enhancing human understanding of models @xcite . this paper pursues the former direction, but unlike existing models, typically using attention in "unsupervised" nature. adding human supervision to attention has been shown to improve model predictions and explanations @xcite . for example, consider a review in @xcite "this place is small and crowded but the service is quick". models with unsupervised attention may attend highly on "quick", a generic strong signal for restaurant reviews, but one may supervise to focus on "crowded" to guide models to predict a negative sentiment correctly. for this goal, attention supervision task @xcite treats attention as output variables so that models can be trained to generate similar attention to human supervision. we categorize such human supervision into the following two levels: a naive belief would be assuming the former with a higher annotation cost is more effective at supervising the model's attention. our key claim, in contrast, is that requiring more annotation, or, sample-specific supervision, can be less effective than requiring less from human then augmenting it by machine (less-is-more-hypothesis). similar skepticism on asking more, or sample-level rationales from humans, was explored in @xcite , where machine attention from large additional annotations was more effective supervisions than rationales. in this paper, we validate less-is-more without additional annotation overhead, by proposing a holistic approach of combining both human annotation and machine attention. key distinctions from @xcite are (a) humans annotate even less, and (b) without additional training resources. specifically, we start by loosening the definition of human annotation @xcite into the task-level annotation: it reduces annotation cost to the size of vocabulary, or often to zero, when public resources such as sentiment lexicon replace such annotation. we show the effectiveness of this zero-cost supervision, for both sentiment classification and news categorization scenarios, after our proposed adaptation. ## less is more for attention supervision sample-level annotation is reportedly too expensive in many practical settings @xcite , and is far difficult for humans to capture the dependency with corresponding class labels. in contrast, annotators may select important words for a target task, namely task-level attention annotation (def. 3.1), without looking up individual samples and their labels. definition 3.1 (task-level attention annotation) assuming the existence of the vocabulary v, the vocab-level annotation a @xmath0, 1} |v| is a binary vector of the hard selection for words in v, i.e., ∀w @xmath1: a task (w t ) ∈ {0, 1}. based on a task , when given an input sample x, we can use a proxy of the sample-level annotation a, i.e., ∀w @xmath2: a(w t ) = a task (w t ). as shown in tab. 1, the annotation space, which is referred to as a word set size for annotation, is 10∼36 times smaller at task-level than at samplelevel. generally, the vocabulary size is far smaller than the total number of word occurrences in training samples. our goal is thus to keep annotation cost cognitively reasonable @xcite , leaving machine self-supervision to close the annotation quality gap (sec. 3.1 and 3.2). meanwhile, we present a setup of zero-cost supervision, which allows us attention supervision without any human efforts in all scenarios using public resources and tools (sec. 3.3). ## results and discussion we now proceed to empirically validate the effectiveness of sana, compared to unsupervised attention, and attention supervision approaches using either task-level or sample-level annotations as baselines (shortly, unsupervised, task-level, and sample). for task-level annotations (e.g., in sana), we adopt pre-annotated task-level annotations without any additional human efforts: for the two sentiment tasks, we use sentiwordnet @xcite , and for 20ng task, we use entities recognized by allennlp ner @xcite . we thus present the empirical findings for the following four research questions: rq1: does sana improve model accuracy? rq2: does sana improve model robustness? rq3: is sana effective for data-scarce cases? rq4: does sana improve attention explainability? ## conclusion & future work we studied the problem of attention supervision, and showed that requiring sample-level human supervision is often less effective than task-level alternative with lower (and often zero-) overhead. specifically, we proposed a counterfactual signal for self-supervision, to augment task-level human annotation, into sample-level machine attention supervision, to increase both the accuracy and robustness of the model. we hope future research to explore scenarios where human intuition is not working as well as text classification, such as graph attention @xcite .