Human Rationales as Attribution Priors for Explainable Stance Detection (2021)

Abstract: As NLP systems become better at detecting opinions and beliefs from text, it is important to ensure not only that models are accurate but also that they arrive at their predictions in ways that align with human reasoning. In this work, we present a method for imparting human-like rationalization to a stance detection model using crowdsourced annotations on a small fraction of the training data. We show that in a data-scarce setting, our approach can improve the reasoning of a state-of-the-art classifier—particularly for inputs containing challenging phenomena such as sarcasm—at no cost in predictive performance. Furthermore, we demonstrate that attention weights surpass a leading attribution method in providing faithful explanations of our model’s predictions, thus serving as a computationally cheap and reliable source of attributions for our model.

## introduction stance detection, automatically identifying the position on a topic taken by a text @xcite , allows readers to glean valuable information from news articles and social media, such as whether the writing is politically slanted. due to the sensitive nature of many topics (e.g., political ideologies and religious beliefs), it is crucial that stance models are transparent and rationalize their predictions in human-like ways. furthermore, their reasoning must remain human-like even when they are tasked with generalizing to a new, unseen test topic. a model's rationale for a specific input can be extracted in the form of feature attributions, which quantify the influence that each input feature asserts on the model's predictions. methods for obtaining these attributions vary by faithfulness (the extent to which they accurately measure the importance of each feature). attributions can be incorporated into the training process via attribution priors, a powerful framework for imparting domain knowledge @xmath0, though, how many people who have used marijuana do not use other illegal drugs. i think the answer would surprise you. correlation is not causation -the sooner you learn that, the sooner you can get to the real root of our opiate addition problem. prediction against (wrong) our method the better question is, though, how many people who have used marijuana do not use other illegal drugs. i think the answer would surprise you. correlation is not causation -the sooner you learn that, the sooner you can get to the real root of our opiate addition problem. prediction for (correct) to models @xcite . in this work, we propose a prior that penalizes a stance classifier for producing attributions that deviate from human judgements of word importance (i.e., which words or phrases in a text are most indicative of stance). notably, our method is model-agnostic and can be used with any differentiable feature attribution technique. we train and evaluate our model on the vast (varied stance topics) dataset, whose test set covers topics that are either absent from (zero-shot) or scarce in (few-shot) the training data @xcite . to construct our attribution prior, we crowdsource word importance annotations on a small subset (∼500 examples) of the training data, as well as on a sample of the test data for evaluation. additionally, to assess how our method might fare in realistic, resource-limited scenarios, we experiment using not only the complete vast train set, but also reduced versions of it. as the attribution method for our prior, we choose mean attention weights (maw), a method that is computationally cheap in comparison to popular alternatives. although numerous recent publications have demonstrated that attention weights do not in general provide faithful explanations of model behavior, we find strong evidence that the attributions offered by maw are more faithful to our model's predictions than gradient × input (gi), a leading attribution method for transformer models. our contributions are as follows: ## related work while early work on stance detection focused primarily on ideological debates @xcite @xcite , recent datasets have also begun to include more political topics, such as elections @xcite @xcite and referendums @xcite . this reflects a growing interest in developing models to understand public opinion on a range of topics. however, to be used in real-world scenarios, such models must also exhibit good generalization ability and a degree of transparency. recent work has focused on the generalization ability of stance detection models: across topics @xcite @xcite @xcite , languages @xcite , and even label sets and genres @xcite . in contrast, our work focuses on the reasoning of models during topic generalization. many attribution methods have been used to extract rationales from text classifiers, including activation-based @xcite , gradient-based, and attention-based @xcite methods. furthermore, numerous works incorporate these model attributions into the training process. @xcite train a toxicity classifier using an attribution prior based on integrated gradients, a gradient-based method of feature attribution @xcite . @xcite directly train an attention mechanism for relation extraction. previous studies have used word importance annotations much like ours to supervise attention with the goal of improving predictive performance @xcite . our work, in contrast, focuses on verifiably improving the reasoning of a classifier. while there exist models whose reasoning paths are naturally transparent and easy to train, such as select-predict @xcite and rationale-augmented @xcite ) models, our framework makes no assumptions about model architecture, and can thus be applied to a state-of-the-art stance classifier. a recent survey of explainability (i.e., attribution) methods proposed five diagnostic properties for comparing techniques, including faithfulness, defined as a measure of how true attributions are to the inner workings of a model @xcite . their experiments with transformer models show that gradient-based methods (e.g., gi) score high in faithfulness. in fact, similar assessments of faithfulness have found that attention does not provide faithful explanations @xcite , especially compared to gi @xcite . however, recent studies have argued for a more nuanced understanding of faithfulness that prioritizes 'explainable enough' @xcite and allows for the 'best' technique to vary by model, task, or input @xcite . our work presents a scenario in which attention is in fact more faithful than gi. ## methods we propose a model for stance detection that uses a bert-based encoder ( §4.1) trained with an additional loss term ( §4.2) designed to impose a prior based on human rationales. define @xmath1, t i , y i )} n @xmath2, each consisting of an argument d i , a topic t i , and a stance label y @xmath3, 1, -1}. in addition, let m θ be some model with parameters θ. then we can define for each example x a set of oracle attributions s, model attributions a, and penalty weights γ (the contribution of each token to our loss term). our prior loss term encourages the model to produce, for each example, attributions a that are very "similar" to s ( §4.2-4.3). ## conclusion this paper addresses two issues concerning the task of stance detection: 1) the need for models whose reasoning aligns with that of humans and 2) the need for a way to meaningfully observe the reasoning of models in the first place. we find that in a simulated data-scarce setting, our attribution prior improves model rationales using a practical volume of crowdsourced annotations. we also find that attention-based explanations, which have recently been the subject of much criticism, provide faithful explanations of our models' behavior, more so than a high-ranking alternative method. in future work we plan to apply our method to more challenging settings, such as multilingual zero-shot stance detection. we will also further investigate the "economics" of our method-for instance, the number of annotated examples necessary to meaningfully improve model reasoning-as well as experiment with a broader range of attribution methods, e.g., guided backpropagation @xcite and lime @xcite . lastly, we hope to study how to condition model reasoning to protect against adversarial attacks.