A survey on improving NLP models with human explanations (2022)

Abstract: Training a model with access to human explanations can improve data efficiency and model performance on in- and out-of-domain data. Adding to these empirical findings, similarity with the process of human learning makes learning from explanations a promising way to establish a fruitful human-machine interaction. Several methods have been proposed for improving natural language processing (NLP) models with human explanations, that rely on different explanation types and mechanism for integrating these explanations into the learning process. These methods are rarely compared with each other, making it hard for practitioners to choose the best combination of explanation type and integration mechanism for a specific use-case. In this paper, we give an overview of different methods for learning from human explanations, and discuss different factors that can inform the decision of which method to choose for a specific use-case.

## introduction training machine learning models with human explanations is considered a promising way for interaction between human and machine that can lead to better models and happier users. if a model is provided with information about why a specific prediction should be made for an instance, it can often learn more and faster than if just given the correct label assignment @xcite . this reduces the need for annotated data and makes learning from explanations attractive for use-cases with little annotated data available, for example for adapting models to new domains @xcite or for personalizing them @xcite . human explanations also push models to focus on relevant features of the data, preventing them from fitting to spurious correlations in the data @xcite . on top of these beneficial effects on model quality, supervision in the form of explanations is in line with e-snli premise a 2-3 year old blond child is kneeling on a couch. hypothesis the child has brown hair. ## learning from explanations highlight and free-text explanations are the most prominent explanation types used to improve nlp models @xcite . highlight explanations (highlight) are subsets of input elements that are deemed relevant for a prediction. 2 for text-based nlp tasks, they correspond to sets of words, phrases or sentences. free-text explanations (free-text) are texts in natural language that are not constrained to be grounded in the input elements and contain implicit or explicit information about why an instance is assigned a specific label. some recent works rely on semistructured text explanations (semi-structured) @xcite , which combine properties of both highlight and free-text explanations. they consist of text in natural language and contain an explicit indication of the input elements that the free-text applies to. 3 if and how much a model can be improved based on such explanations depends on the amount of information contained in the explanation ( § 2.2), and to what extent this information can be integrated into the learning process ( § 2.1). user satisfaction is affected by the effort required to produce explanations and by the difficulty of the task, that might in turn affect explanation quality ( § 2.3). in the following, we discuss these factors in detail and where possible contrast them with respect to explanation type. objectives approaches for learning from explanations have been evaluated with different objectives in mind, and we introduce the different motivations below and link them with their respective evaluation in