ALICE : Active Learning with Contrastive Natural Language Explanations (2020)

Abstract: Training a supervised neural network classifier typically requires many annotated training samples. Collecting and annotating a large number of data points are costly and sometimes even infeasible. Traditional annotation process uses a low-bandwidth human-machine communication interface: classification labels, each of which only provides a few bits of information. We propose Active Learning with Contrastive Explanations (ALICE), an expert-in-the-loop training framework that utilizes contrastive natural language explanations to improve data efficiency in learning. AL-ICE learns to first use active learning to select the most informative pairs of label classes to elicit contrastive natural language explanations from experts. Then it extracts knowledge from these explanations using a semantic parser. Finally, it incorporates the extracted knowledge through dynamically changing the learning model’s structure. We applied ALICEin two visual recognition tasks, bird species classification and social relationship classification. We found by incorporating contrastive explanations, our models outperform baseline models that are trained with 40-100% more training data. We found that adding1expla-nation leads to similar performance gain as adding 13-30 labeled training data points.

## introduction the de-facto supervised neural network training paradigm requires a large dataset with annotations. it is time-consuming, difficult and sometimes even infeasible to collect a large number of data-points due to task nature. a typical example task is medical diagnosis. in addition, annotating datasets also is costly, especially in domains where experts are difficult to recruit. in a traditional an-1 co-supervised project. ## related work learning with natural language explanation psychologists and philosophers have long posited natural language explanations as central organizing elements to human learning and reasoning @xcite . several attempts have been made to incorporate natural language explanations into supervised classification tasks. andreas et al. (2018); mu et al. (2020 ) adopt a multi-task setting by learning classification and captioning simultaneously. @xcite ; @xcite encode natural language explanations as additional features to assist classification. orthogonal to their approaches, we focus on eliciting fewer but more informative explanations to reduce expert involvement with class-based active learning. another line of research collects heuristic rules as explanations (e.g., 'honey month' for predicting spouse relationship) to automatically label unlabeled data @xcite @xcite . different from their settings, we assume no additional training datapoints. in addition, we leverage natural language explanations by extracting knowledge and incorporate the knowledge into classifiers. distantly related to our work, @xcite propose to generate explanations for image classifiers but they do not explore improving the classifiers with the explanations. ## problem formulation contrastive natural language explanations existing research in social science and cognitive science @xcite suggests contrastive explanations are more effective in human learning than descriptive explanations. therefore, we choose contrastive natural language explanations to benefit our learners. an example contrastive explanation is like "why p rather than q?", in which p is the target event and q is a counterfactual contrast case that did not occur @xcite . in the example in figure 1 , if we ask the expert to differentiate between ring-billed gull against california gull, the expert would output the following natural language explanation: "ringbilled gull has a bill with a black ring near the tip while california gull has a red spot near tip of lower mandible". our explanations are classbased and are not specifically associated with any particular images. problem setup we are interested in a c class classification problem defined over an input space x and a label space @xmath0, ..., c}. initially, the training set d @xmath1, y i )} n train 1 is small, since our setting is restricted to be low resource. we also assume that there is a limited budget to ask domain experts to provide explanations during training. specifically, we consider k rounds of interactions with domain experts and each round has a query budget b. for each query, we need to specify two classes y p , y q for domain experts to compare. domain experts would return a contrastive natural language explanation e. each explanation e would guide us to focus on the most discriminating semantic segments to differentiate between y p and y q . in this paper, a semantic segment refers to a semantic segment of an object (e.g., "bill" in bird species classification) or a semantic object (e.g., "soccer" in social relationship classification). to make our framework more general, we start from a standard image classification neural architecture. we formulate our initial model as m (φ, g pool , f ) = f (g pool (φ(x))): here φ is an image encoder that maps each input image x to an activation map φ(x) ∈ r h×w ×d . g pool is a global pooling layer g pool (φ(x)) ∈ r d pool . f is a fully connected layer that performs flat c way classification. this formulation covers most of the off-the-shelf pre-trained image classifiers. ## conclusion we propose an expert-in-the-loop training framework alice to utilize contrastive natural language explanations to improve a learning algorithm's data efficiency. we extend the concept of active learning to class-based active learning for choosing the most informative query pair. we incorporate the extracted knowledge from expert natural language explanation by changing our algorithm's neural network structure. our experiments on two visual recognition tasks show that incorporating natural language explanations is far more data-efficient than adding extra training data. in the future, we plan to examine the hierarchical classification architecture's potential for reducing computational runtime.