NILE : Natural Language Inference with Faithful Natural Language Explanations (2020)

Abstract: The recent growth in the popularity and success of deep learning models on NLP classification tasks has accompanied the need for generating some form of natural language explanation of the predicted labels. Such generated natural language (NL) explanations are expected to be faithful, i.e., they should correlate well with the model’s internal decision making. In this work, we focus on the task of natural language inference (NLI) and address the following question: can we build NLI systems which produce labels with high accuracy, while also generating faithful explanations of its decisions? We propose Natural-language Inference over Label-specific Explanations (NILE), a novel NLI method which utilizes auto-generated label-specific NL explanations to produce labels along with its faithful explanation. We demonstrate NILE’s effectiveness over previously reported methods through automated and human evaluation of the produced labels and explanations. Our evaluation of NILE also supports the claim that accurate systems capable of providing testable explanations of their decisions can be designed. We discuss the faithfulness of NILE’s explanations in terms of sensitivity of the decisions to the corresponding explanations. We argue that explicit evaluation of faithfulness, in addition to label and explanation accuracy, is an important step in evaluating model’s explanations. Further, we demonstrate that task-specific probes are necessary to establish such sensitivity.

## introduction deep learning methods have been employed to improve performance on several benchmark classification tasks in nlp @xcite . typically, these models aim at improving label accuracy, while it is often desirable to also produce explanations for these decisions @xcite . in this work, we focus on producing natural language explanations for natural language inference (nli), without sacrificing much on label accuracy. there has been growing interest in producing natural language explanations for deep learning systems @xcite @xcite , including nli @xcite . in general, the explanations from these methods can typically be categorized as post-hoc explanations @xcite . @xcite propose an nli system which first produces an explanation and then processes the explanation to produce the final label. we argue that these explanations also resemble post-hoc explanations (section 4.2). further, existing methods don't provide a natural way to test the faithfulness of the generated explanations, i.e., how well do the provided explanations correlate with the model's decision making. we therefore propose natural-language inference over label-specific explanations (nile) foot_0 , which we train and evaluate on english language examples. through nile, we aim to answer the following question: can we build nli systems which produce faithful natural language explanations of predicted labels, while maintaining high accuracy? briefly, in nile, we first generate natural language explanations for each possible decision, and subsequently process these explanations to produce the final decision. we argue that such a system provides a natural way of explaining its decisions. the key advantage is the testability of these explanations, in themselves, as well as in terms of the sensitivity of the system's prediction v8 s step i: generate label-specific candidate explanations step ii: process explanations to infer the task label 0.8 0.0 0.2 g entail g contradict g neutral a white dog with long hair jumps to catch a red and green toy. an animal is jumping to catch an object. ## related work explainability of a model's predictions has been studied from different perspectives, including feature importance based explanations @xcite @xcite , or post-hoc natural language explanations @xcite @xcite . @xcite produce counterfactual natural language explanations for image classification given an image and a counter-class label. @xcite propose a model for nli to first generate a free-form natural language explanation and then infer the label from the explanation. however, as noted by oana-maria et al. (2019a), the system tends to generate inconsistent explanations. we reason that requiring a model to generate an explanation of the correct output requires it to first infer the output, and the system thus resembles post-hoc explanation generation methods. given the diversity of desiderata and techniques for interpretability, the need for understanding interpretation methods and evaluating them has grown. difficulty in building interpretation models and the lack of robustness of the same are some of the major issues in existing deep neural networks systems @xcite @xcite . given these observations, measuring faithfulness, i.e., how well do the provided explanations correlate with the model's decision making, is crucial. deyoung et al. (2019) propose metrics to evaluate such faithfulness of rationales (supporting evidence) for nlp tasks. through nile, we propose a framework for generating faithful natural language explanations by requiring the model to condition on generated natural language explanations. the idea of using natural language strings as a latent space has been explored to capture compositional task structure @xcite . @xcite explore improving visual question answering by learning to generate question-relevant captions. @xcite aim to improve commonsense question answering by first generating commonsense explanations for multiple-choice questions, where the question and the choices are provided as the prompt. similar to @xcite , they learn by trying to generate human-provided explanations and subsequently conditioning on the generated explanation. in nile, we instead aim to produce an explanation for each possible label and subsequently condition on the generated label-specific explanations to produce the final decision. ## background in this section, we discuss the datasets (section 3.1) and pre-trained models (section 3.2) used to build nile. ## natural-language inference over label-specific explanations (nile) the overall architecture employed in nile is shown in figure 1 . we introduce the notation used in this paper in section 4.1. we then discuss the motivation for the major design choices in section 4.2. nile performs the following steps to produce labels and explanations: ## experiments in this section, we aim to answer the following questions: q1 how does nile compare with the baselines and other existing approaches in terms of final task performance, and explanation accuracy, on in-domain evaluation sets (train and test on snli)? (section 5.1) q2 how well does nile transfer to out-of-domain examples (train on snli, and test on mnli)? (section 5.2) q3 how faithful are the model's predictions to the generated explanations? (section 5.3) we provide training details in appendix a, and examples of generated label-specific explanations in appendix b. ## conclusion in this paper we propose nile, a system for natural language inference (nli) capable of generating labels along with natural language explanations for the predicted labels. through extensive experiments, we demonstrate the effectiveness of this approach, in terms of both label and explanation accuracy. nile supports the hypothesis that accurate systems can produce testable natural language explanations of their decisions. in the paper, we also argue the importance of explicit evaluation of faithfulness of the generated explanations, i.e., how correlated are the explanations to the model's decision making. we evaluate faithfulness of nile's explanations using sensitivity analysis. finally, we demonstrate that task-specific probes are necessary to measure such sensitivity. for roberta-base models, we report results through model selection on models trained using 5 seeds of random initialization -42, 219, 291, 67 and 741. model selection is done using label accuracies on snli dev set. in we ran our experiments on geforce gtx 1080 ti gpus. we adjust the batch size to be the largest multiple of 16 to fit on the gpu memory (∼12gb). we now list all the hyper-parameters used. gpt2: the hyper-parameters used for finetuning gpt2-medium include a maximum sequence length of 128, batch size of 2, learning rate of 5e-5, adam epsilon of 1e-8, max gradient norm of 1.0 and a seed of 42. for generating text, we used greedy decoding.