Plausible Extractive Rationalization through Semi-Supervised Entailment Signal (2024)

Abstract: The increasing use of complex and opaque black box models requires the adoption of interpretable measures, one such option is extractive rationalizing models, which serve as a more interpretable alternative. These models, also known as Explain-Then-Predict models, employ an explainer model to extract rationales and subsequently condition the predictor with the extracted information. Their primary objective is to provide precise and faithful explanations, represented by the extracted rationales. In this paper, we take a semi-supervised approach to optimize for the plausibility of extracted rationales. We adopt a pre-trained natural language inference (NLI) model and further fine-tune it on a small set of supervised rationales (10%). The NLI predictor is leveraged as a source of supervisory signals to the explainer via entailment alignment. We show that, by enforcing the alignment agreement between the explanation and answer in a question-answering task, the performance can be improved without access to ground truth labels. We evaluate our approach on the ERASER dataset and show that our approach achieves comparable results with supervised extractive models and outperforms unsupervised approaches by > 100%.

## introduction large language models such as google's bert @xcite ) and openai's gpt series @xcite are gaining widespread adoption in natural language processing (nlp) tasks. these models achieved impressive performance in multiple nlp tasks ranging from solving text generation to information extraction @xcite . however, little is known regarding how answers are generated or which portion of the input text the model focuses on. these flaws highlight concerns surrounding trust and fear of undesirable biases in the model's reasoning chain. explainable ai (xai) is currently an active field of research aimed at addressing these issues @xcite @xcite . in this work, we focus on extractive rationalizing models @xcite , which are also known as explain-then-predict (etp) models, and are designed towards producing highlights serving as faithful explanations. faithfulness is defined as serving an explanation that represents the model's reasoning process for a given decision, while plausibility refers to the level of agreement with humans @xcite ). an advantageous characteristic of etp models is that they concurrently produce the explanation and the task label, eliminating the necessity for an added layer of interpretation. figure 1 : an example from the fever dataset, where the bold statement is the annotated rationale. given the document and claim, the label denotes that the document contains evidence supporting the claim. the nli predictor interprets this as a form of entailment between the claim and rationale. this differs from post-hoc techniques such as lime @xcite or shap (lundberg and lee, 2017), specifically tailored to interpret black-box models. although these techniques are model-agnostic by design, they are computationally expensive and do not guarantee faithfulness nor optimized for plausibility. chain-of-thought (cot) @xcite is another popular approach, aimed at prompting large language models (llm) such as openai's gpt4 to elucidate its own prediction, in the form of reasoning steps which is said to be a form of explanation. however, we note that though the reasoning steps are seemingly plausible and convincing, there is no guarantee of the reasoning being faithful towards the supported output, since there is no constraint on the conditional variables. etp models instead constrain the predictions on a compressed subset of the input, referred to as rationales, thereby guaranteeing the output to be solely conditioned on the subset, analogous to a binary form of feature relevance. in our work, we focus on improving the plausibility of rationales, measured via matching human annotations. several work has established benchmark datasets that consist of both the task label as well as human-annotated rationales @xcite . current works in extractive rationalization mostly implement a pipeline procedure of training an explainer and a predictor @xcite , trained either jointly or separately. the training approach for these models can be bifurcated into two primary methods: supervised or unsupervised rationale extraction. in our methodology, we strike a balance by leveraging a minimal subset of annotated rationales (≤ 10%) to refine an etp model. this refinement is applied to a separate nli predictor, functioning as an auxiliary instructor for the explainer in the event of limited annotated rationales. more importantly, the explainer has no access to the annotations, which are exclusively presented to the nli predictor. our approach is inspired by recent work in ensuring factual consistency in abstraction summarization @xcite , which has been found useful in cases of hallucination. firstly, we create an augmented dataset based on a label transformation algorithm, based on the annotated rationales and nli classes. this is used to provide further training on the nli predictor to generate sentencelevel rationale annotations. nli models are designed to determine whether a hypothesis contradicts, entails, or is neutral to a given premise. as such, they provide useful signals to align a given explanation to the answer produced by the predictor, as shown in subsequent experiments, this can have some desirable effects on the robustness of rationales @xcite . an example is illustrated in figure 1 on a fact verifica-tion task, where the purpose of the rationale is to act as evidence to either support or refute the given claim. in summary, the three key contributions of this work are the following: ## results all results are averaged over three runs with different seeds. for full-c, we do not report plausibility performance since there is no explainer module. in the eraser benchmark, the number of annotated rationale varies between tasks, where boolq features a higher quantity of annotated sentences and also includes larger number of contiguous spans. the main objective of this study is to assess between different unsupervised and supervised approaches in generating plausible and robust rationales, while minimizing negative effects on downstream task performance. ## conclusion in this paper, we have introduced a simple yet unique way of generating artificial learning signals from an alternative source, to cope with scenarios where human-annotated rationales are scarce. the method harnesses a transformer pre-trained on the nli task. through additional fine-tuning, the nli predictor can produce less biased labels, enhancing the learning process for the explainer. through the extensive experiments conducted, we have shown that our work can alleviate the plausibility and robustness of etp models in a lowresource environment. notably, with just 10% of the annotated rationale, our method delivers performance on par with fully supervised models and significantly outperforms both semi-supervised approaches that utilize more annotated data and unsupervised settings. in future directions, we plan to extend this work toward models that generate abstractive explanations, where the nli signal can act as verification feedback to ensure the mitigation of biased explanations. another interesting direction is to study how can we extend the nli predictor's coverage beyond a single sentence, to capture the correspondence between longer documents.