Reasoning with Natural Language Explanations (2024)

Abstract: Explanation constitutes an archetypal feature of human rationality, underpinning learning and generalisation, and representing one of the media supporting scientific discovery and communication. Due to the importance of explanations in human reasoning, an increasing amount of research in Natural Language Inference (NLI) has started reconsidering the role that explanations play in learning and inference, attempting to build explanation-based NLI models that can effectively encode and use natural language explanations on downstream tasks. Research in explanation-based NLI, however, presents specific challenges and opportunities, as explanatory reasoning reflects aspects of both material and formal inference, making it a particularly rich setting to model and deliver complex reasoning. In this tutorial, we provide a comprehensive introduction to the field of explanation-based NLI, grounding this discussion on the epistemological-linguistic foundations of explanations, systematically describing the main architectural trends and evaluation methodologies that can be used to build systems capable of explanatory reasoning.

## introduction building systems that can understand and explain the world is a long-standing goal for artificial intelligence (ai) @xcite @xcite . the ability to explain, in fact, constitutes an archetypal feature of human rationality, underpinning communication, learning, and generalisation, as well as one of the mediums enabling scientific discovery and progress through the formulation of explanatory theories @xcite @xcite . due to the importance of explanation in human reasoning, an increasing amount of work has started reconsidering the role that explanation plays in learning and inference with natural language @xcite @xcite . in contrast to the existing end-to-end paradigm based on deep learning, explanation-based nli focuses on developing and evaluating models that can address downstream tasks through the explicit construction of a natural language explanation @xcite @xcite . in this context, explanation is seen as a potential solution to mitigate some of the well-known limitations in neural-based nli architectures @xcite , including the susceptibility to learning via shortcuts, the inability to generalise out-of-distribution, and the lack of interpretability @xcite @xcite @xcite . research in explanation-based nli, however, presents several fundamental challenges @xcite . first, the applied methodologies are still poorly informed by theories and accounts of explanations @xcite . this gap between theory and practice poses the risk of slowing down progress, missing the opportunity to formulate clearer hypotheses on the inferential properties of natural language explanations and define systematic evaluation methodologies @xcite @xcite . second, explanation-based nli models still lack robustness, control, and scalability for real-world applications. in particular, existing approaches suffer from several limitations when composing explanatory reasoning chains and performing abstraction for nli in complex domains @xcite . in this tutorial, we will provide a comprehensive introduction to explanatory reasoning in the context of nli, by systematically categorising and surveying explanation-supporting benchmarks, ar-chitectures, and research trends. specifically, we will present how the understanding of explanatory inference have evolved in recent years, together with the emerging methodological and modelling strategies. in parallel, we will attempt to provide an epistemological-linguistic characterisation of natural language explanations reviewing the main theoretical accounts @xcite to derive a fresh perspective for future work in the field. ## description this section outlines the content of the tutorial. ## schedule the tutorial will be organised according to the following timeline: thayaparan et al. (2020) a survey on explainability in machine reading comprehension. zhao et al. (2024) explainability for large language models: a survey. related tutorials zhu et al. (2024) explanation in the era of large language models. camburu and akata (2021) natural-xai: explainable ai with natural language explanation. zhao et al. (2023) complex reasoning in natural language. boyd-graber et al. (2022) human-centered evaluation of explanations. 7 instructor information marco valentino, idiap research institute. foot_1 marco is a postdoctoral researcher at the idiap research institute, switzerland. his research is carried out at the intersection of natural language inference and neuro-symbolic models focusing on building systems that can reason through natural language explanations in complex domains (e.g., mathematics, science, biomedical and clinical applications, ethical reasoning). he has published papers in major ai and nlp conferences including aaai, acl, emnlp, naacl and eacl. marco was involved in the organisation of workshops including mathnlp (emnlp 2022 and lrec-coling 2024), and textgraphs (coling 2022 and acl 2024). andré freitas, university of manchester & idiap research institute. foot_2 andré freitas leads the neuro-symbolic ai lab at the university of manchester and idiap research institute. his main research interests are on enabling the development of ai methods to support abstract, flexible and controlled reasoning in order to support ai-augmented scientific discovery. in particular, he investigates how the combination of neural and symbolic data representation paradigms can deliver better models of inference. he is an active contributor to the main conferences and journals in the ai/natural language processing (nlp) interface (aaai, neurips, acl, emnlp, eacl, coling, tacl, computational linguistics), with over 100 peer-reviewed publications. he contributed to the organisation of mathnlp at emnlp 2022 and lrec-coling 2024. andré participated in 7 tutorials, and coorganised 1 conference and 6 workshops.