Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner (2022)

Abstract: Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive. Structured explanations, called entailment trees, were recently suggested as a way to explain the reasoning behind a QA system’s answer. In order to better generate such entailment trees, we propose an architecture called Iterative Retrieval-Generation Reasoner (IRGR). Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises. The IRGR model iteratively searches for suitable premises, constructing a single entailment step at a time. Contrary to previous approaches, our method combines generation steps and retrieval of premises, allowing the model to leverage intermediate conclusions, and mitigating the input size limit of baseline encoder-decoder models. We conduct experiments using the EntailmentBank dataset, where we outperform existing benchmarks on premise retrieval and entailment tree generation, with around 300% gain in overall correctness.

## introduction large neural network models have successfully been applied to different natural language tasks, achieving state-of-the-art results in many natural language benchmarks. despite this success, these results came with the expense of ai systems becoming less interpretable @xcite . with the desire to make the output of such models less opaque, we propose a question answering (qa) system that is able to explain their decisions not only by retrieving supporting textual evidence (rationales), but by showing how the answer to a question can be systematically proven from simpler textual premises (natural language reasoning). figure 1 : task has as input a hypothesis h (e.g. an answer to a question) and a corpus of premises c (simple textual evidences), the goal is to generate an entailment tree that explains the hypothesis h by using premises from c. these explanations are represented using entailment trees, as depicted in figure 1 . first introduced by @xcite , entailment trees represents a chain of reasoning that shows how a hypothesis (or an answer to a question) can be explained from simpler textual evidence. in comparison, other explanation approaches such as retrieval of passages (rationales) @xcite or multi-hop reasoning (chaining) @xcite are less expressive than entailment trees, which are comprised of multi-premise textual entailment steps. in order to generate such entailment trees, previous works @xcite @xcite have used encoder-decoder models that takes as input a small set of retrieved premises and output a linearized representation of the entailment tree. such models are limited by (1) the language model's fixed input size, and they may construct incorrect proofs when the retrieval module cannot fetch all relevant premises at once figure 2 : comparison among different natural language explanation approaches. the images show (a) plain textual explanations (b) retrieval of evidence passages (c) multi-hop explanations (d) entailment trees. the approach in (d) allows for more detailed inspection of the reasoning behind an explanation. nodes in gray are retrieved from a corpus, nodes in blue are generated, and the red node is the hypothesis or answer that is being explained. our contributions are two-fold. first, we design a retrieval method that is able to better identify premises needed to generate a chain of reasoning, which explains a given hypothesis. our retrieval method outperforms previous baselines by 48.3%, while allowing for a dynamic set of premises to be retrieved. secondly, we propose an iterative retrieval-generation architecture that constructs partial proofs and augments the retrieval probes using intermediate generation results. we show that integrating the retrieval module with iterative generation can significantly improve explanations. our proposed approach achieves new state-of-the-art result on entailment tree generation with over 306% better results on the all-correct metric (strict comparison with golden data), while using a model with one order of magnitude fewer parameters. ## related work traditionally, natural language processing (nlp) frameworks were based on white-box methods such as rule-based systems @xcite @xcite and decision trees @xcite , which were inherently inspectable @xcite . more recently, large deep learning language models (black-box methods) have gained popularity @xcite , but their improvements in result quality came with a cost: the system's outputs lack explainability and inspectability. there have been many attempts to mitigate this issue, including input perturbation @xcite and premises selection @xcite . one promising explanation approach is to combine the model's output with a humaninterpretable explanation. for instance, @xcite introduced the concept of natural language explanation in their e-snli dataset while @xcite expanded this idea to commonsense explanations. jhamtani and clark (2020) further explored the notion of explanation in multihop qa, where explanations contain a chain of reasoning, instead of simple textual explanations. different from these explanation approaches, our work generates explanations in the form of entailment trees, introduced by @xcite , which are composed of multi-premise textual entailment steps. entailment trees are more detailed explanations, making it easier to inspect the reasoning behind the model's answer. figure 2 shows a diagram compering different natural language explanation methods according to their structure and use of textual evidence. the first approach used to generate entailment trees was based on the entailmentwriter model by @xcite . however, their approach is limited by the input size of the encoder-decoder language models, where a fixed set of supporting facts is used to generate an explanation. instead, our model iteratively fetches a set of premises using dense retrieval conditioned on previous entailment steps, allowing for more precise explanations. our work is also related to some recent approaches that combine retrieval and neural networks for qa tasks @xcite . the work of lewis et al. (2020) combined dense retrieval with encoder-decoder models, where a different set of passages were retrieved corpus of premises (c) entailment steps (𝒮) top-k retrieval entailment generator hypothesis h entailment tree (t) "eruptions can cause plants to die" c 1 : "eruptions produce ash clouds" c 2 : "plants have green leaves" c 3 : "ash blocks sunlight" […] c 1 & c 3 => s 1 : "eruptions block sunlight" […] @xmath0: irgr is composed of two modules, irgr-retriever and irgr-generator . the irgr-retriever iteratively fetches a set of premises from a corpus c in order to generate an entailment tree (structured explanation for a given hypothesis). the irgr-generator computes a single entailment step at a time, and the intermediate generated steps are stored and used for subsequent retrieval and generation. for each generated character. conditioning the retrieval of a passage on previously retrieved passages has been explored in the context of multi-hop qa @xcite , and multi-hop explanations @xcite . however, these approaches either are not used to generate explanations or do not use inferred intermediate reasoning steps to retrieve premises. ## conclusion as deep learning models become more ubiquitous in the natural language field, it is desirable that users can understand the model's answer by inspecting the reasoning chain from simple premises to the answer hypothesis. to generate rich, systematic explanations, we proposed a method that can iteratively generate and retrieve premises to produce entailment trees. we show how our approach has advantages over previous baselines, where the retrieved premises and generated explanations are more accurate. in future work, we plan to improve the generation module by leveraging the structure of the entailment tree instead of relying purely on the encoder-decoder models. this idea could potentially fix the issues with "invalid entailment steps" and "repetition", which account for 73% of entailment step errors. we also plan to understand how explanations can be generated in the case of a false hypothesis, where we would expect the model to build a conclusion explaining why a statement is incorrect. it could help users verify false claims and understand the meaning behind their incorrectness.