Explainable natural language inference aims to provide a mechanism to produce
explanatory (abductive) inference chains which ground claims to their
supporting premises. A recent corpus called EntailmentBank strives to advance
this task by explaining the answer to a question using an entailment tree
\cite{dalvi2021explaining}. They employ the T5 model to directly generate the
tree, which can explain how the answer is inferred. However, it lacks the
ability to explain and control the generation of intermediate steps, which is
crucial for the multi-hop inference process. % One recent corpus,
EntailmentBank, aims to push this task forward by explaining an answer to a
question according to an entailment tree \cite{dalvi2021explaining}. They
employ T5 to generate the tree directly, which can explain how the answer is
inferred but cannot explain how the intermediate is generated, which is
essential to the multi-hop inference process. In this work, we focus on
proposing a controlled natural language inference architecture for
multi-premise explanatory inference. To improve control and enable explanatory
analysis over the generation, we define lexical inference types based on
Abstract Meaning Representation (AMR) graph and modify the architecture of T5
to learn a latent sentence representation (T5 bottleneck) conditioned on said
type information. We also deliver a dataset of approximately 5000 annotated
explanatory inference steps, with well-grounded lexical-symbolic operations.
Experimental results indicate that the inference typing induced at the T5
bottleneck can help T5 to generate a conclusion under explicit control.

提出了一种用于多前提解释推理的控制型自然语言推理体系结构，通过基于抽象含义表示图的词法推理类型来改进生成过程中的控制和解释分析，实验结果表明，在 T5 瓶颈处引入的推理类型有助于 T5 在明确控制下生成结论。

通过词汇推理类型实现可控自然语言推理

Towards Controllable Natural Language Inference through Lexical  Inference Types

Although large language models demonstrate remarkable question-answering
performances, revealing the intermediate reasoning steps that the models
faithfully follow remains challenging. In this paper, we propose FAME (FAithful
question answering with MontE-carlo planning) to answer questions based on
faithful reasoning steps. The reasoning steps are organized as a structured
entailment tree, which shows how premises are used to produce intermediate
conclusions that can prove the correctness of the answer. We formulate the task
as a discrete decision-making problem and solve it through the interaction of a
reasoning environment and a controller. The environment is modular and contains
several basic task-oriented modules, while the controller proposes actions to
assemble the modules. Since the search space could be large, we introduce a
Monte-Carlo planning algorithm to do a look-ahead search and select actions
that will eventually lead to high-quality steps. FAME achieves state-of-the-art
performance on the standard benchmark. It can produce valid and faithful
reasoning steps compared with large language models with a much smaller model
size.

本论文提出 FAithful question answering with MontE-carlo planning （FAME）方法，以结构化蕴含树方式展示中间推理步骤，包含多个基础任务模块并通过蒙特卡罗规划算法作为控制器提出行动，旨在回答问题并与大型语言模型相比，使用更小的模型大小实现了最先进的性能。

蒙特卡罗规划的忠实问答

Faithful Question Answering with Monte-Carlo Planning

Interpreting the reasoning process from questions to answers poses a
challenge in approaching explainable QA. A recently proposed structured
reasoning format, entailment tree, manages to offer explicit logical deductions
with entailment steps in a tree structure. To generate entailment trees, prior
single pass sequence-to-sequence models lack visible internal decision
probability, while stepwise approaches are supervised with extracted single
step data and cannot model the tree as a whole. In this work, we propose RLET,
a Reinforcement Learning based Entailment Tree generation framework, which is
trained utilising the cumulative signals across the whole tree. RLET
iteratively performs single step reasoning with sentence selection and
deduction generation modules, from which the training signal is accumulated
across the tree with elaborately designed aligned reward function that is
consistent with the evaluation. To the best of our knowledge, we are the first
to introduce RL into the entailment tree generation task. Experiments on three
settings of the EntailmentBank dataset demonstrate the strength of using RL
framework.

本文提出了一种基于强化学习的蕴含树生成框架 RLET，通过句子选择和推理生成模块进行单步推理，并积累整个树的训练信号，实现了针对解释性 QA 中的逻辑推理过程，并且在三个数据集上的实验表明了使用强化学习框架的优点。