Towards Controllable Natural Language Inference through Lexical
  Inference Types
Yingji Zhang Danilo S. Carvalho Ian Pratt-Hartmann Andre Freitas
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.