Web: http://arxiv.org/abs/2209.07662

Sept. 19, 2022, 1:15 a.m. | Nathaniel Weir, Benjamin Van Durme

cs.CL updates on arXiv.org arxiv.org

We present an approach for systematic reasoning that produces human
interpretable proof trees grounded in a factbase. Our solution resembles the
style of a classic Prolog-based inference engine, where we replace handcrafted
rules through a combination of neural language modeling, guided generation, and
semiparametric dense retrieval. This novel reasoning engine, NELLIE,
dynamically instantiates interpretable inference rules that capture and score
entailment (de)compositions over natural language statements. NELLIE provides
competitive performance on scientific QA datasets requiring structured
explanations over multiple facts.

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