multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning

By Swarnadeep Saha, Prateek Yadav, Mohit Bansal. 2021

29 citations recorded by the source corpus.

We focus on a type of linguistic formal reasoning where the goal is to reason over explicit knowledge in the form of natural language facts and rules (Clark et al., 2020). A recent work, named PRover (Saha et al., 2020), performs such reasoning by answering a question and also generating a proof graph that explains the answer. However, compositional reasoning is not always unique and there may be multiple ways of reaching the correct answer. Thus, in our work, we address a new and challenging problem of generating multiple proof graphs for reasoning over natural language rule-bases. Each proof provides a different rationale for the answer, thereby improving the interpretability of such reasoning systems. In order to jointly learn from all proof graphs and exploit the correlations between multiple proofs for a question, we pose this task as a set generation problem over structured output spaces where each proof is represented as a directed graph. We propose two variants of a proof-set generation model, multiPRover. Our first model, Multilabel-multiPRover, generates a set of proofs via multi-label classification and implicit conditioning between the proofs; while the second model, Iterative-multiPRover, generates proofs iteratively by explicitly conditioning on the previously generated proofs. Experiments on multiple synthetic, zero-shot, and human-paraphrased datasets reveal that both multiPRover models significantly outperform PRover on datasets containing multiple gold proofs. Iterative-multiPRover obtains state-of-the-art proof F1 in zero-shot scenarios where all examples have single correct proofs. It also generalizes better to questions requiring higher depths of reasoning where multiple proofs are more frequent.

Subfields: Natural Language Processing

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Citations in the corpus

ProoFVer: Natural Logic Theorem Proving for Fact Verification (2021, methodology): “Nevertheless, in the future we plan to experiment with alternative decoding approaches, including some of the recent developments in nonautoregressive conditional language models (Xu and Carpuat, 2021) and transformer-based proof generators (Saha et al., 2021).”
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning (2021, background): “…consisting of explanations graphs (Jansen et al., 2018; Jansen and Ustalov, 2019; Xie et al., 2020; Saha et al., 2020; Kalyanpur et al., 2020; Saha et al., 2021), chain of facts (Khot et al., 2020; Jhamtani and Clark, 2020; Inoue et al., 2020; Geva et al., 2021) or semi-structured text (Ye…”