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arXiv AI By Younes Boufouss (LISN), Luc Pommeret (LISN, CNRS), Thomas Gerald (LISN), Patrick Paroubek (LISN, CNRS), Sophie Rosset (LISN, CNRS)

Can We Do Interpretable NLI with Graphs Based on Atomic Propositions?

Read the original on arXiv AI →

The paper investigates whether natural language inference (NLI) can be performed using only interpretable, graph-based representations of evidence. It introduces a pipeline that decomposes sentences into atomic propositions, maps them to ConceptNet triples, and feeds three graphs—premise, hypothesis, and a retrieved ConceptNet subgraph—into a fine‑tuned 0.8‑billion‑parameter language model. On the SNLI dataset the graph‑only model reaches 89.7% accuracy, close to a text‑based baseline, while on ANLI it matches RoBERTa‑large on rounds R2 and R3 but lags on R1, illustrating a trade‑off between interpretability and performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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