Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters

Understand

Natural Language Inference (NLI) has been extensively studied by the NLP community as a framework for estimating the semantic relation between sentence pairs.

• While early work identified certain biases in NLI models, recent advancements in modeling and datasets demonstrated promising performance.
• In this work, we further explore the direct zero-shot applicability of NLI models to real applications, beyond the sentence-pair setting they were trained on.
• First, we analyze the robustness of these models to longer and out-of-domain inputs.