Entailment

• Evans et al 2018 - Can Neural Networks Understand Logical Entailment?

RTE, also know as natural language inference (NLI).

• Bowman et al 2015 - A large annotated corpus for learning natural language inference SNLI dataset
• ESIM model: Chen et al 2016 - Enhanced LSTM for Natural Language Inference Famous model, the best pre-BERT NLI model
• Williams et al 2017 - A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference MNLI dataset
• Joshi et al 2020 - TaxiNLI: Taking a Ride up the NLU Hill
• Stacey et al 2022 - Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models

See also Natural Language Explanations

• Camburu et al 2018 - e-SNLI: Natural Language Inference with Natural Language Explanations
• Kumar & Talukdar 2020 - NILE: Natural Language Inference with Faithful Natural Language Explanations Doesn't beat the RoBERTa baseline
• Zhao et al 2020 - LIREx: Augmenting Language Inference with Relevant Explanation Improves upon NILE, beats RoBERTa baseline

• SNLI: Stanford Natural Language Inference (SNLI) Corpus paper: Bowman 2015
  • SNLI-hard, hard examples from SNLI (from Gururangan et al 2018 - Annotation Artifacts in Natural Language Inference Data)
• MNLI: Multi-Genre NLI Corpus paper: Williams 2017
• e-SNLI paper
• SICK: Sentences Involving Compositional Knowledge paper: Marelli 2014 Used in a lot a papers recently, see for example Stacey 2022

• In Question Answering
  • Chen et al 2021 - Can NLI Models Verify QA Systems’ Predictions?

Figure from Wu 2021 and MacCartney and Manning 2009.

• Natural Language Explanations
• Paraphrase
• Semantics