# A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations

arXiv: https://arxiv.org/abs/2205.11467 · HTML: https://mlsanity.com/paper/2205.11467/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 9 of 9)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016)
- [Large-Scale QA-SRL Parsing](https://mlsanity.com/paper/1805.05377/) (2018)
- ["I'm Not Mad": Commonsense Implications of Negation and Contradiction](https://mlsanity.com/paper/2104.06511/) (2021)

## Built on by (most cited first, 5 of 5)

- [Paraphrasing in Affirmative Terms Improves Negation Understanding](https://mlsanity.com/paper/2406.07492/) (2024)
- [Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding](https://mlsanity.com/paper/2210.14486/) (2022)
- [Thunder-NUBench: A Benchmark for LLMs' Sentence-Level Negation Understanding](https://mlsanity.com/paper/2506.14397/) (2025)
- [Full Automation of Goal-driven LLM Dialog Threads with And-Or Recursors and Refiner Oracles](https://mlsanity.com/paper/2306.14077/) (2023)
- [Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding](https://mlsanity.com/paper/2601.04693/) (2026)

## Abstract (arXiv)

This paper explores a question-answer driven approach to reveal affirmative interpretations from verbal negations (i.e., when a negation cue grammatically modifies a verb). We create a new corpus consisting of 4,472 verbal negations and discover that 67.1% of them convey that an event actually occurred. Annotators generate and answer 7,277 questions for the 3,001 negations that convey an affirmative interpretation. We first cast the problem of revealing affirmative interpretations from negations as a natural language inference (NLI) classification task. Experimental results show that state-of-the-art transformers trained with existing NLI corpora are insufficient to reveal affirmative interpretations. We also observe, however, that fine-tuning brings small improvements. In addition to NLI classification, we also explore the more realistic task of generating affirmative interpretations directly from negations with the T5 transformer. We conclude that the generation task remains a challenge as T5 substantially underperforms humans.