Challenge: Negation poses a challenge in many natural language understanding tasks . leveraging sentences with negation and affirmative interpretations is beneficial for many tasks involving humans .
Approach: They propose to collect negated sentences and their affirmative interpretations and leverage them to build a plug-and-play neural generator that generates an affirmative interpreter.
Outcome: The proposed method does not require manual effort and does not impact other tasks.

Similar Papers

Paraphrasing in Affirmative Terms Improves Negation Understanding (2024.acl-short)

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Challenge: Negation is a common linguistic phenomenon in human languages . however, language models face challenges with negation in many tasks .
Approach: They propose to incorporate affirmative interpretations into models to make them more robust against negation.
Outcome: The proposed models are more robust against negation when negation is present in input . the proposed models can be used to analyze large corpus and natural language understanding tasks .
A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations (2022.findings-naacl)

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Challenge: Negations carry affirmative meanings, which are difficult to process and understand by humans.
Approach: They propose a question-answer driven approach to reveal affirmative interpretations from verbal negations.
Outcome: The proposed approach is based on a natural language inference task . it shows that state-of-the-art transformers are insufficient to reveal affirmative interpretations .
Understanding by Understanding Not: Modeling Negation in Language Models (2021.naacl-main)

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Challenge: Negation is a core construction in natural language, but state-of-the-art pre-trained language models often handle it incorrectly.
Approach: They propose to augment language modeling objective with unlikelihood objective based on negated generic sentences from a raw text corpus.
Outcome: The proposed approach reduces the top 1 error rate to 4% on negated LAMA dataset and improves on negating NLI benchmarks.
Developmental Negation Processing in Transformer Language Models (2022.acl-short)

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Challenge: Negation is an important construct in language for reasoning over the truth of propositions, garnering interest from philosophy (Horn, 1989) and psycholinguistics (Zwaan, 2012).
Approach: They propose to frame a natural language inference task as a problem and examine how well transformers can process negation categories.
Outcome: The proposed models perform better on certain categories, suggesting clear differences in how they are processed.
CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation (2022.emnlp-main)

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Challenge: Negation is fundamental to human communication.
Approach: They propose a dataset which requires reasoning about implications of negated statements in paragraphs . they collect paragraphs with diverse negation cues and crowdworkers ask questions about implications .
Outcome: The first dataset in english requires reasoning about implications of negated statements in paragraphs . it features 14,182 question-answer pairs with over 200 unique negation cues based on crowd-workers . the best performing model achieves only 42% on consistency metric, well below human performance of 81%.
Making Language Models Robust Against Negation (2025.naacl-long)

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Challenge: Negation is a semantic phenomenon that alters an expression to convey the opposite meaning.
Approach: They propose a self-supervised method to make language models more robust against negation by pre-training models.
Outcome: The proposed task outperforms the off-the-shelf versions on nine negation-related benchmarks.
Improving negation detection with negation-focused pre-training (2022.naacl-main)

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Challenge: Negation is a common linguistic feature that is crucial in many language understanding tasks.
Approach: They propose a new approach to detect negation in language models using data augmentation and negation masking.
Outcome: The proposed approach improves negation detection performance and generalizability over the strong baseline NegBERT.
An Analysis of Natural Language Inference Benchmarks through the Lens of Negation (2020.emnlp-main)

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Challenge: Existing benchmarks for natural language inference ignore negations and can make inferences that are difficult to make.
Approach: They propose a new benchmark for natural language inference in which negation plays a critical role.
Outcome: The proposed benchmarks show that negation plays a critical role in inference judgments.
To Learn or Not to Learn: Replaced Token Detection for Learning the Meaning of Negation (2024.lrec-main)

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Challenge: State-of-the-art language models perform well on a variety of language tasks, but struggle with understanding negation cues in tasks like natural language inference (NLI).
Approach: They propose a new learning strategy for negation building on ELECTRA’s replaced token detection objective.
Outcome: The proposed approach leads to substantial gains on a variant of RTE with additional negation.
This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models (2023.emnlp-main)

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Challenge: Large language models (LLMs) have grammatical knowledge but fail to interpret negation . a recent study shows that LLMs struggle with negative sentences .
Approach: They propose to use a dataset to grasp LLMs' generalization and inference capability . they also fine-tuned models to assess whether the understanding of negation can be trained .
Outcome: The proposed model is able to generalize and infer negation in 400,000 sentences . but it is suboptimal when it comes to negation, a key step in natural language processing .

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