Negation Scope Conversion: Towards a Unified Negation-Annotated Dataset (2024.lrec-main)
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| Challenge: | Negation scope resolution models that use pre-trained language models perform worse when fine-tuned on a combined dataset. |
| Approach: | They propose to automatically convert the negation scopes of BioScope and SFU to those of Sherlock and merge them into a unified dataset. |
| Outcome: | The proposed method improves on the unified dataset compared to the simply combined dataset. |
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| Challenge: | Negation is an important characteristic of language, and a major component of information extraction from text. |
| Approach: | They propose to use a popular transfer learning model to solve Negation Detection and Scope Resolution tasks in 3 datasets that have gained popularity over the years. |
| Outcome: | The proposed model outperforms existing systems on the BioScope Corpus, the Sherlock dataset and the SFU Review Corpus in scope resolution. |
Resolving Legalese: A Multilingual Exploration of Negation Scope Resolution in Legal Documents (2024.lrec-main)
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| Challenge: | Negation scope resolution is a challenging task for NLP because of the complexity of legal texts and lack of annotated in-domain negation corpora. |
| Approach: | They propose to use annotated court decisions to improve negation scope resolution . they release annotations in german, french, and italian to train models without legal data . |
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NegPar: A parallel corpus annotated for negation (L18-1)
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| Challenge: | NegPar is the first parallel corpus annotated for negation in the narrative domain. |
| Approach: | They present NegPar, a parallel corpus annotated for negation in the narrative domain . they follow the annotation guidelines in the CONANDOYLE-NEG corpus . |
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Detecting Negation Cues and Scopes in Spanish (2020.lrec-1)
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Salud María Jiménez-Zafra, Roser Morante, Eduardo Blanco, María Teresa Martín Valdivia, L. Alfonso Ureña López
| Challenge: | Negation is a phenomenon that "relates an expression e to another expression with a meaning that is in some way opposed to the meaning of e" previous work on negation in English has focused mostly and only recently on annotation tasks. |
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Learning with Structured Representations for Negation Scope Extraction (P18-2)
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| Challenge: | Existing approaches to negation scope detection have been criticized for capturing information related to negations, long-distance dependencies and structural information. |
| Approach: | They propose to use conditional random fields, semi-Markov CRF and latent-variable CRF models to capture useful information such as long-distance dependencies and some latent structural information. |
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Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text (D19-62)
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| Challenge: | Existing evidence for improved performance on natural language tasks is unclear to what degree the learned token representations capture and encode highlevel morphological/syntactic knowledge about the usage of a given token in a sentence. |
| Approach: | They propose to use context-aware token representations to capture morphological/syntactic knowledge about the usage of a given word/token in a sentence. |
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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%. |
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. |
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 . |
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Context Matters: A Pragmatic Study of PLMs’ Negation Understanding (2022.acl-long)
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| Challenge: | In linguistics, there are two main perspectives on negation: a semantic and a pragmatic view. |
| Approach: | They propose to use transformer-based pre-trained language models to study negation understanding using a pragmatic paradigm. |
| Outcome: | The proposed transformer-based model outperforms the human benchmark at NLU and GLUE, and the results are much more optimistic than previous studies. |