mlsanityData to 7 Oct 2026

Scalar Adjective Identification and Multilingual Ranking

arXiv 2105.01180 ↗First posted 3 May 2021cs.CLcited by 6

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Abstract (arXiv)

The intensity relationship that holds between scalar adjectives (e.g., nice < great < wonderful) is highly relevant for natural language inference and common-sense reasoning. Previous research on scalar adjective ranking has focused on English, mainly due to the availability of datasets for evaluation. We introduce a new multilingual dataset in order to promote research on scalar adjectives in new languages. We perform a series of experiments and set performance baselines on this dataset, using monolingual and multilingual contextual language models. Additionally, we introduce a new binary classification task for English scalar adjective identification which examines the models' ability to distinguish scalar from relational adjectives. We probe contextualised representations and report baseline results for future comparison on this task.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 6 of its 22 references that are in the map. First posted on arXiv 3 May 2021.

Builds on

6 of 22 references.

References · 6

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  3. 2018Learning Word Vectors for 157 Languagescited by 279
  4. 2019FlauBERT: Unsupervised Language Model Pre-training for Frenchcited by 153
  5. 2020GREEK-BERT: The Greeks visiting Sesame Streetcited by 37
  6. 2020BERT Knows Punta Cana is not just beautiful, it's gorgeous: Ranking Scalar Adjectives with Contextualised Representationscited by 15

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 0; 2026: 1.

6 papers.

  1. 2024Fantastic Semantics and Where to Find Them: Investigating Which Layers of Generative LLMs Reflect Lexical Semanticscited by 43
  2. 2023Expectations over Unspoken Alternatives Predict Pragmatic Inferencescited by 9
  3. 2023Representation Of Lexical Stylistic Features In Language Models' Embedding Spacecited by 8
  4. 2024Adjusting Interpretable Dimensions in Embedding Space with Human Judgmentscited by 4
  5. 2023Exploring Data Augmentation Methods on Social Media Corporacited by 0
  6. 2026LLMs Infer Cultural Context but Fail to Apply It When Respondingcited by 0