| Challenge: | Existing lexical resources do not include the relative intensities of adjectives. |
| Approach: | They propose a method to automatically learn relative intensity relation between scalar adjectives . they use a paraphrase-based method that assumes that a pair of adjectives is "really hot" a similar method is used to infer the polarity of indirect answers to "yes/no" questions . |
| Outcome: | The proposed method improves the quality of systems for ordering sets of scalar adjectives and inferring the polarity of indirect answers to "yes/no" questions. |
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| Challenge: | Existing studies on scalar adjective ranking have focused on English due to the availability of datasets for evaluation. |
| Approach: | They propose a binary classification task to examine the models’ ability to distinguish scalar from relational adjectives in English. |
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BERT Knows Punta Cana is not just beautiful, it’s gorgeous: Ranking Scalar Adjectives with Contextualised Representations (2020.emnlp-main)
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| Challenge: | Adjectives describe positive properties of nouns but with different intensity. |
| Approach: | They propose a BERT-based approach to intensity detection for scalar adjectives by generating vectors directly from contextualised representations. |
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Probing Large Language Models for Scalar Adjective Lexical Semantics and Scalar Diversity Pragmatics (2024.lrec-main)
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| Challenge: | Scalar adjectives describe different domain scales and vary in intensity . they can be triggered by scalar adjective and require listeners to reason pragmatically about them. |
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ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation (2023.acl-long)
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| Challenge: | Paraphrase generation is a long-standing task in natural language processing (NLP). |
| Approach: | They propose to generate large-scale syntactically diverse paraphrase datasets by abstract meaning representation back-translation. |
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Improving Paraphrase Detection with the Adversarial Paraphrasing Task (2021.acl-long)
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| Challenge: | a new adversarial method of paraphrase identification is being used to identify paraphrases based on word overlap and syntax . authors propose a dataset that generates semantically equivalent but lexically and syntactically disparate paraphrase pairs . |
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Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory (2020.findings-emnlp)
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| Challenge: | Having high quality annotated data is crucial for training supervised machine learning models. |
| Approach: | They propose automated methods to improve NLP datasets by viewing them as graphs with expected semantic properties. |
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Guilt by Association: Emotion Intensities in Lexical Representations (2021.emnlp-main)
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| Challenge: | linguistic models have a higher correlation with human ground truth ratings than labeled data . word vectors have often been evaluated on standard word relatedness benchmarks . |
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Paraphrastic Representations at Scale (2022.emnlp-demos)
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| Challenge: | a new system allows users to train their own state-of-the-art paraphrastic sentence representations in a variety of languages. |
| Approach: | They propose a system that allows users to train their own paraphrastic sentence representations in a variety of languages. |
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Paraphrase Makes Perfect: Leveraging Expression Paraphrase to Improve Implicit Sentiment Learning (2025.coling-main)
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| Challenge: | Existing implicit sentiment learning methods focus on capturing implicit sentiment knowledge individually, without considering the potential connection between implicit and explicit sentiment. |
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PAM: Paraphrase AMR-Centric Evaluation Metric (2025.findings-acl)
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| Challenge: | Current evaluation metrics for paraphrase generation are based on borrowed metrics from text-to-text tasks . this is not ideal for paraphrasing as we typically want variation in the lexicon while persisting semantics. |
| Approach: | They propose a Paraphrase AMR-Centric Evaluation Metric that uses AMR graphs extracted from the input text to evaluate paraphrases. |
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