Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts (D19-1)
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| Challenge: | gradable adjectives of size are relative, i.e., determined by the context. |
| Approach: | They propose to model how the meaning of gradable adjectives of size can be learned from visually-grounded contexts by using four tasks to determine whether an object is ‘big’ or ‘small’. |
| Outcome: | The proposed model can learn subtending the meaning of size adjectives, but their performance decreases while moving from simple to more complex tasks. |
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Big Generalizations with Small Data: Exploring the Role of Training Samples in Learning Adjectives of Size (D19-64)
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| Challenge: | In previous work, models have been shown to fail in generalizing to unseen adjective-noun combinations. |
| Approach: | They propose a visual reasoning task dealing with quantities that challenges models to learn the meaning of size adjectives from visually-grounded contexts. |
| Outcome: | The proposed task is based on a visual reasoning task dealing with quantities and shows that seeing some of the cases during training helps a model understand the rule subtending the task. |
Grounding Gradable Adjectives through Crowdsourcing (L18-1)
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| Challenge: | Often, texts describe interactions using vague, high-level language . crowdsourcing is expensive and requires extensive literature review and time . |
| Approach: | They propose a method for estimating concrete groundings for a set of gradable adjectives by crowdsourcing human intuitions and fitting a mixed effects model to the text. |
| Outcome: | The proposed model can generalize to unseen data and has a predictive R 2 of 0.632 in general and 0.677 on a subset of high-frequency adjectives. |
‘Lighter’ Can Still Be Dark: Modeling Comparative Color Descriptions (P18-2)
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| Challenge: | Multimodal approaches to object recognition ground adjectives and nouns from text using comparative adjectives. |
| Approach: | They propose a new paradigm of grounding comparative adjectives within the realm of color descriptions by using a vector model. |
| Outcome: | The proposed model generates representations of comparative adjectives with an average accuracy of 0.65 cosine similarity to the desired direction of change. |
Metaphor and Large Language Models: When Surface Features Matter More than Deep Understanding (2025.findings-acl)
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| Challenge: | Existing studies on metaphor processing have focused on single datasets and specific task settings, often using artificially constructed data through lexical replacement. |
| Approach: | They propose to evaluate the capabilities of Large Language Models (LLMs) in metaphor interpretation across multiple datasets, tasks, and prompt configurations. |
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Are Multimodal Large Language Models Pragmatically Competent Listeners in Simple Reference Resolution Tasks? (2025.findings-acl)
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| Challenge: | Existing models are unable to resolve references to abstract visual stimuli, such as color patches and color grids, but their pragmatic capabilities are still a challenge for state-of-the-art MLLMs. |
| Approach: | They investigate whether multimodal large language models are able to resolve references to abstract visual stimuli, such as color patches and color grids, in a well-known reference resolution paradigm. |
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Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) (2025.acl-srw)
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| Challenge: | ACL 2025 Student Research Workshop is a forum for students conducting research in Computational Linguistics, Natural Language Processing, and Machine Learning. |
| Approach: | the ACL 2025 Student Research Workshop will be held in conjunction with ACL 2020 . the virtual conference will be a forum for students conducting research in Computational Linguistics, Natural Language Processing, and Machine Learning. |
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Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts) (2025.acl-tutorials)
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| Challenge: | ACL 2025 tutorial sessions are a cornerstone event of the conference . 76 tutorial submissions were received this year, many of which were very engaging . |
| Approach: | 76 tutorial submissions were received this year for the tutorial session at ACL 2025 . the tutorials are designed to equip you with the latest insights, tools, and methodologies . |
| Outcome: | the tutorial sessions at ACL 2025 will be held in london on november 8 . the conference received 76 tutorial submissions this year . |
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) (2024.acl-srw)
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| Challenge: | ACL Student Research Workshop (SRW) 2024 will be held in conjunction with ACL 2024 . Authors can submit both research papers and thesis proposals as non-archival . |
| Approach: | ACL Student Research Workshop (SRW) 2024 will be held in conjunction with ACL 2024 . authors can submit both research papers and thesis proposals as non-archival . |
| Outcome: | The ACL Student Research Workshop (SRW) 2024 will be held in conjunction with ACL 2024. |
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts) (2024.acl-tutorials)
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| Challenge: | ACL tutorial session is a highlight of the conference . it aims to provide attendees with a thorough introduction to key topics in our fast-evolving research field . |
| Approach: | the ACL tutorial session is a highlight of the conference . it aims to provide attendees with a thorough introduction to key topics in the field . |
| Outcome: | the ACL tutorial session is a highlight of the conference . the review process involved multiple conferences and three reviewers . |
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) (2023.acl-srw)
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| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |