Challenge: Linguistic studies have revealed important aspects of grammaticization of deverbal prepositions.
Approach: They propose a computational approach to measure the degree of grammaticization of deverbal prepositions based on corpus data.
Outcome: The proposed method correlates well with human judgements and supports previous findings in linguistics.

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Paraphrasing Compound Nominalizations (2021.emnlp-main)

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Challenge: Nominalizations are difficult to interpret because of ambiguous semantic relations between deverbal noun and its arguments.
Approach: They propose to generate clausal paraphrases for nominalizations by mapping arguments to verbs . they use a contextualized language model to re-rank nominalization candidates .
Outcome: The proposed task is based on a pre-trained model to re-rank paraphrase candidates identified by a textual entailment model.
Prepositions Matter in Quantifier Scope Disambiguation (2022.coling-1)

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Challenge: Existing work on how to integrate world knowledge into a QSD model has been limited .
Approach: They use a scope-disambiguated corpus annotated with prepositional senses to integrate our knowledge into a machine learning model.
Outcome: The proposed model is based on a scope-disambiguated corpus annotated with prepositional senses . Statistical analysis shows that prepositions have a positive impact on the learnability of automatic QSD systems.
Unsupervised Mapping of Arguments of Deverbal Nouns to Their Corresponding Verbal Labels (2023.findings-acl)

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Challenge: Deverbal nouns are nominal forms of verbs used in English texts to describe events or actions . many NLP systems neglect to handle nominalized constructions, resulting in limited applications .
Approach: They propose to map arguments of deverbal nouns to universal-dependency relations of verbal constructions . they propose to use the same labels as verbal cases to map the arguments .
Outcome: The proposed approach maps arguments of nominalized nouns to the corresponding verbal constructions.
Automatic Nominalization of Clauses through Textual Entailment (2022.coling-1)

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Challenge: Past research on clause nominalization has focused on replacement of the head verb with a deverbal noun and resource development to support the task.
Approach: They propose to use a textual entailment model to optimize the position and POS of nominal arguments by fine-tuning a model on the task.
Outcome: The proposed model outperforms unsupervised approaches on the nominalization task and outperformed a state-of-the-art neural language model.
Preposition Sense Disambiguation and Representation (D18-1)

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Challenge: Prepositions are highly polysemous and their variegated senses encode significant semantic information.
Approach: They match each preposition’s context and their interplay to the geometry of the word vectors to the left and right of the preposition.
Outcome: The proposed algorithm is comparable to and better than state-of-the-art on two benchmark datasets.
Embedding Syntax and Semantics of Prepositions via Tensor Decomposition (N18-1)

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Challenge: Existing methods on preposition representation treat prepositions no different from content words (e.g., word2vec and GloVe).
Approach: They propose to use word-triple counts to capture a preposition’s interaction with its attachment and complement and derive preposition embeddings via tensor decomposition on a large unlabeled corpus.
Outcome: The proposed model is comparable to or better than the state-of-the-art on multiple standardized datasets.
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 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 64th Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts) (2026.acl-tutorials)

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Challenge: 61 submissions were received for the tutorials at ACL 2026 .
Approach: 61 tutorials were submitted for the joint call for proposals with EACL . the call for submissions was a highly competitive selection process .
Outcome: 61 submissions were received for the tutorial session at the conference this year . the tutorials cover a range of topics that have moved to the forefront of NLP research in 2026 .
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts) (2023.acl-tutorials)

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Challenge: ACL 2023 tutorials are organized to give conference attendees a comprehensive introduction by expert researchers to some topics of importance drawn from our rapidly growing and changing research field.
Approach: ACL 2023 tutorials session is organized to give conference attendees a comprehensive introduction by experts in the field. 42 tutorial submissions were received, of which 6 were selected for presentation at ACL.
Outcome: ACL 2023 tutorials are organized to give conference attendees a comprehensive introduction . the review committee includes the EACL tutorial chairs and the ACL tutorial chair .

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