% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@inproceedings{veronis1990word,
  title={Word sense disambiguation with very large neural networks extracted from machine readable dictionaries},
  author={Veronis, Jean and Ide, Nancy},
  booktitle={COLING 1990 Volume 2: Papers presented to the 13th International Conference on Computational Linguistics},
  year={1990}
}
@inproceedings{rgcn_citation,
  title={Modeling relational data with graph convolutional networks},
  author={Schlichtkrull, Michael and Kipf, Thomas N and Bloem, Peter and Van Den Berg, Rianne and Titov, Ivan and Welling, Max},
  booktitle={European semantic web conference},
  pages={593--607},
  year={2018},
  organization={Springer}
}

@inproceedings{stringli2025pitfalls,
  title={Pitfalls of Scale: Investigating the Inverse Task of Redefinition in Large Language Models},
  author={Stringli, Elena and Lymperaiou, Maria and Filandrianos, Giorgos and Voulodimos, Athanasios and Stamou, Giorgos},
  booktitle={Findings of the Association for Computational Linguistics: ACL 2025},
  pages={9445--9469},
  year={2025}
}

@article{sun2025large,
  title={Large language models are overconfident and amplify human bias},
  author={Sun, Fengfei and Li, Ningke and Wang, Kailong and Goette, Lorenz},
  journal={arXiv preprint arXiv:2505.02151},
  year={2025}
}

@article{sharma2023towards,
  title={Towards understanding sycophancy in language models},
  author={Sharma, Mrinank and Tong, Meg and Korbak, Tomasz and Duvenaud, David and Askell, Amanda and Bowman, Samuel R and Cheng, Newton and Durmus, Esin and Hatfield-Dodds, Zac and Johnston, Scott R and others},
  journal={arXiv preprint arXiv:2310.13548},
  year={2023}
}

@inproceedings{general-domain,
  title={Towards General-Domain Word Sense Disambiguation: Distilling Large Language Model into Compact Disambiguator},
  author={Ming, Liqiang and Zhong, Sheng-hua and Li, Luncong},
  booktitle={Conference on Empirical Methods in Natural Language Processing},
  pages={884--897},
  year={2025},
  organization={Association for Computational Linguistics}
}

@inproceedings{assist-ambiguity,
  title={Can LLMs assist with Ambiguity? A Quantitative Evaluation of various Large Language Models on Word Sense Disambiguation},
  author={Sumanathilaka, T.G.D.K and Micallef, Nicholas and Hough, Julian},
  booktitle={1st International Conference on NLP & AI for Cyber Security},
  pages={97--108},
  year={2024},
  organization={Association for Computational Linguistics}
}

@inproceedings{teruel-etal,
    title = "Increasing Argument Annotation Reproducibility by Using Inter-annotator Agreement to Improve Guidelines",
    author = "Teruel, Milagro  and
      Cardellino, Cristian  and
      Cardellino, Fernando  and
      Alonso Alemany, Laura  and
      Villata, Serena",
    editor = "Calzolari, Nicoletta and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Hasida, Koiti and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios and Tokunaga, Takenobu",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = May,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L18-1640/"
}

@article{gcn_citation,
  title={Semi-supervised classification with graph convolutional networks},
  author={Kipf, TN},
  journal={arXiv preprint arXiv:1609.02907},
  year={2016}
}

@inproceedings{banarescu2013abstract,
  title={Abstract meaning representation for sembanking},
  author={Banarescu, Laura and Bonial, Claire and Cai, Shu and Georgescu, Madalina and Griffitt, Kira and Hermjakob, Ulf and Knight, Kevin and Koehn, Philipp and Palmer, Martha and Schneider, Nathan},
  booktitle={Proceedings of the 7th linguistic annotation workshop and interoperability with discourse},
  pages={178--186},
  year={2013}
}

@article{li2018constructing,
  title={Constructing narrative event evolutionary graph for script event prediction},
  author={Li, Zhongyang and Ding, Xiao and Liu, Ting},
  journal={arXiv preprint arXiv:1805.05081},
  year={2018}
}

@article{yamamoto2022dependency,
  title={Dependency patterns of complex sentences and semantic disambiguation for abstract meaning representation parsing},
  author={Yamamoto, Yuki and Matsumoto, Yuji and Watanabe, Taro},
  journal={Journal of Natural Language Processing},
  volume={29},
  number={2},
  pages={515--541},
  year={2022},
  publisher={The Association for Natural Language Processing}
}

@article{sert2023enhancing,
  title={Enhancing the performance of WSD task using regularized GNNs with semantic diffusion},
  author={Sert, Bіlge {\c{S}}іpal and Elma, Eren and berna Altinel, Ay{\c{s}}e},
  journal={IEEE Access},
  volume={11},
  pages={40565--40578},
  year={2023},
  publisher={IEEE}
}

@inproceedings{lee-etal2020-weakly,
    title = "Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations",
    author = "Lee, I-Ta  and
      Pacheco, Maria Leonor  and
      Goldwasser, Dan",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.findings-emnlp.446/",
    doi = "10.18653/v1/2020.findings-emnlp.446",
    pages = "4962--4972",
    abstract = "Representing, and reasoning over, long narratives requires models that can deal with complex event structures connected through multiple relationship types. This paper suggests to represent this type of information as a narrative graph and learn contextualized event representations over it using a relational graph neural network model. We train our model to capture event relations, derived from the Penn Discourse Tree Bank, on a huge corpus, and show that our multi-relational contextualized event representation can improve performance when learning script knowledge without direct supervision and provide a better representation for the implicit discourse sense classification task."
}

@article{RAHMAN20226643,
title = {An unsupervised method for word sense disambiguation},
journal = {Journal of King Saud University - Computer and Information Sciences},
volume = {34},
number = {9},
pages = {6643-6651},
year = {2022},
issn = {1319-1578},
doi = {https://doi.org/10.1016/j.jksuci.2021.07.022},
url = {https://www.sciencedirect.com/science/article/pii/S1319157821002007},
author = {Nazreena Rahman and Bhogeswar Borah},
keywords = {Word sense disambiguation (WSD), WordNet lexical dictionary, Wikipedia corpus, Collocation extraction score, Knowledge-based unsupervised and supervised systems, Senseval and SemEval datasets},
abstract = {Word sense disambiguation (WSD) finds the actual meaning of a word according to its context. This paper presents a novel WSD method to find the correct sense of a word present in a sentence. The proposed method uses both the WordNet lexical dictionary and the Wikipedia corpus. Initially, we find all the probable senses of the target word using WordNet. For each of the words present in a sense, we calculate the collocation extraction score with the other words in the sentence. The collocation extraction score finds the probability of the occurrence of two words together in the Wikipedia corpus. The maximum collocation extraction score assigns the proper sense for that context of the sentence. Our method is not limited to the bi-grams that are made up of only two consecutive words. Our method can find the probability of having two words together in a sentence when other words separate these two words. To compare our WSD method with current knowledge-based unsupervised and supervised systems, we use different Senseval and SemEval datasets for doing WSD on English words. Finally, the experimental analysis illustrates the significance of the proposed approach over many baseline and current systems.}
}

@inproceedings{shen-etal-2013-coarse,
    title = "Coarse to Fine Grained Sense Disambiguation in {W}ikipedia",
    author = "Shen, Hui  and
      Bunescu, Razvan  and
      Mihalcea, Rada",
    editor = "Diab, Mona  and
      Baldwin, Tim  and
      Baroni, Marco",
    booktitle = "Second Joint Conference on Lexical and Computational Semantics (*{SEM}), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity",
    month = jun,
    year = "2013",
    address = "Atlanta, Georgia, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S13-1003/",
    pages = "22--31"
}

@inproceedings{zhong-ng-2010-makes,
    title = "It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text",
    author = "Zhong, Zhi  and
      Ng, Hwee Tou",
    editor = {K{\"u}bler, Sandra},
    booktitle = "Proceedings of the {ACL} 2010 System Demonstrations",
    month = jul,
    year = "2010",
    address = "Uppsala, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P10-4014/",
    pages = "78--83"
}

@inproceedings{krovetz1989word,
  title={Word sense disambiguation using machine-readable dictionaries},
  author={Krovetz, Robert and Croft, W Bruce},
  booktitle={Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval},
  pages={127--136},
  year={1989}
}

@inproceedings{raganato-etal-2017-word,
    title = "Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison",
    author = "Raganato, Alessandro  and
      Camacho-Collados, Jose  and
      Navigli, Roberto",
    editor = "Lapata, Mirella  and
      Blunsom, Phil  and
      Koller, Alexander",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/E17-1010/",
    pages = "99--110",
    abstract = "Word Sense Disambiguation is a long-standing task in Natural Language Processing, lying at the core of human language understanding. However, the evaluation of automatic systems has been problematic, mainly due to the lack of a reliable evaluation framework. In this paper we develop a unified evaluation framework and analyze the performance of various Word Sense Disambiguation systems in a fair setup. The results show that supervised systems clearly outperform knowledge-based models. Among the supervised systems, a linear classifier trained on conventional local features still proves to be a hard baseline to beat. Nonetheless, recent approaches exploiting neural networks on unlabeled corpora achieve promising results, surpassing this hard baseline in most test sets."
}

@article{chicco2021siamese,
  title={Siamese neural networks: An overview},
  author={Chicco, Davide},
  journal={Artificial neural networks},
  pages={73--94},
  year={2021},
  publisher={Springer}
}

@article{dong2023word,
  title={Word sense disambiguation as a game of neurosymbolic darts},
  author={Dong, Tiansi and Sifa, Rafet},
  journal={arXiv preprint arXiv:2307.16663},
  year={2023}
}

@inproceedings{akiba2019optuna,
  title={Optuna: A next-generation hyperparameter optimization framework},
  author={Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori},
  booktitle={Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery \& data mining},
  pages={2623--2631},
  year={2019}
}

@article{basile2025exploring,
  title={Exploring the Word Sense Disambiguation Capabilities of Large Language Models},
  author={Basile, Pierpaolo and Siciliani, Lucia and Musacchio, Elio and Semeraro, Giovanni},
  journal={arXiv preprint arXiv:2503.08662},
  year={2025}
}

@inproceedings{mihalcea2004senseval,
  title={The Senseval-3 English lexical sample task},
  author={Mihalcea, Rada and Chklovski, Timothy and Kilgarriff, Adam},
  booktitle={Proceedings of SENSEVAL-3, the third international workshop on the evaluation of systems for the semantic analysis of text},
  pages={25--28},
  year={2004}
}

@article{guo2024knowledge,
  title={Knowledge graph enhanced language agents for recommendation},
  author={Guo, Taicheng and Liu, Chaochun and Wang, Hai and Mannam, Varun and Wang, Fang and Chen, Xin and Zhang, Xiangliang and Reddy, Chandan K},
  journal={arXiv preprint arXiv:2410.19627},
  year={2024}
}

@inproceedings{moro2015semeval,
  title={Semeval-2015 task 13: Multilingual all-words sense disambiguation and entity linking},
  author={Moro, Andrea and Navigli, Roberto},
  booktitle={Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015)},
  pages={288--297},
  year={2015}
}

@inproceedings{navigli2013semeval,
  title={Semeval-2013 task 12: Multilingual word sense disambiguation},
  author={Navigli, Roberto and Jurgens, David and Vannella, Daniele},
  booktitle={Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)},
  pages={222--231},
  year={2013}
}

@inproceedings{martelli-etal-2021-semeval,
    title = "{S}em{E}val-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation ({MCL}-{W}i{C})",
    author = "Martelli, Federico  and
      Kalach, Najla  and
      Tola, Gabriele  and
      Navigli, Roberto",
    editor = "Palmer, Alexis  and
      Schneider, Nathan  and
      Schluter, Natalie  and
      Emerson, Guy  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.semeval-1.3/",
    doi = "10.18653/v1/2021.semeval-1.3",
    pages = "24--36",
    abstract = "In this paper, we introduce the first SemEval task on Multilingual and Cross-Lingual Word-in-Context disambiguation (MCL-WiC). This task allows the largely under-investigated inherent ability of systems to discriminate between word senses within and across languages to be evaluated, dropping the requirement of a fixed sense inventory. Framed as a binary classification, our task is divided into two parts. In the multilingual sub-task, participating systems are required to determine whether two target words, each occurring in a different context within the same language, express the same meaning or not. Instead, in the cross-lingual part, systems are asked to perform the task in a cross-lingual scenario, in which the two target words and their corresponding contexts are provided in two different languages. We illustrate our task, as well as the construction of our manually-created dataset including five languages, namely Arabic, Chinese, English, French and Russian, and the results of the participating systems. Datasets and results are available at: \url{https://github.com/SapienzaNLP/mcl-wic}."
}

@article{bai2022,
  title={Graph Pre-training for AMR Parsing and Generation},
  author={Bai, Xuefeng and Chen, Yulong and Zhang, Yue},
  journal={arXiv preprint arXiv:2203.07836},
  year={2022},
}

@article{jin2024AMR,
  title={Analyzing the Role of Semantic Representations
in the Era of Large Language Models},
  author={Jin, Zhijing and Chen, Yuen and Gonzalez, Fernando and Liu, Jiarui and Zhang, Jiayi and Michael, Julian and Schölkopf, Bernhard and Diab, Mona},
  journal={arXiv preprint arXiv:2405.01502},
  year={2024},
}

@inproceedings{Bevilacqua2021,
    title = "One {SPRING} to Rule Them Both:
Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline",
    author = "Bevilacqua, Michele and Blloshmi, Rexhina and Navigli, Roberto",
    editor = {AAAI},
    booktitle = "Proceedings of the AAAI Conference on Artificial Intelligence",
    month = may,
    year = "2021",
    address = "Online",
    publisher = "PKP Publishing Services Network",
    url = "https://doi.org/10.1609/aaai.v35i14.17489",
    pages = "12564--12573"

@article{coral2020,
title={Rank consistent ordinal regression for neural networks with application to age estimation},
journal={Pattern Recognition Letters},
volume={140},
pages={325-331},
year={2020},
issn={0167-8655},
doi={https://doi.org/10.1016/j.patrec.2020.11.008},
url={http://www.sciencedirect.com/science/article/pii/S016786552030413X},
author={Wenzhi Cao and Vahid Mirjalili and Sebastian Raschka}
}

@article{AMR3,
    author = {Knight, Kevin and Badarau, Bianca and Baranescu, Laura and Bonial, Claire and Bardocz, Madalina and Griffitt, Kira and Hermjakob, Ulf and Marcu, Daniel and Palmer, Martha and O'Gorman, Tim and Schneider, Nathan},
    title = {Abstract Meaning Representation (AMR) Annotation Release 3.0 LDC2020T02},
    journal = {Philadelphia: Linguistic Data Consortium},
    year = {2020},
    doi = 	{https://doi.org/10.35111/44cy-bp51}
}

@misc{bjascob,
  author = {Jascob, Brad},
  title = {amrlib Models},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/bjascob/amrlib-models}}
}

@inproceedings{edmonds-cotton-2001-senseval,
    title = "{SENSEVAL}-2: Overview",
    author = "Edmonds, Philip  and
      Cotton, Scott",
    editor = "Preiss, Judita  and
      Yarowsky, David",
    booktitle = "Proceedings of {SENSEVAL}-2 Second International Workshop on Evaluating Word Sense Disambiguation Systems",
    month = jul,
    year = "2001",
    address = "Toulouse, France",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S01-1001/",
    pages = "1--5"
}

@inproceedings{mihalcea-etal-2004-senseval,
    title = "The Senseval-3 {E}nglish lexical sample task",
    author = "Mihalcea, Rada  and
      Chklovski, Timothy  and
      Kilgarriff, Adam",
    booktitle = "Proceedings of {SENSEVAL}-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text",
    month = jul,
    year = "2004",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W04-0807/",
    pages = "25--28"
}

@inproceedings{pradhan-etal-2007-semeval,
    title = "{S}em{E}val-2007 Task-17: {E}nglish Lexical Sample, {SRL} and All Words",
    author = "Pradhan, Sameer  and
      Loper, Edward  and
      Dligach, Dmitriy  and
      Palmer, Martha",
    editor = "Agirre, Eneko  and
      M{\`a}rquez, Llu{\'i}s  and
      Wicentowski, Richard",
    booktitle = "Proceedings of the Fourth International Workshop on Semantic Evaluations ({S}em{E}val-2007)",
    month = jun,
    year = "2007",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S07-1016/",
    pages = "87--92"
}

@inproceedings{navigli-etal-2013-semeval,
    title = "{S}em{E}val-2013 Task 12: Multilingual Word Sense Disambiguation",
    author = "Navigli, Roberto  and
      Jurgens, David  and
      Vannella, Daniele",
    editor = "Manandhar, Suresh  and
      Yuret, Deniz",
    booktitle = "Second Joint Conference on Lexical and Computational Semantics (*{SEM}), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation ({S}em{E}val 2013)",
    month = jun,
    year = "2013",
    address = "Atlanta, Georgia, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S13-2040/",
    pages = "222--231"
}

@inproceedings{moro-navigli-2015-semeval,
    title = "{S}em{E}val-2015 Task 13: Multilingual All-Words Sense Disambiguation and Entity Linking",
    author = "Moro, Andrea  and
      Navigli, Roberto",
    editor = "Nakov, Preslav  and
      Zesch, Torsten  and
      Cer, Daniel  and
      Jurgens, David",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S15-2049/",
    doi = "10.18653/v1/S15-2049",
    pages = "288--297"
}

@inproceedings{martelli-etal-2021-semeval,
    title = "{S}em{E}val-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation ({MCL}-{W}i{C})",
    author = "Martelli, Federico  and
      Kalach, Najla  and
      Tola, Gabriele  and
      Navigli, Roberto",
    editor = "Palmer, Alexis  and
      Schneider, Nathan  and
      Schluter, Natalie  and
      Emerson, Guy  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.semeval-1.3/",
    doi = "10.18653/v1/2021.semeval-1.3",
    pages = "24--36",
    abstract = "In this paper, we introduce the first SemEval task on Multilingual and Cross-Lingual Word-in-Context disambiguation (MCL-WiC). This task allows the largely under-investigated inherent ability of systems to discriminate between word senses within and across languages to be evaluated, dropping the requirement of a fixed sense inventory. Framed as a binary classification, our task is divided into two parts. In the multilingual sub-task, participating systems are required to determine whether two target words, each occurring in a different context within the same language, express the same meaning or not. Instead, in the cross-lingual part, systems are asked to perform the task in a cross-lingual scenario, in which the two target words and their corresponding contexts are provided in two different languages. We illustrate our task, as well as the construction of our manually-created dataset including five languages, namely Arabic, Chinese, English, French and Russian, and the results of the participating systems. Datasets and results are available at: \url{https://github.com/SapienzaNLP/mcl-wic}."
}

@inproceedings{raganato-etal-2023-semeval,
    title = "{S}em{E}val-2023 Task 1: Visual Word Sense Disambiguation",
    author = "Raganato, Alessandro  and
      Calixto, Iacer  and
      Ushio, Asahi  and
      Camacho-Collados, Jose  and
      Pilehvar, Mohammad Taher",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.308/",
    doi = "10.18653/v1/2023.semeval-1.308",
    pages = "2227--2234",
    abstract = "This paper presents the Visual Word Sense Disambiguation (Visual-WSD) task. The objective of Visual-WSD is to identify among a set of ten images the one that corresponds to the intended meaning of a given ambiguous word which is accompanied with minimal context. The task provides datasets for three different languages: English, Italian, and Farsi.We received a total of 96 different submissions. Out of these, 40 systems outperformed a strong zero-shot CLIP-based baseline. Participating systems proposed different zero- and few-shot approaches, often involving generative models and data augmentation. More information can be found on the task{'}s website: {\textbackslash}url{\{}https://raganato.github.io/vwsd/{\}}."
}

@article{loureiro2021analysis,
  title={Analysis and evaluation of language models for word sense disambiguation},
  author={Loureiro, Daniel and Rezaee, Kiamehr and Pilehvar, Mohammad Taher and Camacho-Collados, Jose},
  journal={Computational Linguistics},
  volume={47},
  number={2},
  pages={387--443},
  year={2021},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}
@article{conneau2017supervised,
  title={Supervised learning of universal sentence representations from natural language inference data},
  author={Conneau, Alexis and Kiela, Douwe and Schwenk, Holger and Barrault, Lo{\"\i}c and Bordes, Antoine},
  journal={arXiv preprint arXiv:1705.02364},
  year={2017}
}
@inproceedings{pennington2014glove,
  title={Glove: Global vectors for word representation},
  author={Pennington, Jeffrey and Socher, Richard and Manning, Christopher D},
  booktitle={Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)},
  pages={1532--1543},
  year={2014}
}

@inproceedings{langkilde-knight-1998-generation,
    title = "Generation that Exploits Corpus-Based Statistical Knowledge",
    author = "Langkilde, Irene  and
      Knight, Kevin",
    booktitle = "36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1",
    month = aug,
    year = "1998",
    address = "Montreal, Quebec, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P98-1116/",
    doi = "10.3115/980845.980963",
    pages = "704--710"
}

@inproceedings{ribeiro-etal-2021-investigating,
    title = "Investigating Pretrained Language Models for Graph-to-Text Generation",
    author = {Ribeiro, Leonardo F. R.  and
      Schmitt, Martin  and
      Sch{\"u}tze, Hinrich  and
      Gurevych, Iryna},
    editor = "Papangelis, Alexandros  and
      Budzianowski, Pawe{\l}  and
      Liu, Bing  and
      Nouri, Elnaz  and
      Rastogi, Abhinav  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI",
    month = nov,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.nlp4convai-1.20/",
    doi = "10.18653/v1/2021.nlp4convai-1.20",
    pages = "211--227",
    abstract = "Graph-to-text generation aims to generate fluent texts from graph-based data. In this paper, we investigate two recent pretrained language models (PLMs) and analyze the impact of different task-adaptive pretraining strategies for PLMs in graph-to-text generation. We present a study across three graph domains: meaning representations, Wikipedia knowledge graphs (KGs) and scientific KGs. We show that approaches based on PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further. We report new state-of-the-art BLEU scores of 49.72 on AMR-LDC2017T10, 59.70 on WebNLG, and 25.66 on AGENDA datasets - a relative improvement of 31.8{\%}, 4.5{\%}, and 42.4{\%}, respectively, with our models generating significantly more fluent texts than human references. In an extensive analysis, we identify possible reasons for the PLMs' success on graph-to-text tasks. Our findings suggest that the PLMs benefit from similar facts seen during pretraining or fine-tuning, such that they perform well even when the input graph is reduced to a simple bag of node and edge labels."
}

@inproceedings{kibria-etal-2024-functional,
    title = "On Functional Competence of {LLM}s for Linguistic Disambiguation",
    author = "Kibria, Raihan  and
      Dipta, Sheikh Intiser Uddin  and
      Adnan, Muhammad Abdullah",
    editor = "Barak, Libby  and
      Alikhani, Malihe",
    booktitle = "Proceedings of the 28th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2024",
    address = "Miami, FL, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.conll-1.12/",
    doi = "10.18653/v1/2024.conll-1.12",
    pages = "143--160",
    abstract = "We study some Large Language Models to explore their deficiencies in resolving sense ambiguities. In this connection, we evaluate their performance on well-known word sense disambiguation datasets. Word Sense Disambiguation (WSD) has been a long-standing NLP problem, which has given rise to many evaluation datasets and models over the decades. Recently the emergence of Large Language Models (LLM) raises much hope in improving accuracy. In this work, we evaluate word sense disambiguation capabilities of four LLMs: OpenAI{'}s ChatGPT-3.5, Mistral{'}s 7b parameter model, Meta{'}s Llama 70b, and Google{'}s Gemini Pro. We evaluate many well-established datasets containing a variety of texts and senses on these. After observing the performances of some datasets, we selectively study some failure cases and identify the reasons for failures. We explore human judgments that would correct these failures. Our findings suggest that many failure cases are related to a lack of world knowledge and the reasoning to amalgamate this knowledge rather than the lack of linguistic knowledge. We categorize the judgments so that the next generation of LLMs can improve by incorporating deeper world knowledge and reasoning. We conclude that word sense disambiguation could serve as a guide for probing the reasoning power of LLMs to measure their functional competency. We also list the accuracy of these datasets. We find that on many occasions, accuracy drops to below 70{\%}, which is much less than that of well-performing existing models."
}

@inproceedings{brown-et-al-2020,
author = {Brown, Tom B. and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel M. and Wu, Jeffrey and Winter, Clemens and Hesse, Christopher and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},
title = {Language models are few-shot learners},
year = {2020},
isbn = {9781713829546},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
abstract = {We demonstrate that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even becoming competitive with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks. We also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora.},
booktitle = {Proceedings of the 34th International Conference on Neural Information Processing Systems},
articleno = {159},
numpages = {25},
location = {Vancouver, BC, Canada},
series = {NIPS '20}
}

@misc{minaee2025largelanguagemodelssurvey,
      title={Large Language Models: A Survey}, 
      author={Shervin Minaee and Tomas Mikolov and Narjes Nikzad and Meysam Chenaghlu and Richard Socher and Xavier Amatriain and Jianfeng Gao},
      year={2025},
      eprint={2402.06196},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2402.06196}, 
}

@inproceedings{semeval2026-task-5,
title = "{S}em{E}val-2026 Task 5: Rating Plausibility of Word Senses in Ambiguous Stories through Narrative Understanding",
author = "Gehring, Janosch and Meyer, Selina and Roth, Michael",
booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation",
month = jul,
year = "2026",
address = "San Diego, California",
publisher = "Association for Computational Linguistics"
}

@inproceedings{raganato2023semeval,
  title={SemEval-2023 task 1: Visual word sense disambiguation},
  author={Raganato, Alessandro and Calixto, Iacer and Ushio, Asahi and Camacho-Collados, Jose and Pilehvar, Mohammad Taher},
  booktitle={Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)},
  pages={2227--2234},
  year={2023}
}
