@inproceedings{malmasi-etal-2022-semeval,
    title = "{S}em{E}val-2022 Task 11: Multilingual Complex Named Entity Recognition ({M}ulti{C}o{NER})",
    author = "Malmasi, Shervin  and
      Fang, Anjie  and
      Fetahu, Besnik  and
      Kar, Sudipta  and
      Rokhlenko, Oleg",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.semeval-1.196",
    doi = "10.18653/v1/2022.semeval-1.196",
    pages = "1412--1437"
}

@inproceedings{malmasi-etal-2022-multiconer,
    title = "{M}ulti{C}o{NER}: A Large-scale Multilingual Dataset for Complex Named Entity Recognition",
    author = "Malmasi, Shervin  and
      Fang, Anjie  and
      Fetahu, Besnik  and
      Kar, Sudipta  and
      Rokhlenko, Oleg",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.334",
    pages = "3798--3809"
}
@inproceedings{fetahu2021gazetteer,
  title={{Gazetteer Enhanced Named Entity Recognition for Code-Mixed Web Queries}},
  author={Fetahu, Besnik and Fang, Anjie and Rokhlenko, Oleg and Malmasi, Shervin},
  booktitle={Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={1677--1681},
  year={2021}
}

@inproceedings{meng2021gemnet,
  title={{GEMNET: Effective gated gazetteer representations for recognizing complex entities in low-context input}},
  author={Meng, Tao and Fang, Anjie and Rokhlenko, Oleg and Malmasi, Shervin},
  booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  pages={1499--1512},
  year={2021}
}




@inproceedings{multiconer2-report,
    title={{SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)}},
    author={Fetahu, Besnik and Kar, Sudipta and Chen, Zhiyu and Rokhlenko, Oleg and Malmasi, Shervin},
    booktitle={Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)},
    year={2023},
    publisher={Association for Computational Linguistics},
}

@article{multiconer2-data,
    title={{MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition}},
    author={Fetahu, Besnik and Chen, Zhiyu and Kar, Sudipta and Rokhlenko, Oleg and Malmasi, Shervin},
    year={2023},
}
@inproceedings{singh-etal-2022-silpa,
    title = "silpa{\_}nlp at {S}em{E}val-2022 Tasks 11: Transformer based {NER} models for {H}indi and {B}angla languages",
    author = "Singh, Sumit  and
      Jawale, Pawankumar  and
      Tiwary, Uma",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.semeval-1.211",
    doi = "10.18653/v1/2022.semeval-1.211",
    pages = "1536--1542",
    abstract = "We present Transformer based pretrained models, which are fine-tuned for Named Entity Recognition (NER) task. Our team participated in SemEval-2022 Task 11 MultiCoNER: Multilingual Complex Named Entity Recognition task for Hindi and Bangla. Result comparison of six models (mBERT, IndicBERT, MuRIL (Base), MuRIL (Large), XLM-RoBERTa (Base) and XLM-RoBERTa (Large) ) has been performed. It is found that among these models MuRIL (Large) model performs better for both the Hindi and Bangla languages. Its F1-Scores for Hindi and Bangla are 0.69 and 0.59 respectively.",
}
@inproceedings{wang-etal-2022-damo,
    title = "{DAMO}-{NLP} at {S}em{E}val-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition",
    author = "Wang, Xinyu  and
      Shen, Yongliang  and
      Cai, Jiong  and
      Wang, Tao  and
      Wang, Xiaobin  and
      Xie, Pengjun  and
      Huang, Fei  and
      Lu, Weiming  and
      Zhuang, Yueting  and
      Tu, Kewei  and
      Lu, Wei  and
      Jiang, Yong",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.semeval-1.200",
    doi = "10.18653/v1/2022.semeval-1.200",
    pages = "1457--1468",
}

@inproceedings{he-etal-2022-infrrd,
    title = "Infrrd.ai at {S}em{E}val-2022 Task 11: A system for named entity recognition using data augmentation, transformer-based sequence labeling model, and {E}nsemble{CRF}",
    author = "He, Jianglong  and
      Uppal, Akshay  and
      N, Mamatha  and
      Vignesh, Shiv  and
      Kumar, Deepak  and
      Kumar Sarda, Aditya",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.semeval-1.206",
    doi = "10.18653/v1/2022.semeval-1.206",
    pages = "1501--1510",
    
}


@inproceedings{wolf2020huggingfaces,
      title={HuggingFace's Transformers: State-of-the-art Natural Language Processing}, 
      author={Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush},
      year={2020},
      eprint={1910.03771},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}


@inproceedings{liu2019roberta,
      title={RoBERTa: A Robustly Optimized BERT Pretraining Approach}, 
      author={Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov},
      year={2019},
      eprint={1907.11692},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{devlin2019bert,
      title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding}, 
      author={Jacob Devlin and Ming-Wei Chang and Kenton Lee and Kristina Toutanova},
      year={2019},
      eprint={1810.04805},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{https://doi.org/10.48550/arxiv.2103.10730,
  doi = {10.48550/ARXIV.2103.10730},
  url = {https://arxiv.org/abs/2103.10730},
  author = {Khanuja, Simran and Bansal, Diksha and Mehtani, Sarvesh and Khosla, Savya and Dey, Atreyee and Gopalan, Balaji and Margam, Dilip Kumar and Aggarwal, Pooja and Nagipogu, Rajiv Teja and Dave, Shachi and Gupta, Shruti and Gali, Subhash Chandra Bose and Subramanian, Vish and Talukdar, Partha},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {MuRIL: Multilingual Representations for Indian Languages},
  publisher = {arXiv},
  year = {2021},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@misc{conneau2020unsupervised,
      title={Unsupervised Cross-lingual Representation Learning at Scale}, 
      author={Alexis Conneau and Kartikay Khandelwal and Naman Goyal and Vishrav Chaudhary and Guillaume Wenzek and Francisco Guzmán and Edouard Grave and Myle Ott and Luke Zettlemoyer and Veselin Stoyanov},
      year={2020},
      eprint={1911.02116},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{chen-etal-2022-ustc,
    title = "{USTC}-{NELSLIP} at {S}em{E}val-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition",
    author = "Chen, Beiduo  and
      Ma, Jun-Yu  and
      Qi, Jiajun  and
      Guo, Wu  and
      Ling, Zhen-Hua  and
      Liu, Quan",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.semeval-1.223",
    doi = "10.18653/v1/2022.semeval-1.223",
    pages = "1613--1622",
    
}

@misc{https://doi.org/10.48550/arxiv.2103.10730,
  doi = {10.48550/ARXIV.2103.10730},
  
  url = {https://arxiv.org/abs/2103.10730},
  
  author = {Khanuja, Simran and Bansal, Diksha and Mehtani, Sarvesh and Khosla, Savya and Dey, Atreyee and Gopalan, Balaji and Margam, Dilip Kumar and Aggarwal, Pooja and Nagipogu, Rajiv Teja and Dave, Shachi and Gupta, Shruti and Gali, Subhash Chandra Bose and Subramanian, Vish and Talukdar, Partha},
  
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {MuRIL: Multilingual Representations for Indian Languages},
  
  publisher = {arXiv},
  
  year = {2021},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@inproceedings{fu-etal-2016-iscas,
    title = "{ISCAS}{\_}{NLP} at {S}em{E}val-2016 Task 1: Sentence Similarity Based on Support Vector Regression using Multiple Features",
    author = "Fu, Cheng  and
      An, Bo  and
      Han, Xianpei  and
      Sun, Le",
    editor = "Bethard, Steven  and
      Carpuat, Marine  and
      Cer, Daniel  and
      Jurgens, David  and
      Nakov, Preslav  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S16-1098",
    doi = "10.18653/v1/S16-1098",
    pages = "645--649",
}

@inproceedings{liu-etal-2017-itnlp,
    title = "{ITNLP}-{A}i{KF} at {S}em{E}val-2017 Task 1: Rich Features Based {SVR} for Semantic Textual Similarity Computing",
    author = "Liu, Wenjie  and
      Sun, Chengjie  and
      Lin, Lei  and
      Liu, Bingquan",
    editor = "Bethard, Steven  and
      Carpuat, Marine  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      Cer, Daniel  and
      Jurgens, David",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S17-2022",
    doi = "10.18653/v1/S17-2022",
    pages = "159--163",
    abstract = "Semantic Textual Similarity (STS) devotes to measuring the degree of equivalence in the underlying semantic of the sentence pair. We proposed a new system, ITNLP-AiKF, which applies in the SemEval 2017 Task1 Semantic Textual Similarity track 5 English monolingual pairs. In our system, rich features are involved, including Ontology based, word embedding based, Corpus based, Alignment based and Literal based feature. We leveraged the features to predict sentence pair similarity by a Support Vector Regression (SVR) model. In the result, a Pearson Correlation of 0.8231 is achieved by our system, which is a competitive result in the contest of this track.",
}

@misc{ousidhoum2024semrel2024,
title={SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages}, 
author={Nedjma Ousidhoum and Shamsuddeen Hassan Muhammad and Mohamed Abdalla and Idris Abdulmumin and Ibrahim Said Ahmad and
Sanchit Ahuja and Alham Fikri Aji and Vladimir Araujo and Abinew Ali Ayele and Pavan Baswani and Meriem Beloucif and
Chris Biemann and Sofia Bourhim and Christine De Kock and Genet Shanko Dekebo and
Oumaima Hourrane and Gopichand Kanumolu and Lokesh Madasu and Samuel Rutunda and Manish Shrivastava and
Thamar Solorio and Nirmal Surange and Hailegnaw Getaneh Tilaye and Krishnapriya Vishnubhotla and Genta Winata and
Seid Muhie Yimam and Saif M. Mohammad},
      year={2024},
      eprint={2402.08638},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}  

@inproceedings{ousidhoum-etal-2024-semeval, 
title = "{S}em{E}val-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages",
author = "Ousidhoum, Nedjma and Muhammad, Shamsuddeen Hassan and Abdalla, Mohamed and Abdulmumin, Idris and
Ahmad,Ibrahim Said and Ahuja, Sanchit and Aji, Alham Fikri and Araujo, Vladimir and     Beloucif, Meriem and
De Kock, Christine and Hourrane, Oumaima and Shrivastava, Manish and Solorio, Thamar and Surange, Nirmal and
Vishnubhotla, Krishnapriya and Yimam, Seid Muhie and Mohammad, Saif M.",
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
year = "2024",
publisher = "Association for Computational Linguistics"
}

@inproceedings{agirre-etal-2015-semeval,
    title = "{S}em{E}val-2015 Task 2: Semantic Textual Similarity, {E}nglish, {S}panish and Pilot on Interpretability",
    author = "Agirre, Eneko  and
      Banea, Carmen  and
      Cardie, Claire  and
      Cer, Daniel  and
      Diab, Mona  and
      Gonzalez-Agirre, Aitor  and
      Guo, Weiwei  and
      Lopez-Gazpio, I{\~n}igo  and
      Maritxalar, Montse  and
      Mihalcea, Rada  and
      Rigau, German  and
      Uria, Larraitz  and
      Wiebe, Janyce",
    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/S15-2045",
    doi = "10.18653/v1/S15-2045",
    pages = "252--263",
}
@inproceedings{cer-etal-2017-semeval,
    title = "{S}em{E}val-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation",
    author = "Cer, Daniel  and
      Diab, Mona  and
      Agirre, Eneko  and
      Lopez-Gazpio, I{\~n}igo  and
      Specia, Lucia",
    editor = "Bethard, Steven  and
      Carpuat, Marine  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      Cer, Daniel  and
      Jurgens, David",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S17-2001",
    doi = "10.18653/v1/S17-2001",
    pages = "1--14",
    abstract = "Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in \textit{all language tracks}. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the \textit{STS Benchmark} is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017).",
}
@inproceedings{han-etal-2015-samsung,
    title = "{S}amsung: Align-and-Differentiate Approach to Semantic Textual Similarity",
    author = "Han, Lushan  and
      Martineau, Justin  and
      Cheng, Doreen  and
      Thomas, Christopher",
    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/S15-2031",
    doi = "10.18653/v1/S15-2031",
    pages = "172--177",
}
@inproceedings{tian-etal-2017-ecnu,
    title = "{ECNU} at {S}em{E}val-2017 Task 1: Leverage Kernel-based Traditional {NLP} features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity",
    author = "Tian, Junfeng  and
      Zhou, Zhiheng  and
      Lan, Man  and
      Wu, Yuanbin",
    editor = "Bethard, Steven  and
      Carpuat, Marine  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      Cer, Daniel  and
      Jurgens, David",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S17-2028",
    doi = "10.18653/v1/S17-2028",
    pages = "191--197",
    abstract = "To address semantic similarity on multilingual and cross-lingual sentences, we firstly translate other foreign languages into English, and then feed our monolingual English system with various interactive features. Our system is further supported by combining with deep learning semantic similarity and our best run achieves the mean Pearson correlation 73.16{\%} in primary track.",
}

@inproceedings{abdalla-etal-2023-makes,
    title = "What Makes Sentences Semantically Related? A Textual Relatedness Dataset and Empirical Study",
    author = "Abdalla, Mohamed  and
      Vishnubhotla, Krishnapriya  and
      Mohammad, Saif",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.eacl-main.55",
    doi = "10.18653/v1/2023.eacl-main.55",
    pages = "782--796",
    abstract = "The degree of semantic relatedness of two units of language has long been considered fundamental to understanding meaning. Additionally, automatically determining relatedness has many applications such as question answering and summarization. However, prior NLP work has largely focused on semantic similarity, a subset of relatedness, because of a lack of relatedness datasets. In this paper, we introduce a dataset for Semantic Textual Relatedness, STR-2022, that has 5,500 English sentence pairs manually annotated using a comparative annotation framework, resulting in fine-grained scores. We show that human intuition regarding relatedness of sentence pairs is highly reliable, with a repeat annotation correlation of 0.84. We use the dataset to explore questions on what makes sentences semantically related. We also show the utility of STR-2022 for evaluating automatic methods of sentence representation and for various downstream NLP tasks. Our dataset, data statement, and annotation questionnaire can be found at: \url{https://doi.org/10.5281/zenodo.7599667}.",
}

@inproceedings{singh-tiwary-2023-silp,
    title = "Silp{\_}nlp at {S}em{E}val-2023 Task 2: Cross-lingual Knowledge Transfer for Mono-lingual Learning",
    author = "Singh, Sumit  and
      Tiwary, Uma",
    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-1.164",
    doi = "10.18653/v1/2023.semeval-1.164",
    pages = "1183--1189",
    abstract = "Our team silp{\_}nlp participated in SemEval2023 Task 2: MultiCoNER II. Our work made systems for 11 mono-lingual tracks. For leveraging the advantage of all track knowledge we chose transformer-based pretrained models, which have strong cross-lingual transferability. Hence our model trained in two stages, the first stage for multi-lingual learning from all tracks and the second for fine-tuning individual tracks. Our work highlights that the knowledge of all tracks can be transferred to an individual track if the baseline language model has crosslingual features. Our system positioned itself in the top 10 for 4 tracks by scoring 0.7432 macro F1 score for the Hindi track ( 7th rank ) and 0.7322 macro F1 score for the Bangla track ( 9th rank ).",
}

@inproceedings{reimers-gurevych-2019-sentence,
    title = "Sentence-{BERT}: Sentence Embeddings using {S}iamese {BERT}-Networks",
    author = "Reimers, Nils  and
      Gurevych, Iryna",
    editor = "Inui, Kentaro  and
      Jiang, Jing  and
      Ng, Vincent  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-1410",
    doi = "10.18653/v1/D19-1410",
    pages = "3982--3992",
    abstract = "BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10,000 sentences requires about 50 million inference computations ({\textasciitilde}65 hours) with BERT. The construction of BERT makes it unsuitable for semantic similarity search as well as for unsupervised tasks like clustering. In this publication, we present Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT. We evaluate SBERT and SRoBERTa on common STS tasks and transfer learning tasks, where it outperforms other state-of-the-art sentence embeddings methods.",
}
