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

@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
    publisher = {American Psychological Association},
    address = {Washington, DC}
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {L1}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK}
}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005}
}

@inproceedings{jullien-etal-2023-semeval,
    title = "{S}em{E}val-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data",
    author = {Jullien, Ma{\"e}l  and
      Valentino, Marco  and
      Frost, Hannah  and
      O{'}regan, Paul  and
      Landers, Donal  and
      Freitas, Andr{\'e}},
    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-1.307",
    doi = "10.18653/v1/2023.semeval-1.307",
    pages = "2216--2226",
    abstract = "This paper describes the results of SemEval 2023 task 7 {--} Multi-Evidence Natural Language Inference for Clinical Trial Data (NLI4CT) {--} consisting of 2 tasks, a Natural Language Inference (NLI) task, and an evidence selection task on clinical trial data. The proposed challenges require multi-hop biomedical and numerical reasoning, which are of significant importance to the development of systems capable of large-scale interpretation and retrieval of medical evidence, to provide personalized evidence-based care. Task 1, the entailment task, received 643 submissions from 40 participants, and Task 2, the evidence selection task, received 364 submissions from 23 participants. The tasks are challenging, with the majority of submitted systems failing to significantly outperform the majority class baseline on the entailment task, and we observe significantly better performance on the evidence selection task than on the entailment task. Increasing the number of model parameters leads to a direct increase in performance, far more significant than the effect of biomedical pre-training. Future works could explore the limitations of large models for generalization and numerical inference, and investigate methods to augment clinical datasets to allow for more rigorous testing and to facilitate fine-tuning. We envisage that the dataset, models, and results of this task will be useful to the biomedical NLI and evidence retrieval communities. The dataset, competition leaderboard, and website are publicly available.",
}

@inproceedings{jullien-etal-2024-semeval,
    title = "{S}em{E}val-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials",
    author = {Jullien, Ma{\"e}l  and
      Valentino, Marco  and
      Freitas, Andr{\'e}},
    booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
    year = "2024",
    publisher = "Association for Computational Linguistics",
}

@inproceedings{devlin-etal-2019-bert,
    title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding",
    author = "Devlin, Jacob  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1423",
    doi = "10.18653/v1/N19-1423",
    pages = "4171--4186",
    abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7{\%} (4.6{\%} absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).",
}

@article{Biobert_Lee_2019,
   title={BioBERT: a pre-trained biomedical language representation model for biomedical text mining},
   volume={36},
   ISSN={1367-4811},
   url={http://dx.doi.org/10.1093/bioinformatics/btz682},
   DOI={10.1093/bioinformatics/btz682},
   number={4},
   journal={Bioinformatics},
   publisher={Oxford University Press (OUP)},
   author={Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
   editor={Wren, Jonathan},
   year={2019},
   month=sep, pages={1234–1240} }

@article{Shickel_2018,
   title={Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis},
   volume={22},
   ISSN={2168-2208},
   url={http://dx.doi.org/10.1109/JBHI.2017.2767063},
   DOI={10.1109/jbhi.2017.2767063},
   number={5},
   journal={IEEE Journal of Biomedical and Health Informatics},
   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
   author={Shickel, Benjamin and Tighe, Patrick James and Bihorac, Azra and Rashidi, Parisa},
   year={2018},
   month=sep, pages={1589–1604} }

@inproceedings{jin-etal-2019-deep,
    title = "Deep Contextualized Biomedical Abbreviation Expansion",
    author = "Jin, Qiao  and
      Liu, Jinling  and
      Lu, Xinghua",
    editor = "Demner-Fushman, Dina  and
      Cohen, Kevin Bretonnel  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-5010",
    doi = "10.18653/v1/W19-5010",
    pages = "88--96",
    abstract = "Automatic identification and expansion of ambiguous abbreviations are essential for biomedical natural language processing applications, such as information retrieval and question answering systems. In this paper, we present DEep Contextualized Biomedical Abbreviation Expansion (DECBAE) model. DECBAE automatically collects substantial and relatively clean annotated contexts for 950 ambiguous abbreviations from PubMed abstracts using a simple heuristic. Then it utilizes BioELMo to extract the contextualized features of words, and feed those features to abbreviation-specific bidirectional LSTMs, where the hidden states of the ambiguous abbreviations are used to assign the exact definitions. Our DECBAE model outperforms other baselines by large margins, achieving average accuracy of 0.961 and macro-F1 of 0.917 on the dataset. It also surpasses human performance for expanding a sample abbreviation, and remains robust in imbalanced, low-resources and clinical settings.",
}

@misc{wang2021entailment,
      title={Entailment as Few-Shot Learner}, 
      author={Sinong Wang and Han Fang and Madian Khabsa and Hanzi Mao and Hao Ma},
      year={2021},
      eprint={2104.14690},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{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}
}

@misc{yang2020xlnet,
      title={XLNet: Generalized Autoregressive Pretraining for Language Understanding}, 
      author={Zhilin Yang and Zihang Dai and Yiming Yang and Jaime Carbonell and Ruslan Salakhutdinov and Quoc V. Le},
      year={2020},
      eprint={1906.08237},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{wang-etal-2018-glue,
    title = "{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
    author = "Wang, Alex  and
      Singh, Amanpreet  and
      Michael, Julian  and
      Hill, Felix  and
      Levy, Omer  and
      Bowman, Samuel",
    editor = "Linzen, Tal  and
      Chrupa{\l}a, Grzegorz  and
      Alishahi, Afra",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-5446",
    doi = "10.18653/v1/W18-5446",
    pages = "353--355",
    abstract = "Human ability to understand language is \textit{general, flexible, and robust}. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions.",
}

@inproceedings{williams-etal-2018-broad,
    title = "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference",
    author = "Williams, Adina  and
      Nangia, Nikita  and
      Bowman, Samuel",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N18-1101",
    doi = "10.18653/v1/N18-1101",
    pages = "1112--1122",
    abstract = "This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. At 433k examples, this resource is one of the largest corpora available for natural language inference (a.k.a. recognizing textual entailment), improving upon available resources in both its coverage and difficulty. MultiNLI accomplishes this by offering data from ten distinct genres of written and spoken English, making it possible to evaluate systems on nearly the full complexity of the language, while supplying an explicit setting for evaluating cross-genre domain adaptation. In addition, an evaluation using existing machine learning models designed for the Stanford NLI corpus shows that it represents a substantially more difficult task than does that corpus, despite the two showing similar levels of inter-annotator agreement.",
}

@misc{rajpurkar2016squad,
      title={SQuAD: 100,000+ Questions for Machine Comprehension of Text}, 
      author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
      year={2016},
      eprint={1606.05250},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{10.5555/3031843.3031909,
author = {Levesque, Hector J. and Davis, Ernest and Morgenstern, Leora},
title = {The Winograd schema challenge},
year = {2012},
isbn = {9781577355601},
publisher = {AAAI Press},
abstract = {In this paper, we present an alternative to the Turing Test that has some conceptual and practical advantages. A Wino-grad schema is a pair of sentences that differ only in one or two words and that contain a referential ambiguity that is resolved in opposite directions in the two sentences. We have compiled a collection of Winograd schemas, designed so that the correct answer is obvious to the human reader, but cannot easily be found using selectional restrictions or statistical techniques over text corpora. A contestant in the Winograd Schema Challenge is presented with a collection of one sentence from each pair, and required to achieve human-level accuracy in choosing the correct disambiguation.},
booktitle = {Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning},
pages = {552–561},
numpages = {10},
location = {Rome, Italy},
series = {KR'12}
}

@misc{liu2019improving,
      title={Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding}, 
      author={Xiaodong Liu and Pengcheng He and Weizhu Chen and Jianfeng Gao},
      year={2019},
      eprint={1904.09482},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{radford2018improving,
  added-at = {2020-07-14T16:49:49.000+0200},
  author = {Radford, Alec and Narasimhan, Karthik and Salimans, Tim and Sutskever, Ilya},
  biburl = {https://www.bibsonomy.org/bibtex/273ced32c0d4588eb95b6986dc2c8147c/jonaskaiser},
  interhash = {5c343ed9a31ac52fd17a898f72af228f},
  intrahash = {73ced32c0d4588eb95b6986dc2c8147c},
  keywords = {},
  timestamp = {2020-07-14T16:49:49.000+0200},
  title = {Improving language understanding by generative pre-training},
  year = 2018
}

@article{Lee_2019,
   title={BioBERT: a pre-trained biomedical language representation model for biomedical text mining},
   volume={36},
   ISSN={1367-4811},
   url={http://dx.doi.org/10.1093/bioinformatics/btz682},
   DOI={10.1093/bioinformatics/btz682},
   number={4},
   journal={Bioinformatics},
   publisher={Oxford University Press (OUP)},
   author={Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
   editor={Wren, Jonathan},
   year={2019},
   month=sep, pages={1234–1240} }

@inproceedings{bowman-etal-2015-large,
    title = "A large annotated corpus for learning natural language inference",
    author = "Bowman, Samuel R.  and
      Angeli, Gabor  and
      Potts, Christopher  and
      Manning, Christopher D.",
    editor = "M{\`a}rquez, Llu{\'\i}s  and
      Callison-Burch, Chris  and
      Su, Jian",
    booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D15-1075",
    doi = "10.18653/v1/D15-1075",
    pages = "632--642",
}

@inproceedings{romanov-shivade-2018-lessons,
    title = "Lessons from Natural Language Inference in the Clinical Domain",
    author = "Romanov, Alexey  and
      Shivade, Chaitanya",
    editor = "Riloff, Ellen  and
      Chiang, David  and
      Hockenmaier, Julia  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D18-1187",
    doi = "10.18653/v1/D18-1187",
    pages = "1586--1596",
    abstract = "State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training. This is even more challenging in specialized, and knowledge intensive domains, where training data is limited. To address this gap, we introduce MedNLI - a dataset annotated by doctors, performing a natural language inference task (NLI), grounded in the medical history of patients. We present strategies to: 1) leverage transfer learning using datasets from the open domain, (e.g. SNLI) and 2) incorporate domain knowledge from external data and lexical sources (e.g. medical terminologies). Our results demonstrate performance gains using both strategies.",
}

@inproceedings{bastan-etal-2022-bionli,
    title = "{B}io{NLI}: Generating a Biomedical {NLI} Dataset Using Lexico-semantic Constraints for Adversarial Examples",
    author = "Bastan, Mohaddeseh  and
      Surdeanu, Mihai  and
      Balasubramanian, Niranjan",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-emnlp.374",
    doi = "10.18653/v1/2022.findings-emnlp.374",
    pages = "5093--5104",
    abstract = "Natural language inference (NLI) is critical in many domains requiring complex decision-making, such as the biomedical domain. We introduce a novel semi-supervised procedure that bootstraps biomedical NLI datasets from positive entailment examples present in abstracts of biomedical publications. We focus on challenging texts where the hypothesis includes mechanistic information such as biochemical interactions between two entities. A key contribution of this work is automating the creation of negative examples that are informative without being simplistic. We generate a range of negative examples using nine strategies that manipulate the structure of the underlying mechanisms both with rules, e.g., flip the roles of the entities in the interaction, and, more importantly, by imposing the perturbed conditions as logical constraints in a neuro-logical decoding system (CITATION).We use this procedure to create a novel dataset for NLI in the biomedical domain, called . The accuracy of neural classifiers on this dataset is in the mid 70s F1, which indicates that this NLI task remains to be solved. Critically, we observe that the performance on the different classes of negative examples varies widely, from 97{\%} F1 on the simple negative examples that change the role of the entities in the hypothesis, to barely better than chance on the negative examples generated using neuro-logic decoding.",
}

@article{Gu_2021,
   title={Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing},
   volume={3},
   ISSN={2637-8051},
   url={http://dx.doi.org/10.1145/3458754},
   DOI={10.1145/3458754},
   number={1},
   journal={ACM Transactions on Computing for Healthcare},
   publisher={Association for Computing Machinery (ACM)},
   author={Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao, Jianfeng and Poon, Hoifung},
   year={2021},
   month=oct, pages={1–23} }

@inproceedings{yasunaga-etal-2022-linkbert,
    title = "{L}ink{BERT}: Pretraining Language Models with Document Links",
    author = "Yasunaga, Michihiro  and
      Leskovec, Jure  and
      Liang, Percy",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.551",
    doi = "10.18653/v1/2022.acl-long.551",
    pages = "8003--8016",
    abstract = "Language model (LM) pretraining captures various knowledge from text corpora, helping downstream tasks. However, existing methods such as BERT model a single document, and do not capture dependencies or knowledge that span across documents. In this work, we propose LinkBERT, an LM pretraining method that leverages links between documents, e.g., hyperlinks. Given a text corpus, we view it as a graph of documents and create LM inputs by placing linked documents in the same context. We then pretrain the LM with two joint self-supervised objectives: masked language modeling and our new proposal, document relation prediction. We show that LinkBERT outperforms BERT on various downstream tasks across two domains: the general domain (pretrained on Wikipedia with hyperlinks) and biomedical domain (pretrained on PubMed with citation links). LinkBERT is especially effective for multi-hop reasoning and few-shot QA (+5{\%} absolute improvement on HotpotQA and TriviaQA), and our biomedical LinkBERT sets new states of the art on various BioNLP tasks (+7{\%} on BioASQ and USMLE). We release our pretrained models, LinkBERT and BioLinkBERT, as well as code and data.",
}

@misc{phan2021scifive,
      title={SciFive: a text-to-text transformer model for biomedical literature}, 
      author={Long N. Phan and James T. Anibal and Hieu Tran and Shaurya Chanana and Erol Bahadroglu and Alec Peltekian and Grégoire Altan-Bonnet},
      year={2021},
      eprint={2106.03598},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inbook{Nentidis_2020,
   title={Results of the Seventh Edition of the BioASQ Challenge},
   ISBN={9783030438876},
   ISSN={1865-0937},
   url={http://dx.doi.org/10.1007/978-3-030-43887-6_51},
   DOI={10.1007/978-3-030-43887-6_51},
   booktitle={Communications in Computer and Information Science},
   publisher={Springer International Publishing},
   author={Nentidis, Anastasios and Bougiatiotis, Konstantinos and Krithara, Anastasia and Paliouras, Georgios},
   year={2020},
   pages={553–568} }

# GNN for NLI
@Article{math9161949,
AUTHOR = {Chen, Chonghao and Zheng, Jianming and Chen, Honghui},
TITLE = {Knowledge-Enhanced Graph Attention Network for Fact Verification},
JOURNAL = {Mathematics},
VOLUME = {9},
YEAR = {2021},
NUMBER = {16},
ARTICLE-NUMBER = {1949},
URL = {https://www.mdpi.com/2227-7390/9/16/1949},
ISSN = {2227-7390},
ABSTRACT = {Fact verification aims to evaluate the authenticity of a given claim based on the evidence sentences retrieved from Wikipedia articles. Existing works mainly leverage the natural language inference methods to model the semantic interaction of claim and evidence, or further employ the graph structure to capture the relation features between multiple evidences. However, previous methods have limited representation ability in encoding complicated units of claim and evidences, and thus cannot support sophisticated reasoning. In addition, a limited amount of supervisory signals lead to the graph encoder could not distinguish the distinctions of different graph structures and weaken the encoding ability. To address the above issues, we propose a Knowledge-Enhanced Graph Attention network (KEGA) for fact verification, which introduces a knowledge integration module to enhance the representation of claims and evidences by incorporating external knowledge. Moreover, KEGA leverages an auxiliary loss based on contrastive learning to fine-tune the graph attention encoder and learn the discriminative features for the evidence graph. Comprehensive experiments conducted on FEVER, a large-scale benchmark dataset for fact verification, demonstrate the superiority of our proposal in both the multi-evidences and single-evidence scenarios. In addition, our findings show that the background knowledge for words can effectively improve the model performance.},
DOI = {10.3390/math9161949}
}


@inproceedings{chen-etal-2018-neural-natural,
    title = "Neural Natural Language Inference Models Enhanced with External Knowledge",
    author = "Chen, Qian  and
      Zhu, Xiaodan  and
      Ling, Zhen-Hua  and
      Inkpen, Diana  and
      Wei, Si",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1224",
    doi = "10.18653/v1/P18-1224",
    pages = "2406--2417",
    abstract = "Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.",
}

@article{song_kganet_2020,
author = {Song, Meina and Zhao, Wen and HaiHong, E.},
title = {KGAnet: a knowledge graph attention network for enhancing natural language inference},
year = {2020},
issue_date = {Sep 2020},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
volume = {32},
number = {18},
issn = {0941-0643},
url = {https://doi.org/10.1007/s00521-020-04851-5},
doi = {10.1007/s00521-020-04851-5},
abstract = {Natural language inference (NLI) is the basic task of many applications such as question answering and paraphrase recognition. Existing methods have solved the key issue of how the NLI model can benefit from external knowledge. Inspired by this, we attempt to further explore the following two problems: (1) how to make better use of external knowledge when the total amount of such knowledge is constant and (2) how to bring external knowledge to the NLI model more conveniently in the application scenario. In this paper, we propose a novel joint training framework that consists of a modified graph attention network, called the knowledge graph attention network, and an NLI model. We demonstrate that the proposed method outperforms the existing method which introduces external knowledge, and we improve the performance of multiple NLI models without additional external knowledge.},
journal = {Neural Comput. Appl.},
month = {sep},
pages = {14963–14973},
numpages = {11},
keywords = {External knowledge, Natural language inference, Natural language processing}
}

@inproceedings{10.1145/3357384.3358071,
author = {Chen, Daoyuan and Li, Yaliang and Yang, Min and Zheng, Hai-Tao and Shen, Ying},
title = {Knowledge-aware Textual Entailment with Graph Attention Network},
year = {2019},
isbn = {9781450369763},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3357384.3358071},
doi = {10.1145/3357384.3358071},
abstract = {Textual entailment is a central problem of language variability, which has been attracting a lot of interest and it poses significant issues in front of systems aimed at natural language understanding. Recently, various frameworks have been proposed for textual entailment recognition, ranging from traditional computational linguistics techniques to deep learning model based methods. However, recent deep neural networks that achieve the state of the art on textual entailment task only consider the context information of the given sentences rather than the real-world background information and knowledge beyond the context. In the paper, we propose a Knowledge-Context Interactive Textual Entailment Network (KCI-TEN) that learns graph level sentence representations by harnessing external knowledge graph with graph attention network. We further propose a text-graph interaction mechanism for neural based entailment matching learning, which endows the redundancy and noise with less importance and put emphasis on the informative representations. Experiments on the SciTail dataset demonstrate that KCI-TEN outperforms the state-of-the-art methods.},
booktitle = {Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
pages = {2145–2148},
numpages = {4},
keywords = {graph attention network, knowledge base, textual entailment},
location = {Beijing, China},
series = {CIKM '19}
}

@misc{wu2022graph,
      title={Graph Neural Networks for Natural Language Processing: A Survey}, 
      author={Lingfei Wu and Yu Chen and Kai Shen and Xiaojie Guo and Hanning Gao and Shucheng Li and Jian Pei and Bo Long},
      year={2022},
      eprint={2106.06090},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{chakraborty-2023-rgat,
    title = "{RGAT} at {S}em{E}val-2023 Task 2: Named Entity Recognition Using Graph Attention Network",
    author = "Chakraborty, Abir",
    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-1.23",
    doi = "10.18653/v1/2023.semeval-1.23",
    pages = "163--170",
    abstract = "In this paper, we (team RGAT) describe our approach for the SemEval 2023 Task 2: Multilingual Complex Named Entity Recognition (MultiCoNER II). The goal of this task is to locate and classify named entities in unstructured short complex texts in 12 different languages and one multilingual setup. We use the dependency tree of the input query as additional feature in a Graph Attention Network along with the token and part-of-speech features. We also experiment with additional layers like BiLSTM and Transformer in addition to the CRF layer. However, we have not included any external Knowledge base like Wikipedia to enrich our inputs. We evaluated our proposed approach on the English NER dataset that resulted in a clean-subset F1 of 61.29{\textbackslash}{\%} and overall F1 of 56.91{\textbackslash}{\%}. However, other approaches that used external knowledge base performed significantly better.",
}

@inproceedings{wang-etal-2020-relational,
    title = "Relational Graph Attention Network for Aspect-based Sentiment Analysis",
    author = "Wang, Kai  and
      Shen, Weizhou  and
      Yang, Yunyi  and
      Quan, Xiaojun  and
      Wang, Rui",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.295",
    doi = "10.18653/v1/2020.acl-main.295",
    pages = "3229--3238",
    abstract = "Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews. Most recent efforts adopt attention-based neural network models to implicitly connect aspects with opinion words. However, due to the complexity of language and the existence of multiple aspects in a single sentence, these models often confuse the connections. In this paper, we address this problem by means of effective encoding of syntax information. Firstly, we define a unified aspect-oriented dependency tree structure rooted at a target aspect by reshaping and pruning an ordinary dependency parse tree. Then, we propose a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction. Extensive experiments are conducted on the SemEval 2014 and Twitter datasets, and the experimental results confirm that the connections between aspects and opinion words can be better established with our approach, and the performance of the graph attention network (GAT) is significantly improved as a consequence.",
}

@misc{dozat-manning-2016,
     author={Timothy Dozat and Christopher D Manning},
     year={2016},
     title={Deep biaffine attention for neural dependency parsing},
     archivePrefix={arXiv},
     primaryClass={cs.CL},
     eprint={1611.01734},
}

@misc{kingma-adam-2014,
  doi = {10.48550/ARXIV.1412.6980},
  url = {https://arxiv.org/abs/1412.6980},
  author = {Kingma, Diederik P. and Ba, Jimmy},
  keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Adam: A Method for Stochastic Optimization},
  publisher = {arXiv},
  year = {2014},
  copyright = {arXiv.org perpetual, non-exclusive license}
}