{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T15:15:40Z","timestamp":1730214940923,"version":"3.28.0"},"reference-count":44,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T00:00:00Z","timestamp":1696809600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T00:00:00Z","timestamp":1696809600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,9]]},"DOI":"10.1109\/dsaa60987.2023.10302466","type":"proceedings-article","created":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T19:07:21Z","timestamp":1699297641000},"page":"1-9","source":"Crossref","is-referenced-by-count":0,"title":["Learning Representations through Contrastive Strategies for a more Robust Stance Detection"],"prefix":"10.1109","author":[{"given":"Udhaya Kumar","family":"Rajendran","sequence":"first","affiliation":[{"name":"Lakehead University,Department of Computer Science,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir Ben","family":"Khalifa","sequence":"additional","affiliation":[{"name":"Université de La Manouba,École Nationale des Sciences de l’Informatique,Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amine","family":"Trabelsi","sequence":"additional","affiliation":[{"name":"Université de Sherbrooke,Department of Computer Science,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s13218-021-00714-w"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3110025.3110112"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-emnlp.253"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.290"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-005-0466-3"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/e17-2088"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3359307"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v15i1.18082"},{"key":"ref9","first-page":"4356","article-title":"How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?","volume":"34","author":"Dong","year":"2021","journal-title":"Advances In Neural Information Processing Systems"},{"key":"ref10","first-page":"3588","article-title":"Improving the Adversarial Robustness of NLP Models by Information Bottleneck","author":"Zhang","year":"2022","journal-title":"Findings Of The Association For Computational Linguistics: ACL 2022"},{"journal-title":"(arXiv,2020)","article-title":"InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective","author":"Wang","key":"ref11"},{"key":"ref12","first-page":"1558","article-title":"Evaluating the Robustness of Neural Language Models to Input Perturbations","volume-title":"Proceedings Of The 2021 Conference On Empirical Methods In Natural Language Processing","author":"Moradi"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.754"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.544"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.294"},{"key":"ref16","article-title":"Constructing contrastive samples via summarization for text classification with limited annotations","author":"Du","year":"2021","journal-title":"Findings Of The Association For Computational Linguistics: EMNLP 2021"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.532"},{"journal-title":"(arXiv,2022)","article-title":"Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings","author":"Ostendorff","key":"ref18"},{"key":"ref19","first-page":"2944","article-title":"Evaluating adversarial attacks against multiple fact verification systems","volume-title":"Proceedings Of The 2019 Conference On Empirical Methods In Natural Language Processing And The 9th International Joint Conference On Natural Language Processing (EMNLP-IJCNLP)","author":"Thorne"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.21236\/ada006655"},{"journal-title":"(arXiv,2020)","article-title":"DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations","author":"Giorgi","key":"ref21"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1145\/3477495.3532072","article-title":"Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders","volume-title":"Proceedings Of The 45th International ACM SIGIR Conference On Research And Development In Information Retrieval","author":"Li"},{"key":"ref23","first-page":"1348","article-title":"Stance classification of ideological debates: Data, models, features, and constraints","volume-title":"Proceedings Of The Sixth International Joint Conference On Natural Language Processing","author":"Hasan"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S16-1003"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1175"},{"key":"ref26","article-title":"Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims","author":"Chen","year":"2019","journal-title":"arXiv"},{"journal-title":"Fake News Challenge","article-title":"Exploring how artificial intelligence technologies could be leveraged to combat fake news","author":"Pomerleau","key":"ref27"},{"key":"ref28","first-page":"4725","article-title":"Knowledge Enhanced Masked Language Model for Stance Detection","volume-title":"Proceedings Of The 2021 Conference Of The North American Chapter Of The Association For Computational Linguistics: Human Language Technologies","author":"Kawintiranon"},{"key":"ref29","article-title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","author":"Sanh","year":"2019","journal-title":"ArXiv. abs\/1910.01108"},{"volume-title":"It\u2019s what\u2019s happening","key":"ref30"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"101075","DOI":"10.1016\/j.csl.2020.101075","article-title":"Multilingual stance detection in social media political debates","volume":"63","author":"Lai","year":"2020","journal-title":"Computer Speech & Language"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511994"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210145"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.710"},{"key":"ref35","first-page":"312","article-title":"(Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys","volume-title":"Proceedings Of The 2021 Conference On Empirical Methods In Natural Language Processing","author":"Joseph"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210145"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.511"},{"volume-title":"Alleviating Exposure Bias via Contrastive Learning for Abstractive Text Summarization","year":"2021","author":"Sun","key":"ref38"},{"volume-title":"CLEAR: Contrastive Learning for Sentence Representation","year":"2020","author":"Wu","key":"ref39"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.450"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.547"},{"article-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Delvin","key":"ref42"},{"volume-title":"Language Models are Few-Shot Learners","year":"2020","author":"Brown","key":"ref43"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1037\/h0031619"}],"event":{"name":"2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)","start":{"date-parts":[[2023,10,9]]},"location":"Thessaloniki, Greece","end":{"date-parts":[[2023,10,13]]}},"container-title":["2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10302431\/10302461\/10302466.pdf?arnumber=10302466","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T17:32:52Z","timestamp":1718299972000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10302466\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,9]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/dsaa60987.2023.10302466","relation":{},"subject":[],"published":{"date-parts":[[2023,10,9]]}}}