{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ctlr-wic-tsv-target-sense-verification-using","title":"CTLR@WiC-TSV: Target Sense Verification using Marked Inputs andPre-trained Models","arxiv_id":null,"date":"2021-04-30","proceeding":"SemDeep 2021 1","authors":["José G. Moreno","Elvys Linhares Pontes","Gaël Dias"],"abstract":"This paper describes the CTRL participation in the Target Sense Verification of the Words in Context challenge (WiC-TSV) at SemDeep6. Our strategy is based on a simplistic annotation scheme of the target words to later be classified by well-known pre-trained neural models. In particular, the marker allows to include position information to help models to correctly identify the word to disambiguate. Results on the challenge show that our strategy outperforms other participants (+11, 4 Accuracy points) and strong baselines (+1, 7 Accuracy points).","url_abs":"https://www.semanticscholar.org/paper/CTLR%40WiC-TSV%3A-Target-Sense-Verification-using-Moreno-Pontes/4c0478f5f01eec08ab2c92ddeb57cbb6fd26f0a7#paper-header","url_pdf":"https://www.aclweb.org/anthology/2021.semdeep-1.1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":null,"task_name":"Position"}],"methods":[{"method_slug":"adagrad","method_name":"AdaGrad"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"ctrl","method_name":"CTRL"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"gradient-clipping","method_name":"Gradient Clipping"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup","method_name":"Linear Warmup"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/entity-linking-on-wic-tsv","task":"Entity Linking","dataset":"WiC-TSV","model":"CTLR","rank_in_archive_order":2,"of":8,"metrics":{"Task 1 Accuracy: all":"76.8","Task 1 Accuracy: domain specific":"79.6","Task 1 Accuracy: general purpose":"74.5","Task 2 Accuracy: all":"72.7","Task 2 Accuracy: domain specific":"81.5","Task 2 Accuracy: general purpose":"65.6","Task 3 Accuracy: all":"78.3","Task 3 Accuracy: domain specific":"85.7","Task 3 Accuracy: general purpose":"72.1"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}