{"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/adapting-sequence-to-sequence-models-for-text","title":"Adapting Sequence to Sequence models for Text Normalization in Social Media","arxiv_id":"1904.06100","date":"2019-04-12","proceeding":null,"authors":["Ismini Lourentzou","Kabir Manghnani","ChengXiang Zhai"],"abstract":"Social media offer an abundant source of valuable raw data, however informal\nwriting can quickly become a bottleneck for many natural language processing\n(NLP) tasks. Off-the-shelf tools are usually trained on formal text and cannot\nexplicitly handle noise found in short online posts. Moreover, the variety of\nfrequently occurring linguistic variations presents several challenges, even\nfor humans who might not be able to comprehend the meaning of such posts,\nespecially when they contain slang and abbreviations. Text Normalization aims\nto transform online user-generated text to a canonical form. Current text\nnormalization systems rely on string or phonetic similarity and classification\nmodels that work on a local fashion. We argue that processing contextual\ninformation is crucial for this task and introduce a social media text\nnormalization hybrid word-character attention-based encoder-decoder model that\ncan serve as a pre-processing step for NLP applications to adapt to noisy text\nin social media. Our character-based component is trained on synthetic\nadversarial examples that are designed to capture errors commonly found in\nonline user-generated text. Experiments show that our model surpasses neural\narchitectures designed for text normalization and achieves comparable\nperformance with state-of-the-art related work.","url_abs":"http://arxiv.org/abs/1904.06100v1","url_pdf":"http://arxiv.org/pdf/1904.06100v1.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":[{"paper_slug":"adapting-sequence-to-sequence-models-for-text","repo_url":"https://github.com/Isminoula/TextNormSeq2Seq","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"lexical-normalization","task_name":"Lexical Normalization"},{"task_slug":"text-normalization","task_name":"Text Normalization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lexical-normalization-on-lexnorm","task":"Lexical Normalization","dataset":"LexNorm","model":"TextNorm","rank_in_archive_order":3,"of":4,"metrics":{"Accuracy":"83.94"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}