{"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/unsupervised-transfer-learning-for-spoken","title":"Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents","arxiv_id":"1811.05370","date":"2018-11-13","proceeding":null,"authors":["Aditya Siddhant","Anuj Goyal","Angeliki Metallinou"],"abstract":"User interaction with voice-powered agents generates large amounts of\nunlabeled utterances. In this paper, we explore techniques to efficiently\ntransfer the knowledge from these unlabeled utterances to improve model\nperformance on Spoken Language Understanding (SLU) tasks. We use Embeddings\nfrom Language Model (ELMo) to take advantage of unlabeled data by learning\ncontextualized word representations. Additionally, we propose ELMo-Light\n(ELMoL), a faster and simpler unsupervised pre-training method for SLU. Our\nfindings suggest unsupervised pre-training on a large corpora of unlabeled\nutterances leads to significantly better SLU performance compared to training\nfrom scratch and it can even outperform conventional supervised transfer.\nAdditionally, we show that the gains from unsupervised transfer techniques can\nbe further improved by supervised transfer. The improvements are more\npronounced in low resource settings and when using only 1000 labeled in-domain\nsamples, our techniques match the performance of training from scratch on\n10-15x more labeled in-domain data.","url_abs":"http://arxiv.org/abs/1811.05370v1","url_pdf":"http://arxiv.org/pdf/1811.05370v1.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":"unsupervised-transfer-learning-for-spoken","repo_url":"https://github.com/sxjscience/GluonNLP-Slot-Filling","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":{"status":"ok"}}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"spoken-language-understanding","task_name":"Spoken Language Understanding"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"unsupervised-pre-training","task_name":"Unsupervised Pre-training"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.05370","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}