{"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/fine-tuning-tree-lstm-for-phrase-level","title":"Fine-tuning Tree-LSTM for phrase-level sentiment classification on a Polish dependency treebank. Submission to PolEval task 2","arxiv_id":"1711.01985","date":"2017-11-03","proceeding":null,"authors":["Tomasz Korbak","Paulina Żak"],"abstract":"We describe a variant of Child-Sum Tree-LSTM deep neural network (Tai et al,\n2015) fine-tuned for working with dependency trees and morphologically rich\nlanguages using the example of Polish. Fine-tuning included applying a custom\nregularization technique (zoneout, described by (Krueger et al., 2016), and\nfurther adapted for Tree-LSTMs) as well as using pre-trained word embeddings\nenhanced with sub-word information (Bojanowski et al., 2016). The system was\nimplemented in PyTorch and evaluated on phrase-level sentiment labeling task as\npart of the PolEval competition.","url_abs":"http://arxiv.org/abs/1711.01985v1","url_pdf":"http://arxiv.org/pdf/1711.01985v1.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":"fine-tuning-tree-lstm-for-phrase-level","repo_url":"https://github.com/tomekkorbak/treehopper","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"},{"task_slug":"task-2","task_name":"Task 2"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}