{"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/tree-stack-lstm-in-transition-based","title":"Tree-Stack LSTM in Transition Based Dependency Parsing","arxiv_id":null,"date":"2018-10-01","proceeding":"CONLL 2018 10","authors":["{\\\"O}mer K{\\i}rnap","Erenay Dayan{\\i}k","Deniz Yuret"],"abstract":"We introduce tree-stack LSTM to model state of a transition based parser with recurrent neural networks. Tree-stack LSTM does not use any parse tree based or hand-crafted features, yet performs better than models with these features. We also develop new set of embeddings from raw features to enhance the performance. There are 4 main components of this model: stack{'}s σ-LSTM, buffer{'}s β-LSTM, actions{'} LSTM and tree-RNN. All LSTMs use continuous dense feature vectors (embeddings) as an input. Tree-RNN updates these embeddings based on transitions. We show that our model improves performance with low resource languages compared with its predecessors. We participate in CoNLL 2018 UD Shared Task as the {``}KParse{''} team and ranked 16th in LAS, 15th in BLAS and BLEX metrics, of 27 participants parsing 82 test sets from 57 languages.","url_abs":"https://aclanthology.org/K18-2012","url_pdf":"https://aclanthology.org/K18-2012.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":"tree-stack-lstm-in-transition-based","repo_url":"https://github.com/kirnap/ku-dependency-parser2","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"dependency-parsing","task_name":"Dependency Parsing"},{"task_slug":"lemmatization","task_name":"Lemmatization"},{"task_slug":"morphological-analysis","task_name":"Morphological Analysis"},{"task_slug":"morphological-tagging","task_name":"Morphological Tagging"},{"task_slug":"transition-based-dependency-parsing","task_name":"Transition-Based Dependency Parsing"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}