{"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/huji-ku-at-mrp-2020-two-transition-based","title":"HUJI-KU at MRP~2020: Two Transition-based Neural Parsers","arxiv_id":"2010.05710","date":"2020-10-12","proceeding":null,"authors":["Ofir Arviv","Ruixiang Cui","Daniel Hershcovich"],"abstract":"This paper describes the HUJI-KU system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2020 Conference for Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR parser, which were, respectively, the baseline system and winning system in the 2019 MRP shared task. Both are transition-based parsers using BERT contextualized embeddings. We generalized TUPA to support the newly-added MRP frameworks and languages, and experimented with multitask learning with the HIT-SCIR parser. We reached 4th place in both the cross-framework and cross-lingual tracks.","url_abs":"https://arxiv.org/abs/2010.05710v1","url_pdf":"https://arxiv.org/pdf/2010.05710v1.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":"semantic-parsing","task_name":"Semantic Parsing"},{"task_slug":"two","task_name":"Vocal Bursts Valence Prediction"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-parsing-on-amr-chinese-mrp-2020","task":"Semantic Parsing","dataset":"AMR (chinese, MRP 2020)","model":"HUJI-KU","rank_in_archive_order":2,"of":2,"metrics":{"F1":"45"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-parsing-on-amr-english-mrp-2020","task":"Semantic Parsing","dataset":"AMR (english, MRP 2020)","model":"HUJI-KU","rank_in_archive_order":2,"of":2,"metrics":{"F1":"52"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-parsing-on-drg-english-mrp-2020","task":"Semantic Parsing","dataset":"DRG (english, MRP 2020)","model":"HUJI-KU","rank_in_archive_order":2,"of":2,"metrics":{"F1":"63"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-parsing-on-drg-german-mrp-2020","task":"Semantic Parsing","dataset":"DRG (german, MRP 2020)","model":"HUJI-KU","rank_in_archive_order":2,"of":2,"metrics":{"F1":"62"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-parsing-on-eds-english-mrp-2020","task":"Semantic Parsing","dataset":"EDS (english, MRP 2020)","model":"HUJI-KU","rank_in_archive_order":2,"of":2,"metrics":{"F1":"80"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-parsing-on-ptg-czech-mrp-2020","task":"Semantic Parsing","dataset":"PTG (czech, MRP 2020)","model":"HUJI-KU","rank_in_archive_order":3,"of":3,"metrics":{"F1":"58"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-parsing-on-ptg-english-mrp-2020","task":"Semantic Parsing","dataset":"PTG (english, MRP 2020)","model":"HUJI-KU","rank_in_archive_order":2,"of":2,"metrics":{"F1":"54"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-parsing-on-ucca-english-mrp-2020","task":"Semantic Parsing","dataset":"UCCA (english, MRP 2020)","model":"HUJI-KU","rank_in_archive_order":2,"of":2,"metrics":{"F1":"73"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-parsing-on-ucca-german-mrp-2020","task":"Semantic Parsing","dataset":"UCCA (german, MRP 2020)","model":"HUJI-KU","rank_in_archive_order":2,"of":2,"metrics":{"F1":"75"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}