{ "paper_id": "S19-2013", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T15:47:54.324301Z" }, "title": "DANGNT@UIT.VNU-HCM at SemEval 2019 Task 1: Graph Transformation System from Stanford Basic Dependencies to Universal Conceptual Cognitive Annotation (UCCA)", "authors": [ { "first": "Dang", "middle": [ "Tuan" ], "last": "Nguyen", "suffix": "", "affiliation": { "laboratory": "", "institution": "VNU-HCM Ho Chi Minh City", "location": { "country": "Vietnam" } }, "email": "dangnt@uit.edu.vn" }, { "first": "Trung", "middle": [], "last": "Tran", "suffix": "", "affiliation": { "laboratory": "", "institution": "VNU-HCM Ho Chi Minh City", "location": { "country": "Vietnam" } }, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "This paper describes the graph transformation system (GT System) for SemEval 2019 Task 1: Cross-lingual Semantic Parsing with Universal Conceptual Cognitive Annotation (UCCA) 1. The input of GT System is a pair of text and its unannotated xml, which is a layer 0 part of UCCA form. The output of GT System is the corresponding full UCCA xml. Based on the idea of graph illustration and transformation, we perform four main tasks when building GT System. At the first task, we illustrate the graph form of stanford dependencies 2 of input text. We then transform into an intermediate graph in the second task. At the third task, we continue to transform into ouput graph form. Finally, we create the output UCCA xml. The evaluation results show that our method generates good-quality UCCA xml and has a meaningful contribution to the semantic representation sub-field in Natural Language Processing.", "pdf_parse": { "paper_id": "S19-2013", "_pdf_hash": "", "abstract": [ { "text": "This paper describes the graph transformation system (GT System) for SemEval 2019 Task 1: Cross-lingual Semantic Parsing with Universal Conceptual Cognitive Annotation (UCCA) 1. The input of GT System is a pair of text and its unannotated xml, which is a layer 0 part of UCCA form. The output of GT System is the corresponding full UCCA xml. Based on the idea of graph illustration and transformation, we perform four main tasks when building GT System. At the first task, we illustrate the graph form of stanford dependencies 2 of input text. We then transform into an intermediate graph in the second task. At the third task, we continue to transform into ouput graph form. Finally, we create the output UCCA xml. The evaluation results show that our method generates good-quality UCCA xml and has a meaningful contribution to the semantic representation sub-field in Natural Language Processing.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "In the past few years, semantic representation is receiving growing attention in NLP. Researchers have recently proposed different semantic schemes. Examples include Abstract Meaning Representation (Banarescu et al. 2013) , Broadcoverage Semantic Dependencies (Oepen et al. 2014) , Universal Decompositional Semantics (White et al. 2016) , Parallel Meaning Bank (Abzianidze et al. 2016) , Universal Conceptual Cognitive Annotation (Abend and Rappoport 2013) . These advances in semantic representation, along with corresponding advances in semantic parsing, text understanding, summarization, paraphrase detection, and semantic evaluation.", "cite_spans": [ { "start": 198, "end": 221, "text": "(Banarescu et al. 2013)", "ref_id": "BIBREF4" }, { "start": 260, "end": 279, "text": "(Oepen et al. 2014)", "ref_id": "BIBREF14" }, { "start": 318, "end": 337, "text": "(White et al. 2016)", "ref_id": "BIBREF0" }, { "start": 362, "end": 386, "text": "(Abzianidze et al. 2016)", "ref_id": null }, { "start": 431, "end": 457, "text": "(Abend and Rappoport 2013)", "ref_id": "BIBREF10" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "In SemEval 2019 Task 1: Cross-lingual Semantic Parsing with Universal Conceptual Cogni-tive Annotation (UCCA) 1 , the Committee focuses on parsing text according to the UCCA semantic annotation. UCCA (Abend and Rappoport 2013) is a cross-linguistically applicable semantic representation scheme, based on Basic Linguistic Theory (Dixon 2010). In general, UCCA represents the semantics of linguistic utterances as directed acyclic graphs (DAGs). In one DAG, nodes and edges belong to one of several layers. There are two types of node: (i) terminal nodes express the text tokens; (ii) non-terminal nodes express semantic units. Edges are labelled, indicating the role of a child in the relation the parent represents. As an example, consider sentence in Example 1: \"The album was recorded in Switzerland .\". Two layers of UCCA xml of this sentence:", "cite_spans": [ { "start": 200, "end": 226, "text": "(Abend and Rappoport 2013)", "ref_id": "BIBREF10" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "\u2022 Layer0:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": " ...", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "The relations of NodeID and corresponding lexicon: {[ID=\"0.1\" \u00e8 dep=\"det\" \u00e8 \"The\"] [ID=\"0.2\" \u00e8 dep=\"nsubj:pass\" \u00e8 \"album\"] [ID=\"0.3\" \u00e8 dep=\"aux:pass\" \u00e8 \"was\"] [ID=\"0.4\" \u00e8 dep=\"root\" \u00e8 \"recorded\"] [ID=\"0.5\" \u00e8 dep=\"case\" \u00e8 \"in\"] [ID=\"0.6\" \u00e8 dep=\"obl\" \u00e8 \"Switzerland\"] [ID=\"0.7\" \u00e8 dep=\"punct\" \u00e8 \".\"]}", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": " ", "sec_num": null }, { "text": "\u2022 Layer1: ... ... ...", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": " ", "sec_num": null }, { "text": "We have the graphical representation of the above UCCA: The primary purpose of this article is to present our system called graph transformation system (GT System) for Task 1 . We perform four tasks when building GT System. At the first task, we illustrate the graph form of Stanford dependencies 2 (Manning et al. 2014; Marie-Catherine et al. 2014) of input text. We then transform into an intermediate graph in the second task. At the third task, we continue to transform into ouput graph form. Finally, we create the output UCCA xml. The rest of article is separated as follows. We briefly describe Stanford dependencies in Section analyzes the results. We offer conclusions in Section 5.", "cite_spans": [ { "start": 299, "end": 320, "text": "(Manning et al. 2014;", "ref_id": "BIBREF6" }, { "start": 321, "end": 349, "text": "Marie-Catherine et al. 2014)", "ref_id": "BIBREF7" } ], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "Stanford dependencies 2 (Manning et al. 2014 ; Marie-Catherine et al. 2014; Marie-Catherine and Manning 2008) provides a representation of grammatical relations between words in a sentence. Stanford dependencies (SD) have three parts: name of the relation, governor and dependent. Consider English sentence in Example 1, below is the xml representation of SD basic dependencies. This representation is the result of running Stanford CoreNLP pipeline (Manning et al. 2014 ).", "cite_spans": [ { "start": 24, "end": 44, "text": "(Manning et al. 2014", "ref_id": "BIBREF6" }, { "start": 450, "end": 470, "text": "(Manning et al. 2014", "ref_id": "BIBREF6" } ], "ref_spans": [], "eq_spans": [], "section": "Stanford Dependencies", "sec_num": "2" }, { "text": " ROOT recorded album The recorded album recorded was Switzerland in recorded Switzerland ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Stanford Dependencies", "sec_num": "2" }, { "text": "We have the graphical representation of the above SD basic dependencies: ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Stanford Dependencies", "sec_num": "2" }, { "text": "In this section, we express our GT system for creating UCCA xml of the input text. The general architecture is represented in Figure 2 :", "cite_spans": [], "ref_spans": [ { "start": 126, "end": 134, "text": "Figure 2", "ref_id": "FIGREF1" } ], "eq_spans": [], "section": "The Graph Transformation System", "sec_num": "3" }, { "text": "Figure 3: Architecture of Graph Transformation Sys- tem.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Graph Transformation System", "sec_num": "3" }, { "text": "When building GT System, we perform two processes: training and testing process. At training process, we build the intermediate graph from UCCA and SD basic dependencies of training data 1 . At the testing process, which can be called the inverse process of training, we build the ouput UCCA from intermediate graph of testing data.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Graph Transformation System", "sec_num": "3" }, { "text": "In Figure 1 , and quite similar to graph in Figure 2 . ", "cite_spans": [], "ref_spans": [ { "start": 3, "end": 11, "text": "Figure 1", "ref_id": "FIGREF0" }, { "start": 44, "end": 52, "text": "Figure 2", "ref_id": "FIGREF1" } ], "eq_spans": [], "section": "Intermediate Graph", "sec_num": "3.1" }, { "text": "Firstly, at training process, we consider train data 1 and performed main tasks. The first and second task is in turn viewing the graph from of SD basic dependencies and UCCA of input text. At the third task, we propose Left-First-Search liked algorithm with Bottom-Up idea to reduce the graph form of UCCA to intermediate graph. At the final task, we propose rules and heuristics for matching graph form of SD basic dependencies and intermediate graph.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Training Process", "sec_num": "3.2" }, { "text": "The main steps of Left-First-Search (LFS) algorithm is as follow.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Training Process", "sec_num": "3.2" }, { "text": "Step 1. Browse to terminal on the left.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Training Process", "sec_num": "3.2" }, { "text": "Step 2. Back to parent node of this terminal. Check if parent having any other child or not.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Training Process", "sec_num": "3.2" }, { "text": "Step 2.1. If yes. Repeat Step 1 with root is this child node.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Training Process", "sec_num": "3.2" }, { "text": "Step 3. Swap the position of root of sub-tree with position of child having important annotation.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Training Process", "sec_num": "3.2" }, { "text": "Step 4. Back to parent node of this root. Repeat Step 2 with this parent.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Training Process", "sec_num": "3.2" }, { "text": "To perform LFS algorithm, we determine the priority of SD and UCCA annotations according to two factors. First. The meaning of each annotation, representing the dependency relations and grammatical roles of lexicons. Second. The position of each node in graph.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Training Process", "sec_num": "3.2" }, { "text": "Apply LFS algorithm for graph in Figure 3 , we in turn have three level reductions in Figure 5 , 6, 4 (respectively): After having the final reduction, which is intermediate graph, of graph form of UCCA, we compare with graph form of SD basic dependencies. We consider the similarities between two graphs and propose rules and heuristics to (i) determine the level of one node, and (ii) determine the group of UCCA annotation for each level. The general idea of mechanism is: \u2022 Collect all SD-type of relations in UCCA and SD basic dependencies of training data. Below is the collection:", "cite_spans": [], "ref_spans": [ { "start": 33, "end": 41, "text": "Figure 3", "ref_id": null }, { "start": 86, "end": 94, "text": "Figure 5", "ref_id": "FIGREF3" } ], "eq_spans": [], "section": "Training Process", "sec_num": "3.2" }, { "text": "SD basic dependencies acl:relcl / expl / csubjpass / cop / aux / conj / acl / xcomp / dep / appos / advmod / neg / det / cc:preconj / nmod:tmod / ccomp / root / advcl / nsubj / case / iobj / cc / det:predet / nmod:poss / compound:prt / csubj / nsubjpass / nummod / nmod:npmod / nmod / auxpass / parataxis / amod / compound / discourse / mwe / dobj / mark UCCA acl:relcl / expl / obl:npmod / cop / aux / conj / acl / appos / xcomp / goeswith / advmod / det / ccomp / nsubj:pass / cc:preconj / nmod:tmod / flat / root / obl:tmod / advcl / punct / nsubj / case / iobj / cc / vocative / det:predet / nmod:poss / compound:prt / csubj / nummod / nmod:npmod / nmod / parataxis / amod / list / compound / discourse / aux:pass / obj / obl / fixed / mark", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Training Process", "sec_num": "3.2" }, { "text": "\u2022 Determine the priority order of SD-type relations. Example 2: dobj -> amod -> dep -> nmod -> case.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Training Process", "sec_num": "3.2" }, { "text": "\u2022 Determine the compound (UCCA and SD) relation in each node level. Example 3: type conj at level 7:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Training Process", "sec_num": "3.2" }, { "text": "\"H -A -E -C -C -C - conj\"", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Training Process", "sec_num": "3.2" }, { "text": "At the testing process, which can be called the inverse process of training, we considered development and test data 1 and performed main tasks. The first task is viewing the graph from of SD basic dependencies of input text. At the second task, we applied proposed rules and heuristics to transform this graph to intermediate graph. We then, at the final task, we proposed Breadth-First-Search liked algorithm with Top-Down idea to re-create the graph form of UCCA from intermediate graph. This BFS algorithm is, in fact, the inverse mechanism of LFS algorithm in Section 3.2.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Testing Process", "sec_num": "3.3" }, { "text": "The main steps of Breadth-First-Search (BFS) algorithm is as follow.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Testing Process", "sec_num": "3.3" }, { "text": "Step 1. Reduce the first level of node.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Testing Process", "sec_num": "3.3" }, { "text": "Step 2. Determine the intergrated-Child which adheres to this node.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Testing Process", "sec_num": "3.3" }, { "text": "Step 3. If there is no intergratedChild.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Testing Process", "sec_num": "3.3" }, { "text": "Step 3.1. Repeat Step 1 until node come down to terminal position. Step 3.2. Repeat from Step 1 to Step 4 with each child of this node.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Testing Process", "sec_num": "3.3" }, { "text": "Step 4. If there is intergratedChild.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Testing Process", "sec_num": "3.3" }, { "text": "Step 4.1. Repeat from Step 1 to Step 4 with each child of this node which are different from intergrated-Child.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Testing Process", "sec_num": "3.3" }, { "text": "Step 4.2. Repeat from Step 1 to Step 4 with this node.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Testing Process", "sec_num": "3.3" }, { "text": "Step 4.3. Repeat from Step 1 to Step 4 with intergratedChild.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Testing Process", "sec_num": "3.3" }, { "text": "At the evaluation phase, we focus on English indomain setting, using the Wiki corpus. In testing data, this domain consists of 515 small texts with corresponding unannotated UCCA xmls.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Experiment and Evaluation", "sec_num": "4" }, { "text": "We test our method for both open and closed track in the English setting: (i) closed track submission is only allowed to use the gold-standard UCCA annotation distributed for the task in the target language, and limited in its use of additional resources; (ii) open track submission is allowed to use any additional resource. The testing results show that our GT system creates good quality UCCA semantic representations in English Wiki testing data.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Experiment and Evaluation", "sec_num": "4" }, { "text": "We have presented the graph transformation method for creating UCCA semantic representation from English in-domain setting, using the Wiki corpus 1 . Our method performs four main tasks: (i) illustrate the graph form of Stanford dependencies 2 of input text; (ii) transform into an intermediate graph; (iii) continue to transform into ouput graph form; (iv) create the output UCCA xml. The experiment results show that our method meets the requirements from SemEval Task 1 . In future works, we intend to improve the transformational algorithms and propose more accurate rules for selecting best nodes and dependency tags. Besides, we expand our method and test with other datasets for a broader comparison.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Conclusion", "sec_num": "5" }, { "text": "https://competitions.codalab.org/competitions/19160", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": ". In Section 3, we introduce our GT system for Task 1 . 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SemEval 2014 Task 8:Broad-Coverage Semantic Dependency Parsing. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). Dublin, Ireland, pages 63-72.", "links": null } }, "ref_entries": { "FIGREF0": { "uris": null, "type_str": "figure", "text": "Graph form of UCCA xml of sentence in Example 1.", "num": null }, "FIGREF1": { "uris": null, "type_str": "figure", "text": "Graph form of SD basic dependencies of sentence in Example 1.", "num": null }, "FIGREF2": { "uris": null, "type_str": "figure", "text": "Intermediate graph of sentence in Example 1.", "num": null }, "FIGREF3": { "uris": null, "type_str": "figure", "text": "First reduction of Graph form inFigure 1.", "num": null }, "FIGREF4": { "uris": null, "type_str": "figure", "text": "Second reduction of Graph form inFigure 1.", "num": null }, "TABREF1": { "content": "
and 2 view the results of testing data for
open and closed tracks with labeled (first row) and un-
labeled scores (second row).
AveragedPRF1
F1
0.708Primary0.7380.6940.715
Remote1.0000.0000.000
0.822Primary0.8570.8060.831
Remote1.0000.0000.000
", "type_str": "table", "html": null, "text": "", "num": null }, "TABREF2": { "content": "
AveragedPRF1
F1
0.706Primary0.7370.6920.714
Remote1.0000.0000.000
0.825Primary0.8600.8080.833
Remote1.0000.0000.000
", "type_str": "table", "html": null, "text": "Results of testing data in open track.", "num": null }, "TABREF3": { "content": "", "type_str": "table", "html": null, "text": "Results of testing data in closed track.", "num": null } } } }