An explainable semantic parser for end-user development:
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| Format: | Thesis Book |
| Language: | English |
| Published: |
Passau
2021
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| Subjects: | |
| Online Access: | Volltext Volltext Inhaltsverzeichnis |
| Physical Description: | xvi, 165 Seiten Illustrationen, Diagramme |
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| adam_text | Contents I Preamble 1 1 3 Introduction 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 Programming to the Masses...................................................... End-User Programming............................................................ The Semantic Parsing Problem................................................ The Proposed Semantic Parser................................................ 1.4.1 Shallow Parser............................................................... 1.4.2 Ranking Model............................................................... Explainable Artificial Intelligence............................................. The Hierarchical Explanation Model....................................... Research Questions, Hypotheses Goals .............................. 1.7.1 Semantic Parsing............................................................ 1.7.2 Explainability .................................................................. Contributions.............................................................................. Publications................................................................................. Thesis Outline ......................................................................... II Related Work 2 19 End-User Development Explainability 21 2.1 2.2 21 23 23 24 26 27 27 28 29 29 30 31 32 2.3 2.4 2.5 2.6 3 3 4 4 6 6 7 9 10 11 12 13 14 15 16 Introduction................................................................................. Types of End-User Development ............................................. 2.2.1 Visual
Programming...................................................... 2.2.2 Programming by Example............................................. 2.2.3 Crowd-Supported Programming ................................. 2.2.4 Natural Language Programming................................. Other Aspects of End-User Development................................. 2.3.1 Teaching ........................................................................ 2.3.2 Machine Learning ......................................................... 2.3.3 Software Engineering...................................................... The User Perspective.................................................................. Explainability .............................................................................. Summary.................................................................................... Semantic Parsingof Natural LanguageCommands 3.1 3.2 35 Introduction................................................................................. 35 The Typical Tasks of Parsing of Natural Language Commands 36 3.2.1 Commands for Database Queries................................. 36 vii
Contents viii 3.3 3.4 3.5 3.6 III 4 Designing an Explainable Semantic Parser 59 A Semantic Parser for End-User Development 61 4.1 4.2 4.3 4.4 61 63 64 66 69 70 71 73 73 75 77 77 78 4.5 5 3.2.2 Navigational Instructions.............................................. 38 3.2.3 Program Synthesis.......................................................... 39 3.2.4 Natural Language Interfacefor APIs............................. 41 3.2.5 Other Interests in Semantic Parsingof Commands . . 42 Approaches and Methods .......................................................... 43 3.3.1 Methods Based on Rules................................................ 43 3.3.2 Learning-Based Methods ............................................. 46 An Overview of the Methods for Parsing Natural Language Commands......................................................................... 52 3.4.1 Approach and Feature Set............................................. 52 3.4.2 Explainability.................................................................. 52 3.4.3 Evaluation ..................................................................... 57 Exposing the Gap .............................. 57 Summary..................................................................................... 58 Introduction................................................................................. Re-engineering the Learning Task............................................. Componentising the SemanticParsing..................................... The Proposed Architecture......................................................
4.4.1 Command Segmenter .............. 4.4.2 Shallow Parser............................................................... 4.4.3 Type Inferencer............................................................... 4.4.4 Predicate-Argument Structure .................................... 4.4.5 API Filter........................................................................ 4.4.6 Function Call Generator................................................ 4.4.7 Intent Classifier............................................................... 4.4.8 Ranker ........................................................................... Summary.................................................................................... Explanation Model of a Multi-Component Semantic Parser 79 5.1 5.2 5.3 5.4 Introduction................................................................................. Levels of Interpretability ............................................................ The Explanation Model............................................................ 5.3.1 Shallow Parser Rules Syntactic Tree Layers........... 5.3.2 Word Embedding Layer............................................ 5.3.3 The Ranking Classification Layer........................... 5.3.4 The ComparativeExplanation ...................................... Summary.................................................................................... 79 80 81 81 83 83 85 85
Contents I V Experiments and Validation ix 89 6 The Evaluation of the Semantic Parser 91 6.1 Introduction.................................................................................. 91 6.1.1 The Data Set................................................................... 91 6.2 Indra............................................................................................. 92 6.2.1 Word Embedding Vector................................................. 93 6.2.2 Composition Vector....................................................... 93 6.2.3 Semantic Relatedness .................................................... 94 6.2.4 Nearest Neighbours ....................................................... 94 6.3 The Encoder-Decoder Baseline Models.................................... 94 6.3.1 The Sequence-to-Sequence Baseline............................... 94 6.3.2 The Attention-Based Baseline........................................ 95 6.3.3 Baseline Analysis............................................................. 97 6.4 Evaluating the Proposed Architecture.................................... 97 6.4.1 API Filter Settings.......................................................... 97 6.4.2 Intent Classifier Settings................................................. 97 6.5 Results Discussion.................................................................. 98 6.5.1 API Filter......................................................................... 99 6.5.2 Classifiers............................................................................ 102 6.6
Summary....................................................................................... 104 7 The Evaluation of the Explanation Model 105 7.1 Introduction................................................................................... 105 7.2 The Experimental Setting............................................................106 7.2.1 Participants.........................................................................106 7.2.2 General Instructions.......................................................... 107 7.3 Evaluation Criteria........................................................................107 7.3.1 Mental Models................................................................... 108 7.3.2 Type of Explanations ...................................................... 109 7.3.3 Accuracy vs. Explainability............................................. 109 7.4 Results Discussion.....................................................................110 7.4.1 Mental Models...................................................................110 7.4.2 Relevance of theExplanations ......................................... Ill 7.4.3 The Accuracy vs. Explainability Dilemma..................... 112 7.4.4 Concerns About the Design of the Experiment .... 113 7.4.5 Considerations about Completeness .............................. 114 7.4.6 Participants’ Comments and Impressions ..................... 114 7.5 Summary...................................................................................... 115
Contents x V Conclusions and Perspectives 117 8 Conclusions and Future Research 119 8.1 Summary........................................................................................ 119 8.2 Discussion and Conclusions.......................................................... 121 8.2.1 Semantic Parsing............................................................... 121 8.2.2 Explanation Model............................................................ 123 8.3 Revisiting the Principles and Requirements of End-User Pro gramming ....................................................................................... 124 8.3.1 Motivational Task.............................................................124 8.3.2 Principles............................................................................ 125 8.3.3 Challenges ......................................................................... 126 8.4 Future Research Directions ..........................................................129 8.4.1 Enriching the TrainingData ............................................ 129 8.4.2 Further Research on Explainability..................................130 Appendices 133 A Questionnaire 135 AT Mental Models.............................................................................. 135 A.2 Types of Explanation .................................................................. 136 A.3 Accuracy over Interpretability...................................................... 137 Bibliography 138
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| adam_txt |
Contents I Preamble 1 1 3 Introduction 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 Programming to the Masses. End-User Programming. The Semantic Parsing Problem. The Proposed Semantic Parser. 1.4.1 Shallow Parser. 1.4.2 Ranking Model. Explainable Artificial Intelligence. The Hierarchical Explanation Model. Research Questions, Hypotheses Goals . 1.7.1 Semantic Parsing. 1.7.2 Explainability . Contributions. Publications. Thesis Outline . II Related Work 2 19 End-User Development Explainability 21 2.1 2.2 21 23 23 24 26 27 27 28 29 29 30 31 32 2.3 2.4 2.5 2.6 3 3 4 4 6 6 7 9 10 11 12 13 14 15 16 Introduction. Types of End-User Development . 2.2.1 Visual
Programming. 2.2.2 Programming by Example. 2.2.3 Crowd-Supported Programming . 2.2.4 Natural Language Programming. Other Aspects of End-User Development. 2.3.1 Teaching . 2.3.2 Machine Learning . 2.3.3 Software Engineering. The User Perspective. Explainability . Summary. Semantic Parsingof Natural LanguageCommands 3.1 3.2 35 Introduction. 35 The Typical Tasks of Parsing of Natural Language Commands 36 3.2.1 Commands for Database Queries. 36 vii
Contents viii 3.3 3.4 3.5 3.6 III 4 Designing an Explainable Semantic Parser 59 A Semantic Parser for End-User Development 61 4.1 4.2 4.3 4.4 61 63 64 66 69 70 71 73 73 75 77 77 78 4.5 5 3.2.2 Navigational Instructions. 38 3.2.3 Program Synthesis. 39 3.2.4 Natural Language Interfacefor APIs. 41 3.2.5 Other Interests in Semantic Parsingof Commands . . 42 Approaches and Methods . 43 3.3.1 Methods Based on Rules. 43 3.3.2 Learning-Based Methods . 46 An Overview of the Methods for Parsing Natural Language Commands. 52 3.4.1 Approach and Feature Set. 52 3.4.2 Explainability. 52 3.4.3 Evaluation . 57 Exposing the Gap . 57 Summary. 58 Introduction. Re-engineering the Learning Task. Componentising the SemanticParsing. The Proposed Architecture.
4.4.1 Command Segmenter . 4.4.2 Shallow Parser. 4.4.3 Type Inferencer. 4.4.4 Predicate-Argument Structure . 4.4.5 API Filter. 4.4.6 Function Call Generator. 4.4.7 Intent Classifier. 4.4.8 Ranker . Summary. Explanation Model of a Multi-Component Semantic Parser 79 5.1 5.2 5.3 5.4 Introduction. Levels of Interpretability . The Explanation Model. 5.3.1 Shallow Parser Rules Syntactic Tree Layers. 5.3.2 Word Embedding Layer. 5.3.3 The Ranking Classification Layer. 5.3.4 The ComparativeExplanation . Summary. 79 80 81 81 83 83 85 85
Contents I V Experiments and Validation ix 89 6 The Evaluation of the Semantic Parser 91 6.1 Introduction. 91 6.1.1 The Data Set. 91 6.2 Indra. 92 6.2.1 Word Embedding Vector. 93 6.2.2 Composition Vector. 93 6.2.3 Semantic Relatedness . 94 6.2.4 Nearest Neighbours . 94 6.3 The Encoder-Decoder Baseline Models. 94 6.3.1 The Sequence-to-Sequence Baseline. 94 6.3.2 The Attention-Based Baseline. 95 6.3.3 Baseline Analysis. 97 6.4 Evaluating the Proposed Architecture. 97 6.4.1 API Filter Settings. 97 6.4.2 Intent Classifier Settings. 97 6.5 Results Discussion. 98 6.5.1 API Filter. 99 6.5.2 Classifiers. 102 6.6
Summary. 104 7 The Evaluation of the Explanation Model 105 7.1 Introduction. 105 7.2 The Experimental Setting.106 7.2.1 Participants.106 7.2.2 General Instructions. 107 7.3 Evaluation Criteria.107 7.3.1 Mental Models. 108 7.3.2 Type of Explanations . 109 7.3.3 Accuracy vs. Explainability. 109 7.4 Results Discussion.110 7.4.1 Mental Models.110 7.4.2 Relevance of theExplanations . Ill 7.4.3 The Accuracy vs. Explainability Dilemma. 112 7.4.4 Concerns About the Design of the Experiment . 113 7.4.5 Considerations about Completeness . 114 7.4.6 Participants’ Comments and Impressions . 114 7.5 Summary. 115
Contents x V Conclusions and Perspectives 117 8 Conclusions and Future Research 119 8.1 Summary. 119 8.2 Discussion and Conclusions. 121 8.2.1 Semantic Parsing. 121 8.2.2 Explanation Model. 123 8.3 Revisiting the Principles and Requirements of End-User Pro gramming . 124 8.3.1 Motivational Task.124 8.3.2 Principles. 125 8.3.3 Challenges . 126 8.4 Future Research Directions .129 8.4.1 Enriching the TrainingData . 129 8.4.2 Further Research on Explainability.130 Appendices 133 A Questionnaire 135 AT Mental Models. 135 A.2 Types of Explanation . 136 A.3 Accuracy over Interpretability. 137 Bibliography 138 |
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| spelling | Sales, Juliano Efson Verfasser (DE-588)1259747778 aut An explainable semantic parser for end-user development Juliano Efson Sales Passau 2021 xvi, 165 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Dissertation Universität Passau 2022 Automatische Sprachanalyse (DE-588)4129935-8 gnd rswk-swf Parser (DE-588)4125056-4 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Parser (DE-588)4125056-4 s Automatische Sprachanalyse (DE-588)4129935-8 s DE-604 Erscheint auch als Online-Ausgabe urn:nbn:de:bvb:739-opus4-10718 https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/1071 kostenfrei Volltext https://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-10718 Resolving-System kostenfrei Volltext Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033625242&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Sales, Juliano Efson An explainable semantic parser for end-user development Automatische Sprachanalyse (DE-588)4129935-8 gnd Parser (DE-588)4125056-4 gnd |
| subject_GND | (DE-588)4129935-8 (DE-588)4125056-4 (DE-588)4113937-9 |
| title | An explainable semantic parser for end-user development |
| title_auth | An explainable semantic parser for end-user development |
| title_exact_search | An explainable semantic parser for end-user development |
| title_exact_search_txtP | An explainable semantic parser for end-user development |
| title_full | An explainable semantic parser for end-user development Juliano Efson Sales |
| title_fullStr | An explainable semantic parser for end-user development Juliano Efson Sales |
| title_full_unstemmed | An explainable semantic parser for end-user development Juliano Efson Sales |
| title_short | An explainable semantic parser for end-user development |
| title_sort | an explainable semantic parser for end user development |
| topic | Automatische Sprachanalyse (DE-588)4129935-8 gnd Parser (DE-588)4125056-4 gnd |
| topic_facet | Automatische Sprachanalyse Parser Hochschulschrift |
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