MIGA: A Unified Multi-Task Generation Framework for Conversational Text-to-SQL Article Swipe
Yingwen Fu
,
Wenjie Ou
,
Yu Zhou
,
Yue Lin
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v37i11.26504
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v37i11.26504
Conversational text-to-SQL is designed to translate multi-turn natural language questions into their corresponding SQL queries. Most advanced conversational text-to-SQL methods are incompatible with generative pre-trained language models (PLMs), such as T5. In this paper, we present a two-stage unified MultI-task Generation frAmework (MIGA) that leverages PLMs’ ability to tackle conversational text-to-SQL. In the pre-training stage, MIGA first decomposes the main task into several related sub-tasks and then unifies them into the same sequence-to-sequence (Seq2Seq) paradigm with task-specific natural language prompts to boost the main task from multi-task training. Later in the fine-tuning stage, we propose four SQL perturbations to alleviate the error propagation problem. MIGA tends to achieve state-of-the-art performance on two benchmarks (SparC and CoSQL). We also provide extensive analyses and discussions to shed light on some new perspectives for conversational text-to-SQL.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v37i11.26504
- https://ojs.aaai.org/index.php/AAAI/article/download/26504/26276
- OA Status
- diamond
- Cited By
- 8
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382202648
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Raw OpenAlex JSON
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https://openalex.org/W4382202648Canonical identifier for this work in OpenAlex
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https://doi.org/10.1609/aaai.v37i11.26504Digital Object Identifier
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MIGA: A Unified Multi-Task Generation Framework for Conversational Text-to-SQLWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-26Full publication date if available
- Authors
-
Yingwen Fu, Wenjie Ou, Yu Zhou, Yue LinList of authors in order
- Landing page
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https://doi.org/10.1609/aaai.v37i11.26504Publisher landing page
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https://ojs.aaai.org/index.php/AAAI/article/download/26504/26276Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/26504/26276Direct OA link when available
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Computer science, SQL, Task (project management), Natural language processing, Artificial intelligence, Natural language, Programming language, Management, EconomicsTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2025: 5, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
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59Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.light | 125 |
| abstract_inverted_index.tends | 105 |
| abstract_inverted_index.their | 11 |
| abstract_inverted_index.(MIGA) | 42 |
| abstract_inverted_index.(SparC | 113 |
| abstract_inverted_index.models | 26 |
| abstract_inverted_index.paper, | 33 |
| abstract_inverted_index.stage, | 54, 92 |
| abstract_inverted_index.tackle | 48 |
| abstract_inverted_index.(PLMs), | 27 |
| abstract_inverted_index.CoSQL). | 115 |
| abstract_inverted_index.PLMs’ | 45 |
| abstract_inverted_index.ability | 46 |
| abstract_inverted_index.achieve | 107 |
| abstract_inverted_index.methods | 19 |
| abstract_inverted_index.natural | 7, 77 |
| abstract_inverted_index.present | 35 |
| abstract_inverted_index.prompts | 79 |
| abstract_inverted_index.propose | 94 |
| abstract_inverted_index.provide | 118 |
| abstract_inverted_index.related | 63 |
| abstract_inverted_index.several | 62 |
| abstract_inverted_index.unified | 38 |
| abstract_inverted_index.unifies | 67 |
| abstract_inverted_index.advanced | 16 |
| abstract_inverted_index.analyses | 120 |
| abstract_inverted_index.designed | 3 |
| abstract_inverted_index.language | 8, 25, 78 |
| abstract_inverted_index.paradigm | 74 |
| abstract_inverted_index.problem. | 103 |
| abstract_inverted_index.queries. | 14 |
| abstract_inverted_index.(Seq2Seq) | 73 |
| abstract_inverted_index.alleviate | 99 |
| abstract_inverted_index.extensive | 119 |
| abstract_inverted_index.frAmework | 41 |
| abstract_inverted_index.leverages | 44 |
| abstract_inverted_index.questions | 9 |
| abstract_inverted_index.sub-tasks | 64 |
| abstract_inverted_index.training. | 87 |
| abstract_inverted_index.translate | 5 |
| abstract_inverted_index.two-stage | 37 |
| abstract_inverted_index.Generation | 40 |
| abstract_inverted_index.MultI-task | 39 |
| abstract_inverted_index.benchmarks | 112 |
| abstract_inverted_index.decomposes | 57 |
| abstract_inverted_index.generative | 23 |
| abstract_inverted_index.multi-task | 86 |
| abstract_inverted_index.multi-turn | 6 |
| abstract_inverted_index.discussions | 122 |
| abstract_inverted_index.fine-tuning | 91 |
| abstract_inverted_index.performance | 109 |
| abstract_inverted_index.pre-trained | 24 |
| abstract_inverted_index.propagation | 102 |
| abstract_inverted_index.text-to-SQL | 1, 18 |
| abstract_inverted_index.incompatible | 21 |
| abstract_inverted_index.perspectives | 129 |
| abstract_inverted_index.pre-training | 53 |
| abstract_inverted_index.text-to-SQL. | 50, 132 |
| abstract_inverted_index.corresponding | 12 |
| abstract_inverted_index.perturbations | 97 |
| abstract_inverted_index.task-specific | 76 |
| abstract_inverted_index.Conversational | 0 |
| abstract_inverted_index.conversational | 17, 49, 131 |
| abstract_inverted_index.state-of-the-art | 108 |
| abstract_inverted_index.sequence-to-sequence | 72 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.73996873 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |