LIQUID: A Framework for List Question Answering Dataset Generation Article Swipe
Seongyun Lee
,
Hyunjae Kim
,
Jaewoo Kang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.01691
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.01691
Question answering (QA) models often rely on large-scale training datasets, which necessitates the development of a data generation framework to reduce the cost of manual annotations. Although several recent studies have aimed to generate synthetic questions with single-span answers, no study has been conducted on the creation of list questions with multiple, non-contiguous spans as answers. To address this gap, we propose LIQUID, an automated framework for generating list QA datasets from unlabeled corpora. We first convert a passage from Wikipedia or PubMed into a summary and extract named entities from the summarized text as candidate answers. This allows us to select answers that are semantically correlated in context and is, therefore, suitable for constructing list questions. We then create questions using an off-the-shelf question generator with the extracted entities and original passage. Finally, iterative filtering and answer expansion are performed to ensure the accuracy and completeness of the answers. Using our synthetic data, we significantly improve the performance of the previous best list QA models by exact-match F1 scores of 5.0 on MultiSpanQA, 1.9 on Quoref, and 2.8 averaged across three BioASQ benchmarks.
Related Topics
Concepts
Computer science
Question answering
Information retrieval
Completeness (order theory)
Context (archaeology)
Generator (circuit theory)
Natural language processing
Data science
Data mining
Artificial intelligence
Physics
Paleontology
Mathematical analysis
Quantum mechanics
Mathematics
Biology
Power (physics)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2302.01691
- https://arxiv.org/pdf/2302.01691
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319451542
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4319451542Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2302.01691Digital Object Identifier
- Title
-
LIQUID: A Framework for List Question Answering Dataset GenerationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-03Full publication date if available
- Authors
-
Seongyun Lee, Hyunjae Kim, Jaewoo KangList of authors in order
- Landing page
-
https://arxiv.org/abs/2302.01691Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2302.01691Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2302.01691Direct OA link when available
- Concepts
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Computer science, Question answering, Information retrieval, Completeness (order theory), Context (archaeology), Generator (circuit theory), Natural language processing, Data science, Data mining, Artificial intelligence, Physics, Paleontology, Mathematical analysis, Quantum mechanics, Mathematics, Biology, Power (physics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2023: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.questions | 35, 49, 120 |
| abstract_inverted_index.synthetic | 34, 152 |
| abstract_inverted_index.unlabeled | 72 |
| abstract_inverted_index.correlated | 106 |
| abstract_inverted_index.generating | 67 |
| abstract_inverted_index.generation | 17 |
| abstract_inverted_index.questions. | 116 |
| abstract_inverted_index.summarized | 92 |
| abstract_inverted_index.therefore, | 111 |
| abstract_inverted_index.benchmarks. | 183 |
| abstract_inverted_index.development | 13 |
| abstract_inverted_index.exact-match | 167 |
| abstract_inverted_index.large-scale | 7 |
| abstract_inverted_index.performance | 158 |
| abstract_inverted_index.single-span | 37 |
| abstract_inverted_index.MultiSpanQA, | 173 |
| abstract_inverted_index.annotations. | 25 |
| abstract_inverted_index.completeness | 146 |
| abstract_inverted_index.constructing | 114 |
| abstract_inverted_index.necessitates | 11 |
| abstract_inverted_index.semantically | 105 |
| abstract_inverted_index.off-the-shelf | 123 |
| abstract_inverted_index.significantly | 155 |
| abstract_inverted_index.non-contiguous | 52 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |