SETTP: Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning Article Swipe
Chaofan Jin
,
Yongfeng Huang
,
Yaqi Wang
,
Peng Cao
,
Osmar R. Zai͏̈ane
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.15556
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.15556
Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources. In this work, we introduce a novel method termed Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning (SETTP) for effective style transfer in low-resource scenarios. First, SETTP learns source style-level prompts containing fundamental style characteristics from high-resource style transfer. During training, the source style-level prompts are transferred through an attention module to derive a target style-level prompt for beneficial knowledge provision in low-resource style transfer. Additionally, we propose instance-level prompts obtained by clustering the target resources based on the semantic content to reduce semantic bias. We also propose an automated evaluation approach of style similarity based on alignment with human evaluations using ChatGPT-4. Our experiments across three resourceful styles show that SETTP requires only 1/20th of the data volume to achieve performance comparable to state-of-the-art methods. In tasks involving scarce data like writing style and role style, SETTP outperforms previous methods by 16.24\%.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.15556
- https://arxiv.org/pdf/2407.15556
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406073262
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4406073262Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2407.15556Digital Object Identifier
- Title
-
SETTP: Style Extraction and Tunable Inference via Dual-level Transferable Prompt LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-22Full publication date if available
- Authors
-
Chaofan Jin, Yongfeng Huang, Yaqi Wang, Peng Cao, Osmar R. Zai͏̈aneList of authors in order
- Landing page
-
https://arxiv.org/abs/2407.15556Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2407.15556Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2407.15556Direct OA link when available
- Concepts
-
Style (visual arts), Inference, Dual (grammatical number), Extraction (chemistry), Transferable utility, Computer science, Artificial intelligence, Mathematics, Geography, Chromatography, Chemistry, Mathematical economics, Philosophy, Linguistics, Archaeology, Game theoryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4406073262 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2407.15556 |
| ids.doi | https://doi.org/10.48550/arxiv.2407.15556 |
| ids.openalex | https://openalex.org/W4406073262 |
| fwci | |
| type | preprint |
| title | SETTP: Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10181 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9958000183105469 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Natural Language Processing Techniques |
| topics[1].id | https://openalex.org/T10028 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9912999868392944 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Topic Modeling |
| topics[2].id | https://openalex.org/T10201 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.973800003528595 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Speech Recognition and Synthesis |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2776445246 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6912093758583069 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1792644 |
| concepts[0].display_name | Style (visual arts) |
| concepts[1].id | https://openalex.org/C2776214188 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6873072385787964 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q408386 |
| concepts[1].display_name | Inference |
| concepts[2].id | https://openalex.org/C2780980858 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6459386348724365 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q110022 |
| concepts[2].display_name | Dual (grammatical number) |
| concepts[3].id | https://openalex.org/C4725764 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5947002172470093 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q844704 |
| concepts[3].display_name | Extraction (chemistry) |
| concepts[4].id | https://openalex.org/C81302675 |
| concepts[4].level | 3 |
| concepts[4].score | 0.4650130867958069 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7834034 |
| concepts[4].display_name | Transferable utility |
| concepts[5].id | https://openalex.org/C41008148 |
| concepts[5].level | 0 |
| concepts[5].score | 0.45385560393333435 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[5].display_name | Computer science |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3446889817714691 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C33923547 |
| concepts[7].level | 0 |
| concepts[7].score | 0.17054709792137146 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[7].display_name | Mathematics |
| concepts[8].id | https://openalex.org/C205649164 |
| concepts[8].level | 0 |
| concepts[8].score | 0.1305328607559204 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[8].display_name | Geography |
| concepts[9].id | https://openalex.org/C43617362 |
| concepts[9].level | 1 |
| concepts[9].score | 0.11534404754638672 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q170050 |
| concepts[9].display_name | Chromatography |
| concepts[10].id | https://openalex.org/C185592680 |
| concepts[10].level | 0 |
| concepts[10].score | 0.10117831826210022 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[10].display_name | Chemistry |
| concepts[11].id | https://openalex.org/C144237770 |
| concepts[11].level | 1 |
| concepts[11].score | 0.09644117951393127 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q747534 |
| concepts[11].display_name | Mathematical economics |
| concepts[12].id | https://openalex.org/C138885662 |
| concepts[12].level | 0 |
| concepts[12].score | 0.07223448157310486 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[12].display_name | Philosophy |
| concepts[13].id | https://openalex.org/C41895202 |
| concepts[13].level | 1 |
| concepts[13].score | 0.056048423051834106 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[13].display_name | Linguistics |
| concepts[14].id | https://openalex.org/C166957645 |
| concepts[14].level | 1 |
| concepts[14].score | 0.05260658264160156 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q23498 |
| concepts[14].display_name | Archaeology |
| concepts[15].id | https://openalex.org/C177142836 |
| concepts[15].level | 2 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q44455 |
| concepts[15].display_name | Game theory |
| keywords[0].id | https://openalex.org/keywords/style |
| keywords[0].score | 0.6912093758583069 |
| keywords[0].display_name | Style (visual arts) |
| keywords[1].id | https://openalex.org/keywords/inference |
| keywords[1].score | 0.6873072385787964 |
| keywords[1].display_name | Inference |
| keywords[2].id | https://openalex.org/keywords/dual |
| keywords[2].score | 0.6459386348724365 |
| keywords[2].display_name | Dual (grammatical number) |
| keywords[3].id | https://openalex.org/keywords/extraction |
| keywords[3].score | 0.5947002172470093 |
| keywords[3].display_name | Extraction (chemistry) |
| keywords[4].id | https://openalex.org/keywords/transferable-utility |
| keywords[4].score | 0.4650130867958069 |
| keywords[4].display_name | Transferable utility |
| keywords[5].id | https://openalex.org/keywords/computer-science |
| keywords[5].score | 0.45385560393333435 |
| keywords[5].display_name | Computer science |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.3446889817714691 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/mathematics |
| keywords[7].score | 0.17054709792137146 |
| keywords[7].display_name | Mathematics |
| keywords[8].id | https://openalex.org/keywords/geography |
| keywords[8].score | 0.1305328607559204 |
| keywords[8].display_name | Geography |
| keywords[9].id | https://openalex.org/keywords/chromatography |
| keywords[9].score | 0.11534404754638672 |
| keywords[9].display_name | Chromatography |
| keywords[10].id | https://openalex.org/keywords/chemistry |
| keywords[10].score | 0.10117831826210022 |
| keywords[10].display_name | Chemistry |
| keywords[11].id | https://openalex.org/keywords/mathematical-economics |
| keywords[11].score | 0.09644117951393127 |
| keywords[11].display_name | Mathematical economics |
| keywords[12].id | https://openalex.org/keywords/philosophy |
| keywords[12].score | 0.07223448157310486 |
| keywords[12].display_name | Philosophy |
| keywords[13].id | https://openalex.org/keywords/linguistics |
| keywords[13].score | 0.056048423051834106 |
| keywords[13].display_name | Linguistics |
| keywords[14].id | https://openalex.org/keywords/archaeology |
| keywords[14].score | 0.05260658264160156 |
| keywords[14].display_name | Archaeology |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2407.15556 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2407.15556 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2407.15556 |
| locations[1].id | doi:10.48550/arxiv.2407.15556 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2407.15556 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5103028388 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-0998-2920 |
| authorships[0].author.display_name | Chaofan Jin |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jin, Chunzhen |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5100768896 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3825-2230 |
| authorships[1].author.display_name | Yongfeng Huang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Huang, Yongfeng |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5115592598 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-6831-9742 |
| authorships[2].author.display_name | Yaqi Wang |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Wang, Yaqi |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5101986225 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-9546-7444 |
| authorships[3].author.display_name | Peng Cao |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Cao, Peng |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5053745515 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-0060-5988 |
| authorships[4].author.display_name | Osmar R. Zai͏̈ane |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Zaiane, Osmar |
| authorships[4].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2407.15556 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-01-05T00:00:00 |
| display_name | SETTP: Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10181 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9958000183105469 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Natural Language Processing Techniques |
| related_works | https://openalex.org/W2085313570, https://openalex.org/W1606293679, https://openalex.org/W3122352033, https://openalex.org/W2111189736, https://openalex.org/W1528849005, https://openalex.org/W4296977871, https://openalex.org/W2100658395, https://openalex.org/W2158669678, https://openalex.org/W2363119030, https://openalex.org/W2050380383 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2407.15556 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2407.15556 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2407.15556 |
| primary_location.id | pmh:oai:arXiv.org:2407.15556 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2407.15556 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2407.15556 |
| publication_date | 2024-07-22 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 31, 81 |
| abstract_inverted_index.In | 26, 154 |
| abstract_inverted_index.We | 113 |
| abstract_inverted_index.an | 3, 76, 116 |
| abstract_inverted_index.by | 99, 169 |
| abstract_inverted_index.in | 7, 50, 89 |
| abstract_inverted_index.of | 120, 143 |
| abstract_inverted_index.on | 105, 124 |
| abstract_inverted_index.to | 12, 16, 79, 109, 147, 151 |
| abstract_inverted_index.we | 29, 94 |
| abstract_inverted_index.Our | 131 |
| abstract_inverted_index.and | 37, 162 |
| abstract_inverted_index.are | 73 |
| abstract_inverted_index.but | 19 |
| abstract_inverted_index.for | 46, 85 |
| abstract_inverted_index.the | 14, 69, 101, 106, 144 |
| abstract_inverted_index.via | 40 |
| abstract_inverted_index.Text | 0 |
| abstract_inverted_index.aims | 11 |
| abstract_inverted_index.also | 114 |
| abstract_inverted_index.data | 145, 158 |
| abstract_inverted_index.from | 63 |
| abstract_inverted_index.like | 159 |
| abstract_inverted_index.only | 141 |
| abstract_inverted_index.role | 163 |
| abstract_inverted_index.show | 137 |
| abstract_inverted_index.text | 15 |
| abstract_inverted_index.that | 138 |
| abstract_inverted_index.this | 27 |
| abstract_inverted_index.with | 23, 126 |
| abstract_inverted_index.SETTP | 54, 139, 165 |
| abstract_inverted_index.Style | 35 |
| abstract_inverted_index.adapt | 13 |
| abstract_inverted_index.based | 104, 123 |
| abstract_inverted_index.bias. | 112 |
| abstract_inverted_index.faces | 21 |
| abstract_inverted_index.human | 127 |
| abstract_inverted_index.novel | 32 |
| abstract_inverted_index.often | 20 |
| abstract_inverted_index.style | 1, 48, 61, 65, 91, 121, 161 |
| abstract_inverted_index.tasks | 155 |
| abstract_inverted_index.three | 134 |
| abstract_inverted_index.using | 129 |
| abstract_inverted_index.work, | 28 |
| abstract_inverted_index.1/20th | 142 |
| abstract_inverted_index.During | 67 |
| abstract_inverted_index.First, | 53 |
| abstract_inverted_index.Prompt | 43 |
| abstract_inverted_index.across | 133 |
| abstract_inverted_index.derive | 80 |
| abstract_inverted_index.learns | 55 |
| abstract_inverted_index.method | 33 |
| abstract_inverted_index.module | 78 |
| abstract_inverted_index.prompt | 84 |
| abstract_inverted_index.reduce | 110 |
| abstract_inverted_index.scarce | 157 |
| abstract_inverted_index.source | 56, 70 |
| abstract_inverted_index.style, | 164 |
| abstract_inverted_index.styles | 136 |
| abstract_inverted_index.target | 82, 102 |
| abstract_inverted_index.termed | 34 |
| abstract_inverted_index.volume | 146 |
| abstract_inverted_index.(SETTP) | 45 |
| abstract_inverted_index.Tunable | 38 |
| abstract_inverted_index.achieve | 148 |
| abstract_inverted_index.content | 108 |
| abstract_inverted_index.limited | 24 |
| abstract_inverted_index.methods | 168 |
| abstract_inverted_index.natural | 8 |
| abstract_inverted_index.prompts | 58, 72, 97 |
| abstract_inverted_index.propose | 95, 115 |
| abstract_inverted_index.through | 75 |
| abstract_inverted_index.various | 17 |
| abstract_inverted_index.writing | 160 |
| abstract_inverted_index.16.24\%. | 170 |
| abstract_inverted_index.Learning | 44 |
| abstract_inverted_index.approach | 119 |
| abstract_inverted_index.language | 9 |
| abstract_inverted_index.methods. | 153 |
| abstract_inverted_index.obtained | 98 |
| abstract_inverted_index.previous | 167 |
| abstract_inverted_index.requires | 140 |
| abstract_inverted_index.research | 5 |
| abstract_inverted_index.semantic | 107, 111 |
| abstract_inverted_index.transfer | 49 |
| abstract_inverted_index.Inference | 39 |
| abstract_inverted_index.alignment | 125 |
| abstract_inverted_index.attention | 77 |
| abstract_inverted_index.automated | 117 |
| abstract_inverted_index.direction | 6 |
| abstract_inverted_index.effective | 47 |
| abstract_inverted_index.important | 4 |
| abstract_inverted_index.introduce | 30 |
| abstract_inverted_index.involving | 156 |
| abstract_inverted_index.knowledge | 87 |
| abstract_inverted_index.provision | 88 |
| abstract_inverted_index.resources | 103 |
| abstract_inverted_index.training, | 68 |
| abstract_inverted_index.transfer, | 2 |
| abstract_inverted_index.transfer. | 66, 92 |
| abstract_inverted_index.ChatGPT-4. | 130 |
| abstract_inverted_index.Dual-level | 41 |
| abstract_inverted_index.Extraction | 36 |
| abstract_inverted_index.beneficial | 86 |
| abstract_inverted_index.challenges | 22 |
| abstract_inverted_index.clustering | 100 |
| abstract_inverted_index.comparable | 150 |
| abstract_inverted_index.containing | 59 |
| abstract_inverted_index.evaluation | 118 |
| abstract_inverted_index.resources. | 25 |
| abstract_inverted_index.scenarios. | 52 |
| abstract_inverted_index.similarity | 122 |
| abstract_inverted_index.evaluations | 128 |
| abstract_inverted_index.experiments | 132 |
| abstract_inverted_index.fundamental | 60 |
| abstract_inverted_index.outperforms | 166 |
| abstract_inverted_index.performance | 149 |
| abstract_inverted_index.preferences | 18 |
| abstract_inverted_index.processing, | 10 |
| abstract_inverted_index.resourceful | 135 |
| abstract_inverted_index.style-level | 57, 71, 83 |
| abstract_inverted_index.transferred | 74 |
| abstract_inverted_index.Transferable | 42 |
| abstract_inverted_index.low-resource | 51, 90 |
| abstract_inverted_index.Additionally, | 93 |
| abstract_inverted_index.high-resource | 64 |
| abstract_inverted_index.instance-level | 96 |
| abstract_inverted_index.characteristics | 62 |
| abstract_inverted_index.state-of-the-art | 152 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |