Automatically Identifying Gender Issues in Machine Translation using Perturbations Article Swipe
Hila Gonen
,
Kellie Webster
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2020.findings-emnlp.180
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2020.findings-emnlp.180
The successful application of neural methods to machine translation has realized huge quality advances for the community. With these improvements, many have noted outstanding challenges, including the modeling and treatment of gendered language. While previous studies have identified issues using synthetic examples, we develop a novel technique to mine examples from real world data to explore challenges for deployed systems. We use our method to compile an evaluation benchmark spanning examples for four languages from three language families, which we publicly release to facilitate research. The examples in our benchmark expose where model representations are gendered, and the unintended consequences these gendered representations can have in downstream application.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2020.findings-emnlp.180
- https://www.aclweb.org/anthology/2020.findings-emnlp.180.pdf
- OA Status
- gold
- Cited By
- 5
- References
- 12
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3021719059
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3021719059Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18653/v1/2020.findings-emnlp.180Digital Object Identifier
- Title
-
Automatically Identifying Gender Issues in Machine Translation using PerturbationsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Hila Gonen, Kellie WebsterList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/2020.findings-emnlp.180Publisher landing page
- PDF URL
-
https://www.aclweb.org/anthology/2020.findings-emnlp.180.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.aclweb.org/anthology/2020.findings-emnlp.180.pdfDirect OA link when available
- Concepts
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Leverage (statistics), Computer science, Machine translation, Benchmark (surveying), Compiler, Artificial intelligence, Data science, Language model, Machine learning, Natural language processing, Software engineering, Programming language, Geography, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1, 2021: 1, 2020: 3Per-year citation counts (last 5 years)
- References (count)
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12Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.language. | 32 |
| abstract_inverted_index.languages | 73 |
| abstract_inverted_index.research. | 84 |
| abstract_inverted_index.synthetic | 40 |
| abstract_inverted_index.technique | 46 |
| abstract_inverted_index.treatment | 29 |
| abstract_inverted_index.challenges | 56 |
| abstract_inverted_index.community. | 16 |
| abstract_inverted_index.downstream | 106 |
| abstract_inverted_index.evaluation | 67 |
| abstract_inverted_index.facilitate | 83 |
| abstract_inverted_index.identified | 37 |
| abstract_inverted_index.successful | 1 |
| abstract_inverted_index.unintended | 98 |
| abstract_inverted_index.application | 2 |
| abstract_inverted_index.challenges, | 24 |
| abstract_inverted_index.outstanding | 23 |
| abstract_inverted_index.translation | 8 |
| abstract_inverted_index.application. | 107 |
| abstract_inverted_index.consequences | 99 |
| abstract_inverted_index.improvements, | 19 |
| abstract_inverted_index.representations | 93, 102 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.7594925 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |