Domain Specific Sub-network for Multi-Domain Neural Machine Translation Article Swipe
Amr Hendy
,
Mohamed Abdelghaffar
,
Mohamed Afify
,
Ahmed Y. Tawfik
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.09805
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.09805
This paper presents Domain-Specific Sub-network (DoSS). It uses a set of masks obtained through pruning to define a sub-network for each domain and finetunes the sub-network parameters on domain data. This performs very closely and drastically reduces the number of parameters compared to finetuning the whole network on each domain. Also a method to make masks unique per domain is proposed and shown to greatly improve the generalization to unseen domains. In our experiments on German to English machine translation the proposed method outperforms the strong baseline of continue training on multi-domain (medical, tech and religion) data by 1.47 BLEU points. Also continue training DoSS on new domain (legal) outperforms the multi-domain (medical, tech, religion, legal) baseline by 1.52 BLEU points.
Related Topics
Concepts
Domain (mathematical analysis)
Computer science
Baseline (sea)
Pruning
Machine translation
Artificial intelligence
Translation (biology)
Generalization
Set (abstract data type)
Artificial neural network
Machine learning
Training set
Data mining
Algorithm
Mathematics
Messenger RNA
Gene
Programming language
Chemistry
Biochemistry
Agronomy
Oceanography
Mathematical analysis
Biology
Geology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2210.09805
- https://arxiv.org/pdf/2210.09805
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4306890615
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4306890615Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2210.09805Digital Object Identifier
- Title
-
Domain Specific Sub-network for Multi-Domain Neural Machine TranslationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-18Full publication date if available
- Authors
-
Amr Hendy, Mohamed Abdelghaffar, Mohamed Afify, Ahmed Y. TawfikList of authors in order
- Landing page
-
https://arxiv.org/abs/2210.09805Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2210.09805Direct 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/2210.09805Direct OA link when available
- Concepts
-
Domain (mathematical analysis), Computer science, Baseline (sea), Pruning, Machine translation, Artificial intelligence, Translation (biology), Generalization, Set (abstract data type), Artificial neural network, Machine learning, Training set, Data mining, Algorithm, Mathematics, Messenger RNA, Gene, Programming language, Chemistry, Biochemistry, Agronomy, Oceanography, Mathematical analysis, Biology, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.continue | 88, 102 |
| abstract_inverted_index.domains. | 70 |
| abstract_inverted_index.obtained | 12 |
| abstract_inverted_index.performs | 31 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.proposed | 60, 81 |
| abstract_inverted_index.training | 89, 103 |
| abstract_inverted_index.(medical, | 92, 112 |
| abstract_inverted_index.finetunes | 23 |
| abstract_inverted_index.religion) | 95 |
| abstract_inverted_index.religion, | 114 |
| abstract_inverted_index.finetuning | 43 |
| abstract_inverted_index.parameters | 26, 40 |
| abstract_inverted_index.Sub-network | 4 |
| abstract_inverted_index.drastically | 35 |
| abstract_inverted_index.experiments | 73 |
| abstract_inverted_index.outperforms | 83, 109 |
| abstract_inverted_index.sub-network | 18, 25 |
| abstract_inverted_index.translation | 79 |
| abstract_inverted_index.multi-domain | 91, 111 |
| abstract_inverted_index.generalization | 67 |
| abstract_inverted_index.Domain-Specific | 3 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |