MorphTE: Injecting Morphology in Tensorized Embeddings Article Swipe
Guobing Gan
,
Peng Zhang
,
Sunzhu Li
,
Xiuqing Lu
,
Benyou Wang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.15379
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.15379
In the era of deep learning, word embeddings are essential when dealing with text tasks. However, storing and accessing these embeddings requires a large amount of space. This is not conducive to the deployment of these models on resource-limited devices. Combining the powerful compression capability of tensor products, we propose a word embedding compression method with morphological augmentation, Morphologically-enhanced Tensorized Embeddings (MorphTE). A word consists of one or more morphemes, the smallest units that bear meaning or have a grammatical function. MorphTE represents a word embedding as an entangled form of its morpheme vectors via the tensor product, which injects prior semantic and grammatical knowledge into the learning of embeddings. Furthermore, the dimensionality of the morpheme vector and the number of morphemes are much smaller than those of words, which greatly reduces the parameters of the word embeddings. We conduct experiments on tasks such as machine translation and question answering. Experimental results on four translation datasets of different languages show that MorphTE can compress word embedding parameters by about 20 times without performance loss and significantly outperforms related embedding compression methods.
Related Topics
Concepts
Morpheme
Embedding
Computer science
Word (group theory)
Natural language processing
Artificial intelligence
Word embedding
Curse of dimensionality
Translation (biology)
Tensor (intrinsic definition)
Space (punctuation)
Mathematics
Pure mathematics
Chemistry
Biochemistry
Gene
Operating system
Messenger RNA
Geometry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2210.15379
- https://arxiv.org/pdf/2210.15379
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307536877
All OpenAlex metadata
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https://openalex.org/W4307536877Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2210.15379Digital Object Identifier
- Title
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MorphTE: Injecting Morphology in Tensorized EmbeddingsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-10-27Full publication date if available
- Authors
-
Guobing Gan, Peng Zhang, Sunzhu Li, Xiuqing Lu, Benyou WangList of authors in order
- Landing page
-
https://arxiv.org/abs/2210.15379Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2210.15379Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2210.15379Direct OA link when available
- Concepts
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Morpheme, Embedding, Computer science, Word (group theory), Natural language processing, Artificial intelligence, Word embedding, Curse of dimensionality, Translation (biology), Tensor (intrinsic definition), Space (punctuation), Mathematics, Pure mathematics, Chemistry, Biochemistry, Gene, Operating system, Messenger RNA, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.methods. | 180 |
| abstract_inverted_index.morpheme | 92, 115 |
| abstract_inverted_index.powerful | 42 |
| abstract_inverted_index.product, | 97 |
| abstract_inverted_index.question | 148 |
| abstract_inverted_index.requires | 21 |
| abstract_inverted_index.semantic | 101 |
| abstract_inverted_index.smallest | 71 |
| abstract_inverted_index.Combining | 40 |
| abstract_inverted_index.accessing | 18 |
| abstract_inverted_index.conducive | 30 |
| abstract_inverted_index.different | 157 |
| abstract_inverted_index.embedding | 52, 85, 165, 178 |
| abstract_inverted_index.entangled | 88 |
| abstract_inverted_index.essential | 9 |
| abstract_inverted_index.function. | 80 |
| abstract_inverted_index.knowledge | 104 |
| abstract_inverted_index.languages | 158 |
| abstract_inverted_index.learning, | 5 |
| abstract_inverted_index.morphemes | 121 |
| abstract_inverted_index.products, | 47 |
| abstract_inverted_index.(MorphTE). | 61 |
| abstract_inverted_index.Embeddings | 60 |
| abstract_inverted_index.Tensorized | 59 |
| abstract_inverted_index.answering. | 149 |
| abstract_inverted_index.capability | 44 |
| abstract_inverted_index.deployment | 33 |
| abstract_inverted_index.embeddings | 7, 20 |
| abstract_inverted_index.morphemes, | 69 |
| abstract_inverted_index.parameters | 133, 166 |
| abstract_inverted_index.represents | 82 |
| abstract_inverted_index.compression | 43, 53, 179 |
| abstract_inverted_index.embeddings. | 109, 137 |
| abstract_inverted_index.experiments | 140 |
| abstract_inverted_index.grammatical | 79, 103 |
| abstract_inverted_index.outperforms | 176 |
| abstract_inverted_index.performance | 172 |
| abstract_inverted_index.translation | 146, 154 |
| abstract_inverted_index.Experimental | 150 |
| abstract_inverted_index.Furthermore, | 110 |
| abstract_inverted_index.augmentation, | 57 |
| abstract_inverted_index.morphological | 56 |
| abstract_inverted_index.significantly | 175 |
| abstract_inverted_index.dimensionality | 112 |
| abstract_inverted_index.resource-limited | 38 |
| abstract_inverted_index.Morphologically-enhanced | 58 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |