Examining Scaling and Transfer of Language Model Architectures for Machine Translation Article Swipe

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Biao Zhang , Behrooz Ghorbani , Ankur Bapna , Yong Cheng , Xavier García , Jonathan Shen , Orhan Fırat ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2202.00528

Natural language understanding and generation models follow one of the two dominant architectural paradigms: language models (LMs) that process concatenated sequences in a single stack of layers, and encoder-decoder models (EncDec) that utilize separate layer stacks for input and output processing. In machine translation, EncDec has long been the favoured approach, but with few studies investigating the performance of LMs. In this work, we thoroughly examine the role of several architectural design choices on the performance of LMs on bilingual, (massively) multilingual and zero-shot translation tasks, under systematic variations of data conditions and model sizes. Our results show that: (i) Different LMs have different scaling properties, where architectural differences often have a significant impact on model performance at small scales, but the performance gap narrows as the number of parameters increases, (ii) Several design choices, including causal masking and language-modeling objectives for the source sequence, have detrimental effects on translation quality, and (iii) When paired with full-visible masking for source sequences, LMs could perform on par with EncDec on supervised bilingual and multilingual translation tasks, and improve greatly on zero-shot directions by facilitating the reduction of off-target translations.

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Computer Science

Machine Translation

Process (Computing)

Artificial Intelligence

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Computer science Machine translation Translation (biology) Language model Masking (illustration) Scaling Stack (abstract data type) Encoder Speech recognition Process (computing) Natural language processing Artificial intelligence Programming language Biochemistry Chemistry Art Gene Geometry Messenger RNA Mathematics Visual arts Operating system

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2202.00528
PDF: https://arxiv.org/pdf/2202.00528
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Cited By: 2
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2202.00528

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Title: Examining Scaling and Transfer of Language Model Architectures for Machine Translation

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Language: en

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Publication year: 2022

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Publication date: 2022-02-01

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Authors: Biao Zhang, Behrooz Ghorbani, Ankur Bapna, Yong Cheng, Xavier García, Jonathan Shen, Orhan Fırat

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Landing page: https://arxiv.org/abs/2202.00528

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Concepts: Computer science, Machine translation, Translation (biology), Language model, Masking (illustration), Scaling, Stack (abstract data type), Encoder, Speech recognition, Process (computing), Natural language processing, Artificial intelligence, Programming language, Biochemistry, Chemistry, Art, Gene, Geometry, Messenger RNA, Mathematics, Visual arts, Operating system

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Cited by: 2

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abstract_inverted_index.objectives	140
abstract_inverted_index.off-target	186
abstract_inverted_index.paradigms:	13
abstract_inverted_index.parameters	129
abstract_inverted_index.sequences,	160
abstract_inverted_index.supervised	169
abstract_inverted_index.systematic	87
abstract_inverted_index.thoroughly	64
abstract_inverted_index.variations	88
abstract_inverted_index.(massively)	80
abstract_inverted_index.detrimental	146
abstract_inverted_index.differences	108
abstract_inverted_index.performance	57, 75, 116, 122
abstract_inverted_index.processing.	40
abstract_inverted_index.properties,	105
abstract_inverted_index.significant	112
abstract_inverted_index.translation	84, 149, 173
abstract_inverted_index.concatenated	19
abstract_inverted_index.facilitating	182
abstract_inverted_index.full-visible	156
abstract_inverted_index.multilingual	81, 172
abstract_inverted_index.translation,	43
abstract_inverted_index.architectural	12, 70, 107
abstract_inverted_index.investigating	55
abstract_inverted_index.translations.	187
abstract_inverted_index.understanding	2
abstract_inverted_index.encoder-decoder	28
abstract_inverted_index.language-modeling	139
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	7
sustainable_development_goals[0].id	https://metadata.un.org/sdg/4
sustainable_development_goals[0].score	0.8399999737739563
sustainable_development_goals[0].display_name	Quality Education
citation_normalized_percentile