000 | 03074nam a22005055i 4500 | ||
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001 | 978-981-32-9748-7 | ||
003 | DE-He213 | ||
005 | 20240423125113.0 | ||
007 | cr nn 008mamaa | ||
008 | 190826s2019 si | s |||| 0|eng d | ||
020 |
_a9789813297487 _9978-981-32-9748-7 |
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024 | 7 |
_a10.1007/978-981-32-9748-7 _2doi |
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050 | 4 | _aQA76.9.N38 | |
072 | 7 |
_aUYQL _2bicssc |
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_aCOM073000 _2bisacsh |
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_aUYQL _2thema |
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082 | 0 | 4 |
_a006.35 _223 |
100 | 1 |
_aCheng, Yong. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aJoint Training for Neural Machine Translation _h[electronic resource] / _cby Yong Cheng. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2019. |
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300 |
_aXIII, 78 p. 23 illus., 9 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aSpringer Theses, Recognizing Outstanding Ph.D. Research, _x2190-5061 |
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505 | 0 | _a1. Introduction -- 2. Neural Machine Translation -- 3. Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation -- 4. Semi-supervised Learning for Neural Machine Translation -- 5. Joint Training for Pivot-based Neural Machine Translation -- 6. Joint Modeling for Bidirectional Neural Machine Translation with Contrastive Learning -- 7. Related Work -- 8. Conclusion. | |
520 | _aThis book presents four approaches to jointly training bidirectional neural machine translation (NMT) models. First, in order to improve the accuracy of the attention mechanism, it proposes an agreement-based joint training approach to help the two complementary models agree on word alignment matrices for the same training data. Second, it presents a semi-supervised approach that uses an autoencoder to reconstruct monolingual corpora, so as to incorporate these corpora into neural machine translation. It then introduces a joint training algorithm for pivot-based neural machine translation, which can be used to mitigate the data scarcity problem. Lastly it describes an end-to-end bidirectional NMT model to connect the source-to-target and target-to-source translation models, allowing the interaction of parameters between these two directional models. | ||
650 | 0 | _aNatural language processing (Computer science). | |
650 | 0 | _aLogic programming. | |
650 | 1 | 4 | _aNatural Language Processing (NLP). |
650 | 2 | 4 | _aLogic in AI. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789813297470 |
776 | 0 | 8 |
_iPrinted edition: _z9789813297494 |
830 | 0 |
_aSpringer Theses, Recognizing Outstanding Ph.D. Research, _x2190-5061 |
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856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-32-9748-7 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c174285 _d174285 |