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010   $a981-329-748-4
024 7 $a10.1007/978-981-32-9748-7
035   $a(CKB)4100000009076254
035   $a(MiAaPQ)EBC5883402
035   $a(DE-He213)978-981-32-9748-7
035   $a(PPN)258304693
035   $a(EXLCZ)994100000009076254
100   $a20190826d2019      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aJoint Training for Neural Machine Translation /$fby Yong Cheng
205   $a1st ed. 2019.
210  1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2019.
215   $a1 online resource (90 pages)
225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
311   $a981-329-747-6 
327   $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.
330   $aThis book presents four approaches to jointly training bidirectional neural machine translation (NMT) models. First, in order to improve the accuracy of the attention mechanism, it proposes an agreement-based joint training approach to help the two complementary models agree on word alignment matrices for the same training data. Second, it presents a semi-supervised approach that uses an autoencoder to reconstruct monolingual corpora, so as to incorporate these corpora into neural machine translation. It then introduces a joint training algorithm for pivot-based neural machine translation, which can be used to mitigate the data scarcity problem. Lastly it describes an end-to-end bidirectional NMT model to connect the source-to-target and target-to-source translation models, allowing the interaction of parameters between these two directional models.
410  0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
606   $aNatural language processing (Computer science)
606   $aArtificial intelligence
606   $aComputer logic
606   $aNatural Language Processing (NLP)$3https://scigraph.springernature.com/ontologies/product-market-codes/I21040
606   $aLogic in AI$3https://scigraph.springernature.com/ontologies/product-market-codes/I21020
615  0$aNatural language processing (Computer science)
615  0$aArtificial intelligence.
615  0$aComputer logic.
615 14$aNatural Language Processing (NLP).
615 24$aLogic in AI.
676   $a418.020285
700   $aCheng$b Yong$4aut$4http://id.loc.gov/vocabulary/relators/aut$0781861
906   $aBOOK
912   $a9910349303003321
996   $aJoint Training for Neural Machine Translation$92537335
997   $aUNINA