{"product_id":"joint-training-for-neural-machine-translation-von-yong-cheng","title":"Joint Training for Neural Machine Translation","description":"\n                                \n                \u003cp\u003e\u003c\/p\u003e\n                                \n                \u003cp\u003e\n                                        This 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.\n                    \n                    \u003cbr\u003e\n                                    \n                \u003c\/p\u003e\n                                \n                \u003cp\u003e\u003c\/p\u003e\n                            \n            \u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9789813297470\"\u003e\u003ch3\u003e\u003c\/h3\u003e\u003c\/div\u003e","brand":"Libri","offers":[{"title":"Hardcover - 9789813297470","offer_id":29367118135389,"sku":"9789813297470","price":53.49,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/d94b1e7d-5b74-4609-b0fe-6488149ce09c.jpg?v=1775623617","url":"https:\/\/shop.autorenwelt.de\/en\/products\/joint-training-for-neural-machine-translation-von-yong-cheng","provider":"Autorenwelt Shop","version":"1.0","type":"link"}