Název: Využití větné struktury v neuronovém strojovém překladu
Překlad názvu: Využití větné struktury v neuronovém strojovém překladu
Autoři: Pham, Thuong-Hai ; Bojar, Ondřej (vedoucí práce) ; Helcl, Jindřich (oponent)
Typ dokumentu: Diplomové práce
Rok: 2018
Jazyk: eng
Abstrakt: Neural machine translation has been lately established as the new state of the art in machine translation, especially with the Transformer model. This model emphasized the importance of self-attention mechanism and sug- gested that it could capture some linguistic phenomena. However, this claim has not been examined thoroughly, so we propose two main groups of meth- ods to examine the relation between these two. Our methods aim to im- prove the translation performance by directly manipulating the self-attention layer. The first group focuses on enriching the encoder with source-side syn- tax with tree-related position embeddings or our novel specialized attention heads. The second group is a joint translation and parsing model leveraging self-attention weight for the parsing task. It is clear from the results that enriching the Transformer with sentence structure can help. More impor- tantly, the Transformer model is in fact able to capture this type of linguistic information with guidance in the context of multi-task learning at nearly no increase in training costs. 1
Klíčová slova: attention machine translation dependency neural network; attention machine translation dependency neural network

Instituce: Fakulty UK (VŠKP) (web)
Informace o dostupnosti dokumentu: Dostupné v digitálním repozitáři UK.
Původní záznam: http://hdl.handle.net/20.500.11956/101647

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-387905


Záznam je zařazen do těchto sbírek:
Školství > Veřejné vysoké školy > Univerzita Karlova > Fakulty UK (VŠKP)
Vysokoškolské kvalifikační práce > Diplomové práce