Transition based Graph Decoder for Neural Machine Translation.
Year: 2021
Type: preprint
Abstract: While a number of works showed gains from incorporating source-side symbolic syntactic and semantic structure into neural machine translation (NMT), much fewer works addressed the decoding of such structure.
We propose a general Transformer-based approach for tree and graph decoding based on generating a sequence of transitions, inspired by a similar approach that uses RNNs by Dyer (2016).
Experiments with using the proposed decoder with Universal Dependencies syntax on English-German, German-English and English-Russian show improved performance over the standard Transformer decoder, as well as over ablated versions of the model.\tacltxt{\footnote{All code implementing the presented models will be released upon acceptance.
Source: arXiv (Cornell University)
Authors: Leshem Choshen, Omri Abend
Institution: Hebrew University of Jerusalem
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3127943074