Experiments in Morphosyntactic Processing for Translating to and from German
By Alexander M. Fraser. 2009
25 citations recorded by the source corpus.
We describe two shared task systems and associated experiments. The German to English system used reordering rules applied to parses and morphological splitting and stemming. The English to German system used an additional translation step which recreated compound words and generated morphological inflection.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Philipp Koehn
- Chris Callison-Burch
- Alexander M. Fraser
- Christof Monz
- Ondrej Dusek
- Filip Jurvc'ivcek
- Holger Schwenk
- Josh Schroeder
In Natural Language Processing
- Philipp Koehn
- Ondrej Dusek
- Alexander M. Fraser
- Christof Monz
- Chris Callison-Burch
- Filip Jurvc'ivcek
- Nadir Durrani
- Lucia Specia
Citations in the corpus
- Neural Generation for Czech: Data and Baselines (2019, positive): “This approach is inspired by similar approaches in phrase-based MT (Bojar, 2007; Toutanova et al., 2008; Fraser, 2009) and was developed in parallel to recent similar experiments with two-step neural MT (Nadejde et al.”
- Findings of the 2009 Workshop on Statistical Machine Translation (2009, background): “, 2009) STUTTGART University of Stuttgart (Fraser, 2009) SYSTRAN Systran (Dugast et al.”
- CimS – The CIS and IMS joint submission to WMT 2014 translating from English into German (2014, background): “Collins et al. (2005), Fraser (2009) and Gojun and Fraser (2012) showed that restructuring the source language so that it corresponds to the expected structure of the target language is helpful for SMT.”