Data Selection for IT Texts using Paragraph Vector

By Mirela-Stefania Duma, Wolfgang Menzel. 2016

6 citations recorded by the source corpus.

This paper presents an overview of the system submitted by the University of Hamburg to the IT domain shared translation task as part of the ACL 2016 First Conference of Machine Translation (WMT 2016). We have chosen data selection as a domain adaptation method. The filtering of the general domain data makes use of paragraph vectors as a novel approach for scoring the sentences. Experiments were conducted for English-German under the constrained condition.

Subfields: Artificial Intelligence, Natural Language Processing, Machine Translation

Citations in the corpus

• Unsupervised Domain Clusters in Pretrained Language Models (2020, unknown): “Duma and Menzel (2016) create paragraph vectors for data selection in the context of SMT. Wang et al. (2017) use internal representations from the NMT model to perform data selection.”