Unsupervised Clustering of Commercial Domains for Adaptive Machine Translation

In this paper, we report on domain clustering in the ambit of an adaptive MT architecture. A standard bottom-up hierarchical clustering algorithm has been instantiated with five different distances, which have been compared, on an MT benchmark built on 40 commercial domains, in terms of dendrograms,...

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Bibliographic Details
Published in:arXiv.org 2016-12
Main Authors: Cettolo, Mauro, Mara Chinea Rios, Cattoni, Roldano
Format: Article
Language:English
Subjects:
Algorithms
Cluster analysis
Clustering
Domains
Machine translation
Online Access:Get full text
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Summary:In this paper, we report on domain clustering in the ambit of an adaptive MT architecture. A standard bottom-up hierarchical clustering algorithm has been instantiated with five different distances, which have been compared, on an MT benchmark built on 40 commercial domains, in terms of dendrograms, intrinsic and extrinsic evaluations. The main outcome is that the most expensive distance is also the only one able to allow the MT engine to guarantee good performance even with few, but highly populated clusters of domains.
ISSN:2331-8422