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An algorithm for cross-lingual sense-clustering tested in a MT evaluation setting

Apidianaki, Marianna and He, Yifan (2010) An algorithm for cross-lingual sense-clustering tested in a MT evaluation setting. In: The 7th International Workshop on Spoken Language Translation (IWSLT 2010), 2-3 December, Paris, France.

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Unsupervised sense induction methods offer a solution to the problem of scarcity of semantic resources. These methods automatically extract semantic information from textual data and create resources adapted to specific applications and domains of interest. In this paper, we present a clustering algorithm for cross-lingual sense induction which generates bilingual semantic inventories from parallel corpora. We describe the clustering procedure and the obtained resources. We then proceed to a large-scale evaluation by integrating the resources into a Machine Translation (MT) metric (METEOR). We show that the use of the data-driven sense-cluster inventories leads to better correlation with human judgments of translation quality, compared to precision-based metrics, and to improvements similar to those obtained when a handcrafted semantic resource is used.

Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Uncontrolled Keywords:machine translation metric; METEOR; parallel corpora
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Next Generation Localisation (CNGL)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:16414
Deposited On:01 Jul 2011 14:12 by Shane Harper. Last Modified 01 Jul 2011 14:12

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