the translator’s multilateral role as auser and processor of the source text as well as the producer of the targettext in today’s multi-cultural and multilingual communications has always drawnresearchers’ attention to examining translators’ actions and behavior includingthe translation process of  novicetranslators this study, outlined in the framework of descriptive translationstudies (dts), ...

The Multi-language Speech (MLS) Corpus supports NIST’s Language Recognition Evaluation series by providing new conversational telephone speech and broadcast narrowband data in 20 languages/dialects. The corpus was built with the intention of testing system performance in the matter of distinguishing closely related or confusable linguistic varieties, and careful manual auditing of collected dat...

Inter-annotator consistency is a concern for any corpus building effort relying on human annotation. Adjudication is as effective way to locate and correct discrepancies of various kinds. It can also be both difficult and time-consuming. This paper introduces Linguistic Data Consortium (LDC)’s model for decision point-based annotation and adjudication, and describes the annotation tools develop...

This paper proposes a method to automatically classify texts from different varieties of the same language. We show that similarity measure is a robust tool for studying comparable corpora of language variations. We take LDC’s Chinese Gigaword Corpus composed of three varieties of Chinese from Mainland China, Singapore, and Taiwan, as the comparable corpora. Top-bag-of-word similarity measures ...

There is an increasing interest in multimodal communication as suggested by several national and international projects (ISLE, HUMAINE, SIMILAR, CHIL, AMI, CALO, VACE, CALLAS), the attention devoted to the topic by well-known institutions and organizations (the National Institute of Standards and Technology, the Linguistic Data Consortium), and the success of conferences related to multimodal c...

In this paper, we describe the textual linguistic resources in nearly 3 dozen languages being produced by Linguistic Data Consortium for DARPA’s LORELEI (Low Resource Languages for Emergent Incidents) Program. The goal of LORELEI is to improve the performance of human language technologies for low-resource languages and enable rapid re-training of such technologies for new languages, with a foc...