It is a well-known fact that machine translation systems, especially those that use deep learning, require massive amounts of data. Several resources for languages are not available in their human-created format. Some of the types of resources available are monolingual, multilingual, translation memories, and lexicons. Those types of resources are generally created for formal purposes such as parliamentary collections when parallel and more informal situations when monolingual. The quality and abundance of resources including corpora used for formal reasons is generally higher than those used for informal purposes. Additionally, corpora for low-resource languages, languages with less digital resources available, tends to be less abundant and of lower quality.
CoCo4MT sets out to be the first workshop centered around research that focuses on corpora creation, cleansing, and augmentation techniques specifically for machine translation. We accept work that covers any spoken language (including high-resource languages) but we are specifically interested in those submissions that are on languages with limited existing resources (low-resource languages) where resources are not highly available.
The goal of this workshop is to begin to close the gap between corpora available for low-resource translation systems and promote high-quality data for online systems that can be used by native speakers of low-resource languages is of particular interest. Therefore, It will be beneficial if the techniques presented in research papers include their impact on the quality of MT output and how they can be used in the real world.
CoCo4MT aims to encourage research on new and undiscovered techniques. We hope that submissions will provide high-quality corpora that is available publicly for download and can be used to increase machine translation performance thus encouraging new dataset creation for multiple languages that will, in turn, provide a general workshop to consult for corpora needs in the future. The workshop’s success will be measured by the following key performance indicators:
- Promotes the ongoing increase in quality of machine translation systems when measured by standard measurements,
- Provides a meeting place for collaboration from several research areas to increase the availability of commonly used corpora and new corpora,
- Drives innovation to address the need for higher quality and abundance of low-resource language data.