Robust Guidance for Unsupervised Data Selection: Capturing Perplexing Named Entities for Domain-Specific Machine Translation
This addresses the problem of data selection for domain-specific machine translation in low-resource settings, though it appears incremental as it builds on prior work on data complexity.
The paper tackles the challenge of selecting training-efficient data for low-resource neural machine translation by introducing a method that uses maximum inference entropy in translated named entities as a metric, achieving robust guidance across domains in tests with a Korean-English specialized corpus.
Low-resourced data presents a significant challenge for neural machine translation. In most cases, the low-resourced environment is caused by high costs due to the need for domain experts or the lack of language experts. Therefore, identifying the most training-efficient data within an unsupervised setting emerges as a practical strategy. Recent research suggests that such effective data can be identified by selecting 'appropriately complex data' based on its volume, providing strong intuition for unsupervised data selection. However, we have discovered that establishing criteria for unsupervised data selection remains a challenge, as the 'appropriate level of difficulty' may vary depending on the data domain. We introduce a novel unsupervised data selection method named 'Capturing Perplexing Named Entities,' which leverages the maximum inference entropy in translated named entities as a metric for selection. When tested with the 'Korean-English Parallel Corpus of Specialized Domains,' our method served as robust guidance for identifying training-efficient data across different domains, in contrast to existing methods.