Combining Evidence from Homologous Datasets

摘要

With Machine Translation and/or Automatic Speech Recognition, there can be different versions of the same data with distinct expressions. We argue that combining evidence from these 'homologous' datasets can give us better representation of the original data, and our experiments show that a model combining all sources outperforms each individual dataset in retrieval.