Optimal Corpus Aware Training for Neural Machine Translation
Corpus Aware Training (CAT) leverages valuable corpus metadata during training by injecting corpus information into each training example, and has been found effective in the literature, commonly known as the “tagging” approach. Models trained with CAT inherently learn the quality, domain and nuance between corpora directly from data, and can easily switch to different inference behavior. To achieve the best evaluation, CAT models pre-define a group of high quality data before training starts which can be error-prone and inefficient. In this work, we propose Optimal Corpus Aware Training…
While Automatic Speech Recognition (ASR) systems are widely used in many real-world applications, they often do not generalize well to new domains and need to be finetuned on data from these domains. However, target-domain data is usually not readily available in many scenarios. In this paper, we propose a new…
Posted Grigor Aslanyan, Software Engineer, Google Patent documents typically use legal and highly technical language, with context-dependent terms that may have meanings quite different from colloquial usage and even between different documents. The process of using traditional patent search methods (e.g., keyword searching) to search through the corpus of over…
Video annotator: a framework for efficiently building video classifiers using vision-language models and active learningAmir Ziai, Aneesh Vartakavi, Kelli Griggs, Eugene Lok, Yvonne Jukes, Alex Alonso, Vi Iyengar, Anna Pulidohttps://medium.com/media/02a5bbf97c619182adba24b45e42edcb/hrefIntroductionProblemHigh-quality and consistent annotations are fundamental to the successful development of robust machine learning models. Conventional techniques for training machine learning classifiers are…