Diego Velazquez, Mikaela Grace, Konstantinos Karageorgos, Lawrence Carin, Aaron Schliem, Dimitrios Zaikis, Roger Wechsler. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

This paper discusses the capabilities and benefits of OPAL Enable, an advanced AI suite designed to modernize localization processes. The suite comprises Machine Translation, AI Post-Editing, and AI Quality Estimation tools, integrated into renowned translation management systems. The paper provides an in-depth analysis of these features, detailing their procedural order, and the time and cost savings they offer. It emphasizes the customization potential of OPAL Enable to meet client-specific requirements, increase scalability, and expedite workflows.

Multidialectal languages can pose challenges for acoustic modeling. Past research has shown that with a large training corpus but without explicit modeling of inter-dialect variability, training individual per-dialect models yields superior performance to that of a single model trained on the combined data [1, 2]. In this work, we were motivated by the idea that adaptation techniques can allow the models to learn dialect-independent features and in turn leverage the power of the larger training corpus sizes afforded when pooling data across dialects. Our goal was thus to create a single multidialect acoustic model that would rival the performance of the dialect-specific models.Working in the context of deep Long-Short Term Memory (LSTM) acoustic models trained on up to 40K hours of speech, we explored several methods for training and incorporating dialect-specific information into the model, including 12 variants of interpolation-of-bases techniques related to Cluster Adaptive Training (CAT) [3] and Factorized Hidden Layer (FHL) [4] techniques. We found that with our model topology and large training corpus, simply appending the dialect-specific information to the feature vector resulted in a more accurate model than any of the more complex interpolation-of-bases techniques, while requiring less model complexity and fewer parameters. This simple adaptation yielded a single unified model for all dialects that, in most cases, outperformed individual models which had been trained per-dialect.