Modern Automatic Speech Recognition (ASR) systems can achieve high performance in
terms of recognition accuracy. However, a perfectly accurate transcript still can be …

In a Data-Centric AI paradigm, the model performance is enhanced without altering the
model architecture, as evidenced by real-world and benchmark dataset demonstrations …

Machine translation quality estimation (QE) refers to the quality assessment of machine
translations without a given reference translation. Supervised QE models based on neural …

Recent studies have attempted to understand natural language and infer answers. Machine
reading comprehension is one of the representatives, and several related datasets have …

Data-centric AI approach aims to enhance the model performance without modifying the
model and has been shown to impact model performance positively. While recent attention …

Data building for automatic post-editing (APE) requires extensive and expert-level human
effort, as it contains an elaborate process that involves identifying errors in sentences and …

Automatic post-editing (APE) is a crucial technique for enhancing the quality of machine
translations. In this thesis, we present an APE approach specifically for English-Hindi …

Para combatir los ataques de ejemplos adversarios, se propuso implementar un modelo de
reconocimiento de palabras y entrenarlo con oraciones creadas a través de diferentes …