Natural language processing employs computational techniques for the purpose of learning,
understanding, and producing human language content. Early computational approaches to …

Increasingly, algorithms are used to make important decisions across society. However,
these algorithms are usually poorly understood, which can reduce transparency and evoke …

Much contemporary rhetoric regards the prospects and pitfalls of using artificial intelligence
techniques to automate an increasing range of tasks, especially those once considered the …

Although large language models (LLMs) have demonstrated impressive potential on simple
tasks, their breadth of scope, lack of transparency, and insufficient controllability can make …

Large-scale generative models enabled the development of AI-powered code completion
tools to assist programmers in writing code. However, much like other AI-powered tools, AI …

This chapter analyses the evolutionary process that human post-editing of Machine
Translation (MT) output has undergone in previous years. It is well known that post-editing …

Despite the promising results achieved in last years by statistical machine translation, and
more precisely, by the neural machine translation systems, this technology is still not error …

Recent graphical interfaces offer direct manipulation mechanisms for authoring
visualizations, but are largely restricted to static output. To author interactive visualizations …

While several studies report translator resistance to post-editing, translators whose work has
followed the evolution of translation technology may consider post-editing to be translation …

Neural machine translation systems require large amounts of training data and resources.
Even with this, the quality of the translations may be insufficient for some users or domains …