The aim of the ITRANS2 project is to develop an innovative computer-assisted system that will facilitate the production of high-quality speech transcriptions and text translations. To do this, ITRANS2 proposes a novel interactive-predictive approach which places a human operator at the centre of the process and embeds a statistical machine translation engine (for translation) or an automatic speech recogniser (for transcription) within an interactive editing environment. The human serves as the guarantor of high-quality; the role of the machine translation engine or automatic speech recogniser is to increase the operator’s productivity by predicting extensions to the current target text which the operator may then accept, correct or ignore. Interactivity allows the system to take advantage of the human-validated portion to improve the accuracy of subsequent predictions. Indeed, ITRANS2 offers a unique context in which to test new adaptive learning techniques: ultimately, our goal is to have the system learn from the operator’s corrections in order to dynamically update its underlying models.
The potential impact of the ITRANS2 project is considerable, both from a scientific and commercial point of view. ITRANS2 addresses a critical scientific problem that is currently hampering many natural language processing systems, namely, their inability to gracefully adapt to unforeseen circumstances and learn from their interaction with expert human users. The kind of adaptive, online learning techniques that will be developed in this project will therefore represent a significant advance to the state of the art. Moreover, in the longer term, these advances could also help a strained service industry cope with the growing demand for high-quality transcription and translation by increasing the contribution of state-of-the-art technologies to these socially and economically important market.