Tuesday 12pm, 27 January 2015
Mixed-Initiative Natural Language Translation
- Stanford University
There are two classical applications of the automatic translation of natural language. Assimilation is translation when a gist of the meaning is sufficient, and speed and convenience are prioritized. Dissemination is translation with the intent to communicate, so there is usually a predefined quality threshold. The most common assimilation scenario is cross-lingual web browsing, where fully automatic machine translation (MT) best satisfies the speed and convenience requirements. Dissemination is the setting for professional translators, who produce translations with the intent to communicate. MT output does not yet come with quality guarantees, so it is best incorporated as an assistive technology in this setting.
In this talk we present a mixed-initiative approach to translation for the dissemination scenario. In a mixed-initiative system, human users and intelligent machine agents collaborate to complete some task. The central question is how to design an efficient human/machine interface. By efficient we mean that human productivity should be enhanced, and the machine should be able to self-correct its model by observing human interactions.
Spence Green recently finished his Ph.D in Computer Science at Stanford University. He worked with Chris Manning and Jeff Heer and was a member of the Stanford NLP Group and the UW Interactive Data Lab. He's currently interested in the intersection of NLP and HCI. He has also worked on syntactic parsing, machine translation, and coreference resolution.