Invited Talk: How Much Information Does a Human Translator Add to the Original?
Kevin K. Knight · DSpace repository (University of Tartu) · 2015
It is well-known that natural language has built-in redundancy. By using context, we can often guess the next word or character in a text. Two practical communities have independently exploited this fact. First, automatic speech and translation researchers build language models to distinguish fluent from non-fluent outputs. Second, text compression researchers convert predictions into short encodings, to save disk space and bandwidth. I will explore what these two communities can learn from each others’ (interestingly different) solutions. Then I will look at the less-studied question of redundancy in bilingual text, addressing questions like How well can we predict human translator behavior? and How much information does a human translator add to the original? (This is joint work with Barret Zoph and Marjan Ghazvininejad.)