Watermarking the Outputs of Structured Prediction with an application in Statistical Machine Translation.
Ashish Venugopal, Jakob Uszkoreit, David Talbot, Franz Josef Och, Juri Ganitkevitch · 2011
We propose a general method to watermark and probabilistically identify the structured outputs of machine learning algorithms. Our method is robust to local editing operations and provides well defined trade-offs between the ability to identify algorithm outputs and the quality of the watermarked output. Unlike previous work in the field, our approach does not rely on controlling the inputs to the algorithm and provides probabilistic guarantees on the ability to identify collections of results from one’s own algorithm. We present an application in statistical machine translation, where machine translated output is watermarked at minimal loss in translation quality and detected with high recall. and human generated translations are currently both found extensively on the web, with no automatic way of distinguishing between them. Algorithms that mine data from the web (Uszkoreit et al., 2010), with the goal of learning to simulate human behavior, will now learn models from this contaminated and potentially selfgenerated data, reinforcing the errors committed by earlier versions of the algorithm. It is beneficial to be able to identify a set of encountered structured results as having been generated by one’s own algorithm, with the purpose of filtering such results when building new models. Problem Statement: We define a structured result of a query q as r = {z1 · · · zL} where the order and identity of elements zi are important to the quality of the result r. The structural aspect of the result implies the existence of alter-1