We present an adaptive translation quality estimation (QE) method to predict the human-targeted translation error rate (HTER) for a document-specific machine translation model.We first introduce features derived internal to the translation decoding process as well as externally from the source sentence analysis.We show the effectiveness of such features in both classification and regression of MT quality.By dynamically training the QE model for the document-specific MT model, we are able to achieve consistency and prediction quality across multiple documents, demonstrated by the higher correlation coefficient and F-scores in finding Good sentences.Additionally, the proposed method is applied to IBM English-to-Japanese MT post editing field study and we observe strong correlation with human preference, with a 10% increase in human translators' productivity.

The paper describes a user oriented performance evaluation measure for text segmentation. Experiments show that the proposed measure differentiates well between error distributions with varying user impact.

The paper describes a maximum entropy based story segmentation system for Arabic, Chinese and English. In experiments with broadcast news data from TDT-3, TDT-4, and corpora collected in the DARPA GALE project we obtain a substantial performance gain using multiple overlapping windows for text-based features.