Parallel aligned treebanks (PAT) are linguistic corpora annotated with morphological and syntactic structures that are aligned at sentence as well as sub-sentence levels. They are valuable resources for improving machine translation (MT) quality. Recently, there has been an increasing demand for such data, especially for divergent language pairs. The Linguistic Data Consortium (LDC) and its academic partners have been developing Arabic-English and Chinese-English PATs for several years. This paper describes the PAT corpus creation effort for the program GALE (Global Autonomous Language Exploitation) and introduces the potential issues of scaling up this PAT effort for the program BOLT (Broad Operational Language Translation). Based on existing infrastructures and in the light of current annotation process, challenges and approaches, we are exploring new methodologies to address emerging challenges in constructing PATs, including data volume bottlenecks, dialect issues of Arabic languages, and new genre features related to rapidly changing social media. Preliminary experimental results are presented to show the feasibility of the approaches proposed.

We describe efforts to create corpora to support development and evaluation of handwriting recognition and translation technology. LDC has developed a stable pipeline and infrastructures for collecting and annotating handwriting linguistic resources to support the evaluation of MADCAT and OpenHaRT. We collect and annotate handwritten samples of pre-processed Arabic and Chinese data that has been already translated in English that is used in the GALE program. To date, LDC has recruited more than 600 scribes and collected, annotated and released more than 225,000 handwriting images. Most linguistic resources created for these programs will be made available to the larger research community by publishing in LDC's catalog. The phase 1 MADCAT corpus is now available.

The goal of the DARPA MADCAT (Multilingual Automatic Document Classification Analysis and Translation) Program is to automatically convert foreign language text images into English transcripts, for use by humans and downstream applications. The first phase the program focuses on translation of handwritten Arabic documents. Linguistic Data Consortium (LDC) is creating publicly available linguistic resources for MADCAT technologies, on a scale and richness not previously available. Corpora will consist of existing LDC corpora and data donations from MADCAT partners, plus new data collection to provide high quality material for evaluation and to address strategic gaps (for genre, dialect, image quality, etc.) in the existing resources. Training and test data properties will expand over time to encompass a wide range of topics and genres: letters, diaries, training manuals, brochures, signs, ledgers, memos, instructions, postcards and forms among others. Data will be ground truthed, with line, word and token segmentation and zoning, and translations and word alignments will be produced for a subset. Evaluation data will be carefully selected from the available data pools and high quality references will be produced, which can be used to compare MADCAT system performance against the human-produced gold standard.