Greg Hanneman

Machine Translation System Combination with Flexible Word Ordering

We describe a synthetic method for combining machine translations produced by different systems given the same input. One-best outputs are explicitly aligned to remove duplicate words. Hypotheses follow system outputs in sentence order, switching between systems mid-sentence to produce a combined output. Experiments with the WMT 2009 tuning data showed improvement of 2 BLEU and 1 METEOR point over the best Hungarian-English system. Constrained to data provided by the contest, our system was submitted to the WMT 2009 shared system combination task.

Decoding with Syntactic and Non-Syntactic Phrases in a Syntax-Based Machine Translation System

A key concern in building syntax-based machine translation systems is how to improve coverage by incorporating more traditional phrase-based SMT phrase pairs that do not correspond to syntactic constituents. At the same time, it is desirable to include as much syntactic information in the system as possible in order to carry out linguistically motivated reordering, for example. We apply an extended and modified version of the approach of Tinsley et al. (2007), extracting syntax-based phrase pairs from a large parallel parsed corpus, combining them with PBSMT phrases, and performing joint decoding in a syntax-based MT framework without loss of translation quality. This effectively addresses the low coverage of purely syntactic MT without discarding syntactic information. Further, we show the potential for improved translation results with the inclusion of a syntactic grammar. We also introduce a new syntax-prioritized technique for combining syntactic and non-syntactic phrases that reduces overall phrase table size and decoding time by 61%, with only a minimal drop in automatic translation metric scores.

An Improved Statistical Transfer System for French-English Machine Translation

This paper presents the Carnegie Mellon University statistical transfer MT system submitted to the 2009 WMT shared task in French-to-English translation. We describe a syntax-based approach that incorporates both syntactic and non-syntactic phrase pairs in addition to a syntactic grammar. After reporting development test results, we conduct a preliminary analysis of the coverage and effectiveness of the systems components.

Statistical Transfer Systems for French-English and German-English Machine Translation

We apply the Stat-XFER statistical transfer machine translation framework to the task of translating from French and German into English. We introduce statistical methods within our framework that allow for the principled extraction of syntax-based transfer rules from parallel corpora given word alignments and constituency parses. Performance is evaluated on test sets from the 2007 WMT shared task.

The CMU Machine Translation Systems at WMT 2014

We describe the CMU systems submitted to the 2014 WMT shared translation task. We participated in two language pairs, German–English and Hindi–English. Our innovations include: a label coarsening scheme for syntactic tree-to-tree translation, a host of new discriminative features, several modules to create “synthetic translation options” that can generalize beyond what is directly observed in the training data, and a method of combining the output of multiple word aligners to uncover extra phrase pairs and grammar rules.