Incremental Hypothesis Alignment for Building Confusion Networks with Application to Machine Translation System Combination
نویسندگان
چکیده
Confusion network decoding has been the most successful approach in combining outputs from multiple machine translation (MT) systems in the recent DARPA GALE and NIST Open MT evaluations. Due to the varying word order between outputs from different MT systems, the hypothesis alignment presents the biggest challenge in confusion network decoding. This paper describes an incremental alignment method to build confusion networks based on the translation edit rate (TER) algorithm. This new algorithm yields significant BLEU score improvements over other recent alignment methods on the GALE test sets and was used in BBN’s submission to the WMT08 shared translation task.
منابع مشابه
Review of Hypothesis Alignment Algorithms for MT System Combination via Confusion Network Decoding
Confusion network decoding has proven to be one of the most successful approaches to machine translation system combination. The hypothesis alignment algorithm is a crucial part of building the confusion networks and many alternatives have been proposed in the literature. This paper describes a systematic comparison of five well known hypothesis alignment algorithms for MT system combination vi...
متن کاملIncremental Hypothesis Alignment with Flexible Matching for Building Confusion Networks: BBN System Description for WMT09 System Combination Task
This paper describes the incremental hypothesis alignment algorithm used in the BBN submissions to the WMT09 system combination task. The alignment algorithm used a sentence specific alignment order, flexible matching, and new shift heuristics. These refinements yield more compact confusion networks compared to using the pair-wise or incremental TER alignment algorithms. This should reduce the ...
متن کاملAn Incremental Three-pass System Combination Framework by Combining Multiple Hypothesis Alignment Methods
System combination has been applied successfully to various machine translation tasks in recent years. As is known, the hypothesis alignment method is a critical factor for the translation quality of system combination. To date, many effective hypothesis alignment metrics have been proposed and applied to the system combination, such as TER, HMM, ITER, IHMM, and SSCI. In addition, Minimum Bayes...
متن کاملImproving Alignments for Better Confusion Networks for Combining Machine Translation Systems
The state-of-the-art system combination method for machine translation (MT) is the word-based combination using confusion networks. One of the crucial steps in confusion network decoding is the alignment of different hypotheses to each other when building a network. In this paper, we present new methods to improve alignment of hypotheses using word synonyms and a two-pass alignment strategy. We...
متن کاملImproving Alignment of System Combination by Using Multi-objective Optimization
This paper proposes a multi-objective optimization framework which supports heterogeneous information sources to improve alignment in machine translation system combination techniques. In this area, most of techniques usually utilize confusion networks (CN) as their central data structure to compact an exponential number of an potential hypotheses, and because better hypothesis alignment may be...
متن کامل