Add to ORKGConfusion network decoding has been the most successful approach in combining out-puts from multiple machine translation (MT) systems in the recent DARPA GALE and NIST Open MT evaluations. Due to the vary-ing word order between outputs from differ-ent MT systems, the hypothesis alignment presents the biggest challenge in confusion network decoding. This paper describes an incremental alignment method to build confu-sion 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.
This paper proposes a multi-objective opti-mization framework which supports heteroge-neous informat...
So far, many effective hypothesis alignment metrics have been proposed and applied to the system com...
This paper describes an approach for computing a consensus translation from the outputs of multiple ...
Confusion network decoding has been the most successful approach in combining out-puts from multiple...
Recently confusion network decoding shows the best performance in combining outputs from multiple ma...
The state-of-the-art system combination method for machine translation (MT) is the word-based combin...
Recently, confusion network decoding has been applied in machine translation system combination. Due...
Abstract The state-of-the-art system combination method for machine translation (MT) is the word-bas...
System combination has been applied successfully to various machine translation tasks in recent year...
This paper describes the incremental hy-pothesis alignment algorithm used in the BBN submissions to ...
This paper presents a new hypothesis alignment method for combining outputs of multiple machine tran...
This paper describes a novel method for computing a consensus translation from the outputs of multip...
This paper describes a recently developed method for computing a consensus translation from the outp...
Inspired by the incremental TER align-ment, we re-designed the Indirect HMM (IHMM) alignment, which ...
Machine translation is a task in the field of natural language processing whose objective is to tran...
This paper proposes a multi-objective opti-mization framework which supports heteroge-neous informat...
So far, many effective hypothesis alignment metrics have been proposed and applied to the system com...
This paper describes an approach for computing a consensus translation from the outputs of multiple ...
Confusion network decoding has been the most successful approach in combining out-puts from multiple...
Recently confusion network decoding shows the best performance in combining outputs from multiple ma...
The state-of-the-art system combination method for machine translation (MT) is the word-based combin...
Recently, confusion network decoding has been applied in machine translation system combination. Due...
Abstract The state-of-the-art system combination method for machine translation (MT) is the word-bas...
System combination has been applied successfully to various machine translation tasks in recent year...
This paper describes the incremental hy-pothesis alignment algorithm used in the BBN submissions to ...
This paper presents a new hypothesis alignment method for combining outputs of multiple machine tran...
This paper describes a novel method for computing a consensus translation from the outputs of multip...
This paper describes a recently developed method for computing a consensus translation from the outp...
Inspired by the incremental TER align-ment, we re-designed the Indirect HMM (IHMM) alignment, which ...
Machine translation is a task in the field of natural language processing whose objective is to tran...
This paper proposes a multi-objective opti-mization framework which supports heteroge-neous informat...
So far, many effective hypothesis alignment metrics have been proposed and applied to the system com...
This paper describes an approach for computing a consensus translation from the outputs of multiple ...