An empirical comparison of stack-based decoding algorithms for statistical machine translation

Unlike other heuristic search algorithms, stack-based decoders have been proved theoretically to guarantee the avoidance of search errors in the decoding phase of a statistical machine translation (SMT) system. The disadvantage of the stack-based decoders are the high computational requirements. Therefore, to make the decoding problem feasible for SMT, some heuristic optimizations have to be performed. However, this yields unavoidable search errors. In this paper, we describe, study, and implement the state of the art stack-based decoding algorithms for SMT making an empirical comparison which focuses specifically on the optimization problems, computational time, and translation results. Results are also presented for two well known task, the TOURIST Task and the HANSARDS task.