Influenced by its training corpus,the performance of different machine translation systems varies greatly.Aiming at achieving higher quality translations,system combination methods combine the translation results of m...Influenced by its training corpus,the performance of different machine translation systems varies greatly.Aiming at achieving higher quality translations,system combination methods combine the translation results of multiple systems through statistical combination or neural network combination.This paper proposes a new multi-system translation combination method based on the Transformer architecture,which uses a multi-encoder to encode source sentences and the translation results of each system in order to realize encoder combination and decoder combination.The experimental verification on the Chinese-English translation task shows that this method has 1.2-2.35 more bilingual evaluation understudy(BLEU)points compared with the best single system results,0.71-3.12 more BLEU points compared with the statistical combination method,and 0.14-0.62 more BLEU points compared with the state-of-the-art neural network combination method.The experimental results demonstrate the effectiveness of the proposed system combination method based on Transformer.展开更多
We describe a novel approach to Bayes risk(BR) decoding for speech recognition,in which we attempt to find the hypothesis that minimizes an estimate of the BR with regard to the minimum word error(MWE) metric.To achie...We describe a novel approach to Bayes risk(BR) decoding for speech recognition,in which we attempt to find the hypothesis that minimizes an estimate of the BR with regard to the minimum word error(MWE) metric.To achieve this,we propose improved forward and backward algorithms on the lattices and the whole procedure is optimized recursively.The remarkable characteristics of the proposed approach are that the optimization procedure is expectation-maximization(EM) like and the formation of the updated result is similar to that obtained with the confusion network(CN) decoding method.Experimental results indicated that the proposed method leads to an error reduction for both lattice rescoring and lattice-based system combinations,compared with CN decoding,confusion network combination(CNC),and ROVER methods.展开更多
基金Supported by the National Key Research and Development Program of China(No.2019YFA0707201)the Fund of the Institute of Scientific and Technical Information of China(No.ZD2021-17).
文摘Influenced by its training corpus,the performance of different machine translation systems varies greatly.Aiming at achieving higher quality translations,system combination methods combine the translation results of multiple systems through statistical combination or neural network combination.This paper proposes a new multi-system translation combination method based on the Transformer architecture,which uses a multi-encoder to encode source sentences and the translation results of each system in order to realize encoder combination and decoder combination.The experimental verification on the Chinese-English translation task shows that this method has 1.2-2.35 more bilingual evaluation understudy(BLEU)points compared with the best single system results,0.71-3.12 more BLEU points compared with the statistical combination method,and 0.14-0.62 more BLEU points compared with the state-of-the-art neural network combination method.The experimental results demonstrate the effectiveness of the proposed system combination method based on Transformer.
文摘We describe a novel approach to Bayes risk(BR) decoding for speech recognition,in which we attempt to find the hypothesis that minimizes an estimate of the BR with regard to the minimum word error(MWE) metric.To achieve this,we propose improved forward and backward algorithms on the lattices and the whole procedure is optimized recursively.The remarkable characteristics of the proposed approach are that the optimization procedure is expectation-maximization(EM) like and the formation of the updated result is similar to that obtained with the confusion network(CN) decoding method.Experimental results indicated that the proposed method leads to an error reduction for both lattice rescoring and lattice-based system combinations,compared with CN decoding,confusion network combination(CNC),and ROVER methods.
