Three heads are better than one

Frederking, Robert E.; Ниренбург, Сергей

doi:10.3115/974358.974380

Cited by 56 publications

(29 citation statements)

References 5 publications

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“…In the competitive combination mode (b), the three translation engines separately translate the input sentence or the components of the input sentence, and the system then selects the best of the three results as the output (Frederking, and Nirenburg 1994;Nomoto 2003Nomoto , 2004. Several studies have shown that the overall performance of the competitive arrangement can in fact, as hoped, be better than that of the best participating MT engine (Hogan and Frederking 1998;Cavar, et al 2000;Akiba et al 2002).…”

Section: Mt and The Usermentioning

confidence: 99%

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Mees

Dragsted²,

Hansen³

et al. 2013

Target

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This paper argues that the time is now right to field practical Spoken Language Translation (SLT) systems. Several sorts of practical systems can be built over the next few years if system builders recognize that, at the present state of the art, users must cooperate and compromise with the programs. Further, SLT systems can be arranged on a scale, in terms of the degree of cooperation or compromise they require from users. In general, the broader the intended linguistic or topical coverage of a system, the more user cooperation or compromise it will presently require. The paper briefly discusses the component technologies of SLT systems as they relate to user cooperation and accommodation ("human factors engineering"), with examples from the authors' work. It describes three classes of "cooperative" SLT systems which could be put into practical use during the next few years.

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Section: Mt and The Usermentioning

confidence: 99%

Sound effects in translation

Mees

Dragsted²,

Hansen³

et al. 2013

Target

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“…System combination has enjoyed great success in many domains, such as automatic speech recognition (Fiscus, 1997;Mangu et al, 2000), machine translation (Frederking and Nirenburg, 1994;Bangalore et al, 2001) and parsing (Henderson and Brill, 1999;Sagae and Lavie, 2006). However, only a handful of papers have leveraged this idea for summarization.…”

Section: Related Workmentioning

confidence: 99%

System Combination for Multi-document Summarization

Hong

Marcus

Nenkova

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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We present a novel framework of system combination for multi-document summarization.For each input set (input), we generate candidate summaries by combining whole sentences from the summaries generated by different systems. We show that the oracle among these candidates is much better than the summaries that we have combined. We then present a supervised model to select among the candidates. The model relies on a rich set of features that capture content importance from different perspectives. Our model performs better than the systems that we combined based on manual and automatic evaluations. We also achieve very competitive performance on six DUC/TAC datasets, comparable to the state-of-the-art on most datasets.

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“…In one of the first publications on system combination in MT, Frederking and Nirenburg [13] create a chart structure where target language phrases from each system are placed according to their corresponding source phrases, together with their confidence scores. A chart-walk algorithm is used to select the best translation from the chart.…”

Section: Related Workmentioning

confidence: 99%

System Combination for Machine Translation of Spoken and Written Language

Matusov

Leusch

Banchs

et al. 2008

IEEE Trans. Audio Speech Lang. Process.

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Abstract-This paper describes an approach for computing a consensus translation from the outputs of multiple machine translation (MT) systems. The consensus translation is computed by weighted majority voting on a confusion network, similarly to the well-established ROVER approach of Fiscus for combining speech recognition hypotheses. To create the confusion network, pairwise word alignments of the original MT hypotheses are learned using an enhanced statistical alignment algorithm that explicitly models word reordering. The context of a whole corpus of automatic translations rather than a single sentence is taken into account in order to achieve high alignment quality. The confusion network is rescored with a special language model, and the consensus translation is extracted as the best path. The proposed system combination approach was evaluated in the framework of the TC-STAR speech translation project. Up to six state-of-the-art statistical phrase-based translation systems from different project partners were combined in the experiments. Significant improvements in translation quality from Spanish to English and from English to Spanish in comparison with the best of the individual MT systems were achieved under official evaluation conditions.

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Three heads are better than one

Cited by 56 publications

References 5 publications

Sound effects in translation

Sound effects in translation

System Combination for Multi-document Summarization

System Combination for Machine Translation of Spoken and Written Language

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