Conference Paper
An Alignment Algorithm Using Belief Propagation and a StructureBased Distortion Model.
Conference: EACL 2009, 12th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, March 30  April3, 2009, Athens, Greece
Source: DBLP

Conference Paper: Unsupervised Word Alignment with Arbitrary Features.
[Show abstract] [Hide abstract]
ABSTRACT: We introduce a discriminatively trained, globally normalized, loglinear variant of the lexical translation models proposed by Brown et al. (1993). In our model, arbitrary, nonindependent features may be freely incorporated, thereby overcoming the inherent limitation of generative models, which require that features be sensitive to the conditional independencies of the generative process. However, unlike previous work on discriminative modeling of word alignment (which also permits the use of arbitrary features), the parameters in our models are learned from unannotated parallel sentences, rather than from supervised word alignments. Using a variety of intrinsic and extrinsic measures, including translation performance, we show our model yields better alignments than generative baselines in a number of language pairs.The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 1924 June, 2011, Portland, Oregon, USA; 01/2011 
Conference Paper: Graphical Models over Multiple Strings.
[Show abstract] [Hide abstract]
ABSTRACT: We study graphical modeling in the case of string valued random variables. Whereas a weighted finitestate transducer can model the probabilis tic relationship between two strings, we are inter ested in building up joint models of three or more strings. This is needed for inflectional paradigms in morphology, cognate modeling or language re construction, and multiplestring alignment. We propose a Markov Random Field in which each factor (potential function) is a weighted finitestate machine, typically a transducer that evaluates the relationship between just two of the strings. The full joint distribution is then a product of these fac tors. Though decoding is actually undecidable in general, we can still do efficient joint inference using approximate belief propagation; the nec essary computations and messages are all finite state. We demonstrate the methods by jointly pre dicting morphological forms. 1 OverviewProceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, 67 August 2009, Singapore, A meeting of SIGDAT, a Special Interest Group of the ACL; 01/2009 
Conference Paper: ModelBased Aligner Combination Using Dual Decomposition.
[Show abstract] [Hide abstract]
ABSTRACT: Unsupervised word alignment is most often modeled as a Markov process that generates a sentence f conditioned on its translation e. A similar model generating e from f will make different alignment predictions. Statistical machine translation systems combine the predictions of two directional models, typically using heuristic combination procedures like growdiagfinal. This paper presents a graphical model that embeds two directional aligners into a single model. Inference can be performed via dual decomposition, which reuses the efficient inference algorithms of the directional models. Our bidirectional model enforces a onetoone phrase constraint while accounting for the uncertainty in the underlying directional models. The resulting alignments improve upon baseline combination heuristics in wordlevel and phraselevel evaluations.The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 1924 June, 2011, Portland, Oregon, USA; 01/2011
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.