A three-pass system combination framework by combining multiple hypothesis alignment methods

So far, many effective hypothesis alignment metrics have been proposed and applied to the system combination, such as TER, HMM, ITER and IHMM. In addition, the Minimum Bayes-risk (MBR) decoding and the confusion network (CN) have become the state-of-the art techniques in system combination. In this paper, we present a three-pass system combination strategy that can combine hypothesis alignment results derived from different alignment metrics to generate a better translation. Firstly the different alignment metrics are carried out to align the backbone and hypotheses, and the individual CN is built corresponding to each alignment results; then we construct a super network by merging the multiple metric-based CN and generate a consensus output. Finally a modified consensus network MBR (ConMBR) approach is employed to search a best translation. Our proposed strategy out performs the best single CN as well as the best single system in our experiments on NIST Chinese-to-English test set

An incremental three-pass system combination framework by combining multiple hypothesis alignment methods

System combination has been applied successfully to various machine translation tasks in recent years. As is known, the hypothesis alignment method is a critical factor for the translation quality of system combination. To date, many effective hypothesis alignment metrics have been proposed and applied to the system combination, such as TER, HMM, ITER, IHMM, and SSCI. In addition, Minimum Bayes-risk (MBR) decoding and confusion networks (CN) have become state-of-the-art techniques in system combination. In this paper, we examine different hypothesis alignment approaches and investigate how much the hypothesis alignment results impact on system combination, and finally present a three-pass system combination strategy that can combine hypothesis alignment results derived from multiple alignment metrics to generate a better translation. Firstly, these different alignment metrics are carried out to align the backbone and hypotheses, and the individual CNs are built corresponding to each set of alignment results; then we construct a ‘super network’ by merging the multiple metric-based CNs to generate a consensus output. Finally a modified MBR network approach is employed to find the best overall translation. Our proposed strategy outperforms the best single confusion network as well as the best single system in our experiments on the NIST Chinese-to-English test set and the WMT2009 English-to-French system combination shared test set

Aligning Neural Machine Translation Models: Human Feedback in Training and Inference

Reinforcement learning from human feedback (RLHF) is a recent technique to improve the quality of the text generated by a language model, making it closer to what humans would generate. A core ingredient in RLHF's success in aligning and improving large language models (LLMs) is its reward model, trained using human feedback on model outputs. In machine translation (MT), where metrics trained from human annotations can readily be used as reward models, recent methods using minimum Bayes risk decoding and reranking have succeeded in improving the final quality of translation. In this study, we comprehensively explore and compare techniques for integrating quality metrics as reward models into the MT pipeline. This includes using the reward model for data filtering, during the training phase through RL, and at inference time by employing reranking techniques, and we assess the effects of combining these in a unified approach. Our experimental results, conducted across multiple translation tasks, underscore the crucial role of effective data filtering, based on estimated quality, in harnessing the full potential of RL in enhancing MT quality. Furthermore, our findings demonstrate the effectiveness of combining RL training with reranking techniques, showcasing substantial improvements in translation quality.Comment: 14 pages, work-in-progres

Accuracy of MAP segmentation with hidden Potts and Markov mesh prior models via Path Constrained Viterbi Training, Iterated Conditional Modes and Graph Cut based algorithms

In this paper, we study statistical classification accuracy of two different Markov field environments for pixelwise image segmentation, considering the labels of the image as hidden states and solving the estimation of such labels as a solution of the MAP equation. The emission distribution is assumed the same in all models, and the difference lays in the Markovian prior hypothesis made over the labeling random field. The a priori labeling knowledge will be modeled with a) a second order anisotropic Markov Mesh and b) a classical isotropic Potts model. Under such models, we will consider three different segmentation procedures, 2D Path Constrained Viterbi training for the Hidden Markov Mesh, a Graph Cut based segmentation for the first order isotropic Potts model, and ICM (Iterated Conditional Modes) for the second order isotropic Potts model. We provide a unified view of all three methods, and investigate goodness of fit for classification, studying the influence of parameter estimation, computational gain, and extent of automation in the statistical measures Overall Accuracy, Relative Improvement and Kappa coefficient, allowing robust and accurate statistical analysis on synthetic and real-life experimental data coming from the field of Dental Diagnostic Radiography. All algorithms, using the learned parameters, generate good segmentations with little interaction when the images have a clear multimodal histogram. Suboptimal learning proves to be frail in the case of non-distinctive modes, which limits the complexity of usable models, and hence the achievable error rate as well. All Matlab code written is provided in a toolbox available for download from our website, following the Reproducible Research Paradigm

Probabilistic Inference for Phrase-based Machine Translation: A Sampling Approach

Recent advances in statistical machine translation (SMT) have used dynamic programming (DP) based beam search methods for approximate inference within probabilistic translation models. Despite their success, these methods compromise the probabilistic interpretation of the underlying model thus limiting the application of probabilistically defined decision rules during training and decoding. As an alternative, in this thesis, we propose a novel Monte Carlo sampling approach for theoretically sound approximate probabilistic inference within these models. The distribution we are interested in is the conditional distribution of a log-linear translation model; however, often, there is no tractable way of computing the normalisation term of the model. Instead, a Gibbs sampling approach for phrase-based machine translation models is developed which obviates the need of computing this term yet produces samples from the required distribution. We establish that the sampler effectively explores the distribution defined by a phrase-based models by showing that it converges in a reasonable amount of time to the desired distribution, irrespective of initialisation. Empirical evidence is provided to confirm that the sampler can provide accurate estimates of expectations of functions of interest. The mix of high probability and low probability derivations obtained through sampling is shown to provide a more accurate estimate of expectations than merely using the n-most highly probable derivations. Subsequently, we show that the sampler provides a tractable solution for finding the maximum probability translation in the model. We also present a unified approach to approximating two additional intractable problems: minimum risk training and minimum Bayes risk decoding. Key to our approach is the use of the sampler which allows us to explore the entire probability distribution and maintain a strict probabilistic formulation through the translation pipeline. For these tasks, sampling allies the simplicity of n-best list approaches with the extended view of the distribution that lattice-based approaches benefit from, while avoiding the biases associated with beam search. Our approach is theoretically well-motivated and can give better and more stable results than current state of the art methods