Using a mixture of n-best lists from multiple MT systems in rank-sum-based confidence measure for MT outputs

This paper addressees the problem of eliminating unsatisfactory outputs from machine translation (MT) systems. The authors intend to eliminate unsatisfactory MT outputs by using confidence measures. Confidence measures for MT outputs include the rank-sum-based confidence measure (RSCM) for statistical machine translation (SMT) systems. RSCM can be applied to non-SMT systems but does not always work well on them. This paper proposes an alternative RSCM that adopts a mixture of the N-best lists from multiple MT systems instead of a single-system’s N-best list in the existing RSCM. In most cases, the proposed RSCM proved to work better than the existing RSCM on two non-SMT systems and to work as well as the existing RSCM on an SMT system.

Neural Network and Bioinformatic Methods for Predicting HIV-1 Protease Inhibitor Resistance

This article presents a new method for predicting viral resistance to seven protease inhibitors from the HIV-1 genotype, and for identifying the positions in the protease gene at which the specific nature of the mutation affects resistance. The neural network Analog ARTMAP predicts protease inhibitor resistance from viral genotypes. A feature selection method detects genetic positions that contribute to resistance both alone and through interactions with other positions. This method has identified positions 35, 37, 62, and 77, where traditional feature selection methods have not detected a contribution to resistance. At several positions in the protease gene, mutations confer differing degress of resistance, depending on the specific amino acid to which the sequence has mutated. To find these positions, an Amino Acid Space is introduced to represent genes in a vector space that captures the functional similarity between amino acid pairs. Feature selection identifies several new positions, including 36, 37, and 43, with amino acid-specific contributions to resistance. Analog ARTMAP networks applied to inputs that represent specific amino acids at these positions perform better than networks that use only mutation locations.Air Force Office of Scientific Research (F49620-01-1-0423); National Geospatial-Intelligence Agency (NMA 201-01-1-2016); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

On the effective deployment of current machine translation technology

Machine translation is a fundamental technology that is gaining more importance each day in our multilingual society. Companies and particulars are turning their attention to machine translation since it dramatically cuts down their expenses on translation and interpreting. However, the output of current machine translation systems is still far from the quality of translations generated by human experts. The overall goal of this thesis is to narrow down this quality gap by developing new methodologies and tools that improve the broader and more efficient deployment of machine translation technology. We start by proposing a new technique to improve the quality of the translations generated by fully-automatic machine translation systems. The key insight of our approach is that different translation systems, implementing different approaches and technologies, can exhibit different strengths and limitations. Therefore, a proper combination of the outputs of such different systems has the potential to produce translations of improved quality. We present minimum Bayes¿ risk system combination, an automatic approach that detects the best parts of the candidate translations and combines them to generate a consensus translation that is optimal with respect to a particular performance metric. We thoroughly describe the formalization of our approach as a weighted ensemble of probability distributions and provide efficient algorithms to obtain the optimal consensus translation according to the widespread BLEU score. Empirical results show that the proposed approach is indeed able to generate statistically better translations than the provided candidates. Compared to other state-of-the-art systems combination methods, our approach reports similar performance not requiring any additional data but the candidate translations. Then, we focus our attention on how to improve the utility of automatic translations for the end-user of the system. Since automatic translations are not perfect, a desirable feature of machine translation systems is the ability to predict at run-time the quality of the generated translations. Quality estimation is usually addressed as a regression problem where a quality score is predicted from a set of features that represents the translation. However, although the concept of translation quality is intuitively clear, there is no consensus on which are the features that actually account for it. As a consequence, quality estimation systems for machine translation have to utilize a large number of weak features to predict translation quality. This involves several learning problems related to feature collinearity and ambiguity, and due to the ¿curse¿ of dimensionality. We address these challenges by adopting a two-step training methodology. First, a dimensionality reduction method computes, from the original features, the reduced set of features that better explains translation quality. Then, a prediction model is built from this reduced set to finally predict the quality score. We study various reduction methods previously used in the literature and propose two new ones based on statistical multivariate analysis techniques. More specifically, the proposed dimensionality reduction methods are based on partial least squares regression. The results of a thorough experimentation show that the quality estimation systems estimated following the proposed two-step methodology obtain better prediction accuracy that systems estimated using all the original features. Moreover, one of the proposed dimensionality reduction methods obtained the best prediction accuracy with only a fraction of the original features. This feature reduction ratio is important because it implies a dramatic reduction of the operating times of the quality estimation system. An alternative use of current machine translation systems is to embed them within an interactive editing environment where the system and a human expert collaborate to generate error-free translations. This interactive machine translation approach have shown to reduce supervision effort of the user in comparison to the conventional decoupled post-edition approach. However, interactive machine translation considers the translation system as a passive agent in the interaction process. In other words, the system only suggests translations to the user, who then makes the necessary supervision decisions. As a result, the user is bound to exhaustively supervise every suggested translation. This passive approach ensures error-free translations but it also demands a large amount of supervision effort from the user. Finally, we study different techniques to improve the productivity of current interactive machine translation systems. Specifically, we focus on the development of alternative approaches where the system becomes an active agent in the interaction process. We propose two different active approaches. On the one hand, we describe an active interaction approach where the system informs the user about the reliability of the suggested translations. The hope is that this information may help the user to locate translation errors thus improving the overall translation productivity. We propose different scores to measure translation reliability at the word and sentence levels and study the influence of such information in the productivity of an interactive machine translation system. Empirical results show that the proposed active interaction protocol is able to achieve a large reduction in supervision effort while still generating translations of very high quality. On the other hand, we study an active learning framework for interactive machine translation. In this case, the system is not only able to inform the user of which suggested translations should be supervised, but it is also able to learn from the user-supervised translations to improve its future suggestions. We develop a value-of-information criterion to select which automatic translations undergo user supervision. However, given its high computational complexity, in practice we study different selection strategies that approximate this optimal criterion. Results of a large scale experimentation show that the proposed active learning framework is able to obtain better compromises between the quality of the generated translations and the human effort required to obtain them. Moreover, in comparison to a conventional interactive machine translation system, our proposal obtained translations of twice the quality with the same supervision effort.González Rubio, J. (2014). On the effective deployment of current machine translation technology [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37888TESI

Comparative Quality Estimation for Machine Translation. An Application of Artificial Intelligence on Language Technology using Machine Learning of Human Preferences

In this thesis we focus on Comparative Quality Estimation, as the automaticprocess of analysing two or more translations produced by a Machine Translation(MT) system and expressing a judgment about their comparison. We approach theproblem from a supervised machine learning perspective, with the aim to learnfrom human preferences. As a result, we create the ranking mechanism, a pipelinethat includes the necessary tasks for ordering several MT outputs of a givensource sentence in terms of relative quality. Quality Estimation models are trained to statistically associate the judgmentswith some qualitative features. For this purpose, we design a broad set offeatures with a particular focus on the ones with a grammatical background.Through an iterative feature engineering process, we investigate several featuresets, we conclude to the ones that achieve the best performance and we proceedto linguistically intuitive observations about the contribution of individualfeatures. Additionally, we employ several feature selection and machine learning methodsto take advantage of these features. We suggest the usage of binary classifiersafter decomposing the ranking into pairwise decisions. In order to reduce theamount of uncertain decisions (ties) we weight the pairwise decisions with theirclassification probability. Through a set of experiments, we show that the ranking mechanism can learn andreproduce rankings that correlate to the ones given by humans. Most importantly,it can be successfully compared with state-of-the-art reference-aware metricsand other known ranking methods for several language pairs. We also apply thismethod for a hybrid MT system combination and we show that it is able to improvethe overall translation performance. Finally, we examine the correlation between common MT errors and decoding eventsof the phrase-based statistical MT systems. Through evidence from the decodingprocess, we identify some cases where long-distance grammatical phenomena cannotbe captured properly. An additional outcome of this thesis is the open source software Qualitative,which implements the full pipeline of ranking mechanism and the systemcombination task. It integrates a multitude of state-of-the-art natural languageprocessing tools and can support the development of new models. Apart from theusage in experiment pipelines, it can serve as an application back-end for webapplications in real-use scenaria.In dieser Promotionsarbeit konzentrieren wir uns auf die vergleichende Qualitätsschätzung der Maschinellen Übersetzung als ein automatisches Verfahren zur Analyse von zwei oder mehr Übersetzungen, die von Maschinenübersetzungssysteme erzeugt wurden, und zur Beurteilung von deren Vergleich. Wir gehen an das Problem aus der Perspektive des überwachten maschinellen Lernens heran, mit dem Ziel, von menschlichen Präferenzen zu lernen. Als Ergebnis erstellen wir einen Ranking-Mechanismus. Dabei handelt es sich um eine Pipeline, welche die notwendigen Arbeitsschritte für die Anordnung mehrerer Maschinenübersetzungen eines bestimmten Quellsatzes in Bezug auf die relative Qualität umfasst. Qualitätsschätzungsmodelle werden so trainiert, dass Vergleichsurteile mit einigen bestimmten Merkmalen statistisch verknüpft werden. Zu diesem Zweck konzipieren wir eine breite Palette von Merkmalen mit besonderem Fokus auf diejenigen mit einem grammatikalischen Hintergrund. Mit Hilfe eines iterativen Verfahrens der Merkmalskonstruktion untersuchen wir verschiedene Merkmalsreihen, erschließen diejenigen, die die beste Leistung erzielen, und leiten linguistisch motivierte Beobachtungen über die Beiträge der einzelnen Merkmale ab. Zusätzlich setzen wir verschiedene Methoden des maschinellen Lernens und der Merkmalsauswahl ein, um die Vorteile dieser Merkmale zu nutzen. Wir schlagen die Verwendung von binären Klassifikatoren nach Zerlegen des Rankings in paarweise Entscheidungen vor. Um die Anzahl der unklaren Entscheidungen (Unentschieden) zu verringern, gewichten wir die paarweisen Entscheidungen mit deren Klassifikationswahrscheinlichkeit. Mithilfe einer Reihe von Experimenten zeigen wir, dass der Ranking-Mechanismus Rankings lernen und reproduzieren kann, die mit denen von Menschen übereinstimmen. Die wichtigste Erkenntnis ist, dass der Mechanismus erfolgreich mit referenzbasierten Metriken und anderen bekannten Ranking-Methoden auf dem neusten Stand der Technik für verschiedene Sprachpaare verglichen werden kann. Diese Methode verwenden wir ebenfalls für eine hybride Systemkombination maschineller Übersetzer und zeigen, dass sie in der Lage ist, die gesamte Übersetzungsleistung zu verbessern. Abschließend untersuchen wir den Zusammenhang zwischen häufig vorkommenden Fehlern der maschinellen Übersetzung und Vorgängen, die während des internen Dekodierungsverfahrens der phrasenbasierten statistischen Maschinenübersetzungssysteme ablaufen. Durch Beweise aus dem Dekodierungsverfahren können wir einige Fälle identifizieren, in denen grammatikalische Phänomene mit Fernabhängigkeit nicht richtig erfasst werden können. Ein weiteres Ergebnis dieser Arbeit ist die quelloffene Software ``Qualitative'', welche die volle Pipeline des Ranking-Mechanismus und das System für die Kombinationsaufgabe implementiert. Die Software integriert eine Vielzahl modernster Softwaretools für die Verarbeitung natürlicher Sprache und kann die Entwicklung neuer Modelle unterstützen. Sie kann sowohl in Experimentierpipelines als auch als Anwendungs-Backend in realen Nutzungsszenarien verwendet werden

Beyond the Low-Degree Algorithm: Mixtures of Subcubes and Their Applications

We introduce the problem of learning mixtures of $k$ subcubes over $\{0,1\}^n$, which contains many classic learning theory problems as a special case (and is itself a special case of others). We give a surprising $n^{O(\log k)}$-time learning algorithm based on higher-order multilinear moments. It is not possible to learn the parameters because the same distribution can be represented by quite different models. Instead, we develop a framework for reasoning about how multilinear moments can pinpoint essential features of the mixture, like the number of components. We also give applications of our algorithm to learning decision trees with stochastic transitions (which also capture interesting scenarios where the transitions are deterministic but there are latent variables). Using our algorithm for learning mixtures of subcubes, we can approximate the Bayes optimal classifier within additive error $\epsilon$ on $k$-leaf decision trees with at most $s$ stochastic transitions on any root-to-leaf path in $n^{O(s + \log k)}\cdot\text{poly}(1/\epsilon)$ time. In this stochastic setting, the classic Occam algorithms for learning decision trees with zero stochastic transitions break down, while the low-degree algorithm of Linial et al. inherently has a quasipolynomial dependence on $1/\epsilon$. In contrast, as we will show, mixtures of $k$ subcubes are uniquely determined by their degree $2 \log k$ moments and hence provide a useful abstraction for simultaneously achieving the polynomial dependence on $1/\epsilon$ of the classic Occam algorithms for decision trees and the flexibility of the low-degree algorithm in being able to accommodate stochastic transitions. Using our multilinear moment techniques, we also give the first improved upper and lower bounds since the work of Feldman et al. for the related but harder problem of learning mixtures of binary product distributions.Comment: 62 pages; to appear in STOC 201

An investigation into weighted data fusion for content-based multimedia information retrieval

Content Based Multimedia Information Retrieval (CBMIR) is characterised by the combination of noisy sources of information which, in unison, are able to achieve strong performance. In this thesis we focus on the combination of ranked results from the independent retrieval experts which comprise a CBMIR system through linearly weighted data fusion. The independent retrieval experts are low-level multimedia features, each of which contains an indexing function and ranking algorithm. This thesis is comprised of two halves. In the first half, we perform a rigorous empirical investigation into the factors which impact upon performance in linearly weighted data fusion. In the second half, we leverage these finding to create a new class of weight generation algorithms for data fusion which are capable of determining weights at query-time, such that the weights are topic dependent