Statistical machine translation (SMT) treats the translation of natural language as a machine learning problem. By examining many samples of human-produced translation, SMT algorithms automatically learn how to translate. SMT has made tremendous strides in less than two decades, and many popular techniques have only emerged within the last few years. This survey presents a tutorial overview of state-of-the-art SMT at the beginning of 2007. We begin with the context of the current research, and then move to a formal problem description and an overview of the four main subproblems: translational equivalence modeling, mathematical modeling, parameter estimation, and decoding. Along the way, we present a taxonomy of some different approaches within these areas. We conclude with an overview of evaluation and notes on future directions.

Tree Adjoining Grammars have well-known advantages, but are typically considered too difficult for practical systems. We demonstrate that, when done right, adjoining improves translation quality without becoming computationally intractable. Using adjoining to model optionality allows general translation patterns to be learned without the clutter of endless variations of optional material. The appropriate modifiers can later be spliced in as needed. In this paper, we describe a novel method for learning a type of Synchronous Tree Adjoining Grammar and associated probabilities from aligned tree/string training data. We introduce a method of converting these grammars to a weakly equivalent tree transducer for decoding. Finally, we show that adjoining results in an end-to-end improvement of +0.8 BLEU over a baseline statistical syntax-based MT model on a large-scale Arabic/English MT task. 1

We develop a new objective function for word alignment that measures the size of the bilingual dictionary induced by an alignment. A word alignment that results in a small dictionary is preferred over one that results in a large dictionary. In order to search for the alignment that minimizes this objective, we cast the problem as an integer linear program. We then extend our objective function to align corpora at the sub-word level, which we demonstrate on a small Turkish-English corpus. 1

Source languages with complex wordformation rules present a challenge for statistical machine translation (SMT). In this paper, we take on three facets of this challenge: (1) common stems are fragmented into many different forms in training data, (2) rare and unknown words are frequent in test data, and (3) spelling variation creates additional sparseness problems. We present a novel, lightweight technique for dealing with this fragmentation, based on bilingual data, and we also present a combination of linguistic and statistical techniques for dealing with rare and unknown words. Taking these techniques together, we demonstrate +1.3 and +1.6 BLEU increases on top of strong baselines for Arabic-English machine translation. 1

The best systems for machine translation of natural language are based on statistical models learned from data. Conventional representation of a statistical translation model requires substantial offline computation and representation in main memory. Therefore, the principal bottlenecks to the amount of data we can exploit and the complexity of models we can use are available memory and CPU time, and current state of the art already pushes these limits. With data size and model complexity continually increasing, a scalable solution to this problem is central to future improvement. Callison-Burch et al. (2005) and Zhang and Vogel (2005) proposed a solution that we call translation by pattern matching, which we bring to fruition in this dissertation. The training data itself serves as a proxy to the model; rules and parameters are computed on demand. It achieves our desiderata of minimal offline computation and compact representation, but is dependent on fast pattern matching algorithms on text. They demonstrated its application to a common model based on the translation of contiguous substrings, but leave some open problems. Among these is a question: can this approach match the performance of conventional methods despite unavoidable differences that it induces in the model? We show how to answer this question affirmatively. The main

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We are interested in learning something using both labeled and unlabeled data, or else we wouldn’t be at this workshop. The question I’d like to think about is: why do we want to do this? Is it: 1. because we think that adding a little labeled data to our pile of unlabeled data will help; or 2. because we think that adding a little unlabeled data to our pile of labeled data will help? Typical approaches in NLP to the unlabeled+labeled problem fall into one of these two categories. In the first case, we basically have some unsupervised learning system that we know does fairly well on its own, and we’re adding labeled data just to help it tweak things in a slightly better way. In the second case, we basically have some

Abstract—A fundamental data integration task faced by online commercial portals and commerce search engines is the integration of products coming from multiple providers to their product catalogs. In this scenario, the commercial portal has its own taxonomy (the “master taxonomy”), while each data provider organizes its products into a different taxonomy (the “provider taxonomy”). In this paper, we consider the problem of categorizing products from the data providers into the master taxonomy, while making use of the provider taxonomy information. Our approach is based on a taxonomy-aware processing step that adjusts the results of a text-based classifier to ensure that products that are close together in the provider taxonomy remain close in the master taxonomy. We formulate this intuition as a structured prediction optimization problem. To the best of our knowledge, this is the first approach that leverages the structure of taxonomies in order to enhance catalog integration. We propose algorithms that are scalable and thus applicable to the large datasets that are typical on the Web. We evaluate our algorithms on real-world data and we show that taxonomy-aware classification provides a significant improvement over existing approaches. Index Terms—catalog integration, classification, data mining, taxonomies.