Articles

AbstractStatistical Machine Translation (SMT) accuracy degrades when there is only a limited amount of training, or when the training is not from the same domain or genre of text as the target application. However, cross-domain applications are typical of many real world tasks. We demonstrate that SMT accuracy can be …...

AbstractInflected languages in a low-resource setting present a data sparsity problem for statistical machine translation. In this paper, we present a minimally supervised algorithm for morpheme segmentation on Arabic dialects which reduces unknown words at translation time by over 50%, total vocabulary size by over 40%, and yields a significant …...

AbstractBasque is both a minority and a highly inflected language with free order of sentence constituents. Machine Translation of Basque is thus both a real need and a test bed for MT techniques. In this paper, we present a modular Data-Driven MT system which includes different chunkers as well as …...

AbstractWe propose and evaluate a new paradigm for machine translation of low resource languages via the learned surface transduction and paraphrase of multilingual glosses....

AbstractTransBooster is a wrapper technology designed to improve the performance of wide-coverage machine translation systems. Using linguistically motivated syntactic information, it automatically decomposes source language sentences into shorter and syntactically simpler chunks, and recomposes their translation to form target language sentences. This generally improves both the word order and lexical …...

AbstractThis paper presents our study of exploiting morpho-syntactic information for phrase-based statistical machine translation (SMT). For morphological transformation, we use hand-crafted transformational rules. For syntactic transformation, we propose a transformational model based on Bayes’ formula. The model is trained using a bilingual corpus and a broad coverage parser of the …...

AbstractThe more expressive and flexible a base formalism for machine translation is, the less efficient parsing of it will be. However, even among formalisms with the same parse complexity, some formalisms better realize the desired characteristics for machine translation formalisms than others. We introduce a particular formalism, probabilistic synchronous tree-insertion …...

AbstractThis paper evaluates the hypothesis that pictorial representations can be used to effectively convey simple sentences across language barriers. Comparative evaluations show that a considerable amount of understanding can be achieved using visual descriptions of information, with evaluation figures within a comparable range of those obtained with linguistic representations produced …...

AbstractTranslation adequacy is defined as the amount of semantic content from the source language document that is conveyed in the target language document. As such, it is more difficult to measure than intelligibility since semantic content must be measured in two documents and then compared. Latent Semantic Analysis is a …...

AbstractIn this paper, we present a novel approach to combine the outputs of multiple MT engines into a consensus translation. In contrast to previous Multi-Engine Machine Translation (MEMT) techniques, we do not rely on word alignments of output hypotheses, but prepare the input sentence for multi-engine processing. We do this …...

AbstractThis paper shows the applicability of language testing techniques to machine translation (MT) evaluation through one of a set of related experiments. One straightforward experiment is to use language testing exams and scoring on MT output with little or no adaptation. This paper describes one such experiment, the first in …...

AbstractState-of-the-art statistical machine translation is based on alignments between phrases – sequences of words in the source and target sentences. The learning step in these systems often relies on alignments between words. It is often assumed that the quality of this word alignment is critical for translation. However, recent results …...

AbstractLexical mappings (word translations) between languages are an invaluable resource for multilingual processing. While the problem of extracting lexical mappings from parallel corpora is well-studied, the task is more challenging when the language samples are from non-parallel corpora. The goal of this work is to investigate one such scenario: finding …...

AbstractWe present observations from three exercises designed to map the effective listening and speaking skills of an operator of a speech-to-speech translation system (S2S) to the Interagency Language Roundtable (ILR) scale. Such a mapping is non-trivial, but will be useful for government and military decision makers in managing expectations of …...

AbstractIn syntax-directed translation, the source-language input is first parsed into a parse-tree, which is then recursively converted into a string in the target-language. We model this conversion by an extended tree-to-string transducer that has multi-level trees on the source-side, which gives our system more expressive power and flexibility. We also …...

AbstractThis paper reports on an experiment investigating how effective free online machine translation (MT) is in helping Internet users to access the contents of websites written only in languages they do not know. This study explores the extent to which using Internet-based MT tools affects the confidence of web-surfers in …...

AbstractAs an approach to syntax based statistical machine translation (SMT), Probabilistic Synchronous Dependency Insertion Grammars (PSDIG), introduced in (Ding and Palmer, 2005), are a version of synchronous grammars defined on dependency trees. In this paper we discuss better learning and decoding algorithms for a PSDIG MT system. We introduce two …...

AbstractAccurately translating multiword expressions is important to obtain good performance in machine translation, cross-language information retrieval, and other multilingual tasks in human language technology. Existing approaches to inducing translation equivalents of multiword units have focused on agglomerating individual words or on aligning words in a statistical machine translation system. We …...

AbstractContext-Based Machine TranslationTM (CBMT) is a new paradigm for corpus-based translation that requires no parallel text. Instead, CBMT relies on a lightweight translation model utilizing a fullform bilingual dictionary and a sophisticated decoder using long-range context via long n-grams and cascaded overlapping. The translation process is enhanced via in-language substitution …...

AbstractThe Joint Probability Model proposed by Marcu and Wong (2002) provides a probabilistic framework for modeling phrase-based statistical machine transla- tion (SMT). The model’s usefulness is, however, limited by the computational complexity of estimating parameters at the phrase level. We present a method of constraining the search space of the …...