Arabic language modeling

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  • In morphologically rich languages such as Arabic, the abundance of word forms resulting from increased morpheme combinations is significantly greater than for languages with fewer inflected forms (Kirchhoff et al., 2006). This exacerbates the out-of-vocabulary (OOV) problem. Test set words are more likely to be unknown, limiting the effectiveness of the model. The goal of this study is to use the regularities of Arabic inflectional morphology to reduce the OOV problem in that language.

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  • We approximate Arabic’s rich morphology by a model that a word consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build the Arabic word segmenter from a large unsegmented Arabic corpus. The algorithm uses a trigram language model to determine the most probable morpheme sequence for a given input.

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  • In this paper, we address statistical machine translation of public conference talks. Modeling the style of this genre can be very challenging given the shortage of available in-domain training data. We investigate the use of a hybrid LM, where infrequent words are mapped into classes. Hybrid LMs are used to complement word-based LMs with statistics about the language style of the talks. Extensive experiments comparing different settings of the hybrid LM are reported on publicly available benchmarks based on TED talks, from Arabic to English and from English to French.

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  • In this paper, we argue that n-gram language models are not sufficient to address word reordering required for Machine Translation. We propose a new distortion model that can be used with existing phrase-based SMT decoders to address those n-gram language model limitations. We present empirical results in Arabic to English Machine Translation that show statistically significant improvements when our proposed model is used. We also propose a novel metric to measure word order similarity (or difference) between any pair of languages based on word alignments. ...

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  • The Arabic language is a collection of spoken dialects with important phonological, morphological, lexical, and syntactic differences, along with a standard written language, Modern Standard Arabic (MSA). Since the spoken dialects are not officially written, it is very costly to obtain adequate corpora to use for training dialect NLP tools such as parsers. In this paper, we address the problem of parsing transcribed spoken Levantine Arabic (LA). We do not assume the existence of any annotated LA corpus (except for development and testing), nor of a parallel corpus LAMSA. ...

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  • This paper presents and evaluates several original techniques for the latent classification of biographic attributes such as gender, age and native language, in diverse genres (conversation transcripts, email) and languages (Arabic, English). First, we present a novel partner-sensitive model for extracting biographic attributes in conversations, given the differences in lexical usage and discourse style such as observed between same-gender and mixedgender conversations.

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  • We consider the problem of NER in Arabic Wikipedia, a semisupervised domain adaptation setting for which we have no labeled training data in the target domain. To facilitate evaluation, we obtain annotations for articles in four topical groups, allowing annotators to identify domain-specific entity types in addition to standard categories. Standard supervised learning on newswire text leads to poor target-domain recall.

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  • Arabic morphology is complex, partly because of its richness, and partly because of common irregular word forms, such as broken plurals (which resemble singular nouns), and nouns with irregular gender (feminine nouns that look masculine and vice versa). In addition, Arabic morphosyntactic agreement interacts with the lexical semantic feature of rationality, which has no morphological realization. In this paper, we present a series of experiments on the automatic prediction of the latent linguistic features of functional gender and number, and rationality in Arabic.

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  • We propose a bootstrapping approach to training a memoriless stochastic transducer for the task of extracting transliterations from an English-Arabic bitext. The transducer learns its similarity metric from the data in the bitext, and thus can function directly on strings written in different writing scripts without any additional language knowledge. We show that this bootstrapped transducer performs as well or better than a model designed specifically to detect Arabic-English transliterations. ...

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  • This research note reports on the work in progress which regards automatic transformation of phrase-structure syntactic trees of Arabic into dependency-driven analytical ones. Guidelines for these descriptions have been developed at the Linguistic Data Consortium, University of Pennsylvania, and at the Faculty of Mathematics and Physics and the Faculty of Arts, Charles University in Prague, respectively.

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  • Syntactic Reordering of the source language to better match the phrase structure of the target language has been shown to improve the performance of phrase-based Statistical Machine Translation. This paper applies syntactic reordering to English-to-Arabic translation. It introduces reordering rules, and motivates them linguistically. It also studies the effect of combining reordering with Arabic morphological segmentation, a preprocessing technique that has been shown to improve Arabic-English and EnglishArabic translation.

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  • Logical approaches to linguistic description, particularly those which employ feature structures, have generally treated phonology as though it was the same as orthography. This approach breaks down for languages where the phonological shape of a morpheme can be heavily dependent on the phonological shape of another, as is the case in Arabic. In this paper we show how the tense logical approach investigated by Blackburn (1989) can be used to encode hierarchical and temporal phonological information of the kind explored by Bird (1990). ...

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  • We propose a real-time machine translation system that allows users to select a news category and to translate the related live news articles from Arabic, Czech, Danish, Farsi, French, German, Italian, Polish, Portuguese, Spanish and Turkish into English.

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  • This paper describes a multi-lingual phrase-based Statistical Machine Translation system accessible by means of a Web page. The user can issue translation requests from Arabic, Chinese or Spanish into English. The same phrase-based statistical technology is employed to realize the three supported language-pairs. New language-pairs can be easily added to the demonstrator. The Web-based interface allows the use of the translation system to any computer connected to the Internet.

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  • We present a novel method for predicting inflected word forms for generating morphologically rich languages in machine translation. We utilize a rich set of syntactic and morphological knowledge sources from both source and target sentences in a probabilistic model, and evaluate their contribution in generating Russian and Arabic sentences. Our results show that the proposed model substantially outperforms the commonly used baseline of a trigram target language model; in particular, the use of morphological and syntactic features leads to large gains in prediction accuracy. ...

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  • We describe a prototype SK~RED CmAt~eAR for the syntax of simple nominal expressions in Arabic, E~IL~lx, French, German, and Japanese implemented at MCC. In this Oamm~', a complex inheritance ian/cc of shared gr~mmAtlcal templates provides pans that each language can put together to form lansuug~specific gramm-ti~tl templates. We conclude that grammar shsrin8 is not only possible but also desirable. It forces us to reveal crossliuguistically invm'iant grammatie~ primitives that may otherwise r e m ~ conflamd with other primitives if we deal only with a single ~.nousge or l-n~uuge type. ...

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  • We describe refinements to hierarchical translation search procedures intended to reduce both search errors and memory usage through modifications to hypothesis expansion in cube pruning and reductions in the size of the rule sets used in translation. Rules are put into syntactic classes based on the number of non-terminals and the pattern, and various filtering strategies are then applied to assess the impact on translation speed and quality. Results are reported on the 2008 NIST Arabic-toEnglish evaluation task. ...

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  • Prosodic Inheritance (PI) morphology provides uniform treatment of both concatenative and non-concatenative morphological and phonological generalisations using default inheritance. Models of an extensive range of German Umlaut and Arabic intercalation facts, implemented in DATR, show that the PI approach also covers 'hard cases' more homogeneously and more extensively than previous computational treatments.

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  • Word lattice decoding has proven useful in spoken language translation; we argue that it provides a compelling model for translation of text genres, as well. We show that prior work in translating lattices using finite state techniques can be naturally extended to more expressive synchronous context-free grammarbased models. Additionally, we resolve a significant complication that non-linear word lattice inputs introduce in reordering models. Our experiments evaluating the approach demonstrate substantial gains for ChineseEnglish and Arabic-English translation. ...

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  • Current parsing models are not immediately applicable for languages that exhibit strong interaction between morphology and syntax, e.g., Modern Hebrew (MH), Arabic and other Semitic languages. This work represents a first attempt at modeling morphological-syntactic interaction in a generative probabilistic framework to allow for MH parsing. We show that morphological information selected in tandem with syntactic categories is instrumental for parsing Semitic languages. We further show that redundant morphological information helps syntactic disambiguation. ...

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