Recent advances have allowed algorithms that learn from aligned natural language texts to exploit aligned sentences in more than two languages. We investigate ways of combining ( N 2 ) bilingual aligned corpora together to create a multilingual aligned corpus across N languages. As a result of the combination of several corpora, our algorithms output a multilingual corpus, with each aligned tup...

In phrase-based statistical machine translation systems, variation in grammatical structures between source and target languages can cause large movements of phrases. Modeling such movements is crucial in achieving translations of long sentences that appear natural in the target language. We explore generative learning approach to phrase reordering in Arabic to English. Formulating the reorderi...

The goal of this study is to automatically differentiate foreign accents in French (Arabic, English, German, Italian, Spanish and Portuguese). We took advantage of automatic alignment into phonemes of non-native French recordings to measure vowel formants, consonant duration and voicing, prosodic cues as well as pronunciation variants combining French and foreign acoustic units (e.g. a rolled ‘...

We present a method to align words in a bitext that combines elements of a traditional statistical approach with linguistic knowledge. We demonstrate this approach for Arabic-English, using an alignment lexicon produced by a statistical word aligner, as well as linguistic resources ranging from an English parser to heuristic alignment rules for function words. These linguistic heuristics have b...

<p style="text-align: justify;"><em>technology. Now many internet-based applications have emerged and are popular with the public, including Tiktok application, which is very local foreign people. The research based on fact that researcher sees potential for using TikTok application to learn Arabic independently. This descriptive qualitative research, describing how this content can...

We describe our entry in the CoNLL-X shared task. The system consists of three phases: a probabilistic vine parser (Eisner and N. Smith, 2005) that produces unlabeled dependency trees, a probabilistic relation-labeling model, and a discriminative minimum risk reranker (D. Smith and Eisner, 2006). The system is designed for fast training and decoding and for high precision. We describe sources o...

In this paper, we propose a model to generate fundamental frequency (F0) contours using neural networks. A learning procedure is proposed as an alternative to synthesis-by-rules. The generation of correct fundamental frequency contour is one of the important issues in the naturalness of automatic text-to-speech conversion systems. The proposed approach is based on a standard feed-forward multi-...

We investigate differences in point of view (POV) between two objective documents, where one is describing the subject matter in a more positive/negative way than the other, and present an automatic method for detecting such POV differences. We use Amazon Mechanical Turk (AMT) to annotate sentences as positive, negative or neutral based on their POV towards a given target. A statistical classif...

In this paper, we present a new approach to align sentences in bilingual parallel corpora based on a probabilistic neural network (P-NNT) classifier. A feature parameter vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually aligned training data was used to train the probabili...

This paper discusses a novel probabilistic synchronous TAG formalism, synchronous Tree Substitution Grammar with sister adjunction (TSG+SA). We use it to parse a language for which there is no training data, by leveraging off a second, related language for which there is abundant training data. The grammar for the resource-rich side is automatically extracted from a treebank; the grammar on the...