Maryam Siahbani

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Out-of-vocabulary (oov) words or phrases still remain a challenge in statistical machine translation especially when a limited amount of parallel text is available for training or when there is a domain shift from training data to test data. In this paper, we propose a novel approach to finding translations for oov words. We induce a lexicon by constructing(More)
Extracting information from text is challenging. Most current practices treat text as a bag of words or word clusters, ignoring valuable linguistic information. Leveraging this linguistic information, we propose a novel approach to visualize textual information. The novelty lies in using state-of-the-art Natural Language Processing (NLP) tools to(More)
Left-to-right (LR) decoding (Watanabe et al., 2006b) is a promising decoding algorithm for hierarchical phrase-based translation (Hiero). It generates the target sentence by extending the hypotheses only on the right edge. LR decoding has complexity O(nb) for input of n words and beam size b, compared toO(n) for the CKY algorithm. It requires a single(More)
Left-to-right (LR) decoding (Watanabe et al., 2006) is promising decoding algorithm for hierarchical phrase-based translation (Hiero) that visits input spans in arbitrary order producing the output translation in left to right order. This leads to far fewer language model calls, but while LR decoding is more efficient than CKY decoding, it is unable to(More)
Hierarchical phrase-based machine translation [1] (Hiero) is a prominent approach for Statistical Machine Translation usually comparable to or better than conventional phrase-based systems. But Hiero typically uses the CKY decoding algorithm which requires the entire input sentence before decoding begins, as it produces the translation in a bottom-up(More)
This paper extracts facts using "micro-reading" of text in contrast to approaches that extract common-sense knowledge using "macro-reading" methods. Our goal is to extract detailed facts about events from natural language using a predicate-centered view of events (who did what to whom, when and how). We exploit semantic role labels in order to create a(More)
The multilingual Paraphrase Database (PPDB) is a freely available automatically created resource of paraphrases in multiple languages. In statistical machine translation, paraphrases can be used to provide translation for out-of-vocabulary (OOV) phrases. In this paper, we show that a graph propagation approach that uses PPDB paraphrases can be used to(More)
We examine approaches of statistical machine translation without parallel data (SMT). SMT has achieved impressive performance by leveraging large amounts of parallel data in the source and target languages. But such data is available only for a few language pairs and domains. Using human annotation to create new parallel corpora sufficient for building a(More)