Standard N-gram Language Model Research Articles

Toward Kurdish language processing: Experiments in collecting and processing the AsoSoft text corpus

In this article, we introduce the first Kurdish text corpus for Central Kurdish (Sorani) branch, called AsoSoft text corpus. Kurdish language, which is spoken by more than 30 million people, has various dialects. As one of the two main branches of Kurdish, Central Kurdish is the formal dialect of Kurdish literature. AsoSoft text corpus is of size 188 million tokens and has been collected mostly from Web sites, published books, and magazines. The corpus has been normalized and converted into Text Encoding Initiative XML format. In both collecting and processing the text, we have faced several challenges and have proposed solutions to them. About 22% of the corpus is topic annotated with six topic tags, and a topic identification task has been done to evaluate the correctness of annotation. The computational experiments of the Central Kurdish text processing are also presented with the support of related supplementary statistics. For the first time, the validity of Zipf’s law for Central Kurdish is presented and also perplexity of this language is calculated using standard N-gram language models. The perplexity of Central Kurdish is 276 for a tri-gram language model.

Modelling and Optimizing on Syntactic N-Grams for Statistical Machine Translation

The role of language models in SMT is to promote fluent translation output, but traditional n-gram language models are unable to capture fluency phenomena between distant words, such as some morphological agreement phenomena, subcategorisation, and syntactic collocations with string-level gaps. Syntactic language models have the potential to fill this modelling gap. We propose a language model for dependency structures that is relational rather than configurational and thus particularly suited for languages with a (relatively) free word order. It is trainable with Neural Networks, and not only improves over standard n-gram language models, but also outperforms related syntactic language models. We empirically demonstrate its effectiveness in terms of perplexity and as a feature function in string-to-tree SMT from English to German and Russian. We also show that using a syntactic evaluation metric to tune the log-linear parameters of an SMT system further increases translation quality when coupled with a syntactic language model.

Backward and trigger-based language models for statistical machine translation

AbstractThe language model is one of the most important knowledge sources for statistical machine translation. In this article, we present two extensions to standard n-gram language models in statistical machine translation: a backward language model that augments the conventional forward language model, and a mutual information trigger model which captures long-distance dependencies that go beyond the scope of standard n-gram language models. We introduce algorithms to integrate the two proposed models into two kinds of state-of-the-art phrase-based decoders. Our experimental results on Chinese/Spanish/Vietnamese-to-English show that both models are able to significantly improve translation quality in terms of BLEU and METEOR over a competitive baseline.

Common Sense Knowledge for Handwritten Chinese Text Recognition

Compared to human intelligence, computers are far short of common sense knowledge which people normally acquire during the formative years of their lives. This paper investigates the effects of employing common sense knowledge as a new linguistic context in handwritten Chinese text recognition. Three methods are introduced to supplement the standard n-gram language model: embedding model, direct model, and an ensemble of these two. The embedding model uses semantic similarities from common sense knowledge to make the n-gram probabilities estimation more reliable, especially for the unseen n-grams in the training text corpus. The direct model, in turn, considers the linguistic context of the whole document to make up for the short context limit of the n-gram model. The three models are evaluated on a large unconstrained handwriting database, CASIA-HWDB, and the results show that the adoption of common sense knowledge yields improvements in recognition performance, despite the reduced concept list hereby employed.