Our goal is to explore methods for combining structured but incomplete information from dictionaries with the unstructured but more complete information available in corpora for t...
We propose a succinct randomized language model which employs a perfect hash function to encode fingerprints of n-grams and their associated probabilities, backoff weights, or oth...
Language modeling is to associate a sequence of words with a priori probability, which is a key part of many natural language applications such as speech recognition and statistic...
We take a multi-pass approach to machine translation decoding when using synchronous context-free grammars as the translation model and n-gram language models: the first pass uses...
We use robust and fast Finite-State Machines (FSMs) to solve scriptural translation problems. We describe a phonetico-morphotactic pivot UIT (universal intermediate transcription)...
M. G. Abbas Malik, Christian Boitet, Pushpak Bhatt...