N-Gram Language Models

Recent work has considered corpus-based or statistical approaches to the problem of prepositional phrase attachment ambiguity. Typically, ambiguous verb phrases of the form v rip1 p rip2 are resolved through …...

We analyze subword-based language models (LMs) in large-vocabulary continuous speech recognition across four “morphologically rich” languages: Finnish, Estonian, Turkish, and Egyptian Colloquial Arabic. By estimating n-gram LMs over sequences of …...

Statistical machine translation, as well as other areas of human language processing, have recently pushed toward the use of large scale n-gram language models. This paper presents efficient algorithmic and …...

We take a multi-pass approach to ma- chine translation decoding when using syn- chronous context-free grammars as the trans- lation model and n-gram language models: the first pass uses a …...

The recent availability of large corpora for training N-gram language models has shown the utility of models of higher or- der than just trigrams. In this paper, we investigate methods …...

We investigate the task of performance pre- diction for language models belonging to the exponential family. First, we attempt to em- pirically discover a formula for predicting test set cross-entropy …...

This paper presents a novel approach to automatic captioning of geo-tagged images by summarizing multiple web- documents that contain information re- lated to an image’s location. The summa- rizer is …...

We introduce a novel Bayesian approach for deciphering complex substitution ciphers. Our method uses a decipherment model which combines information from letter n-gram lan- guage models as well as word …...

In this paper we present work on the task of Native Language Identification (NLI). We present an alternative corpus to the ICLE which has been used in most work up …...

In this paper, we describe the UPC system that participated in the WMT 2012 shared task on Quality Estimation for Machine Translation. Based on the empirical evidence that fluency- related …...

We introduce a shift-reduce parsing algorithm for phrase-based string-to- dependency translation. As the algorithm generates dependency trees for partial translations left-to-right in decoding, it allows for efficient integration of both …...

Ngram language models tend to increase in size with inflating the corpus size, and con- sume considerable resources. In this pa- per, we propose an efficient method for im- plementing …...

Sentence completion is a challenging seman- tic modeling task in which models must choose the most appropriate word from a given set to complete a sentence. Although a variety of …...

. If neural networks play an increasingly important role in natural language processing, training issues still hinder their dissemination in the community. This paper studies different learning strategies for neural …...

We describe an approach to word ordering using modelling techniques from statisti- cal machine translation. The system in- corporates a phrase-based model of string generation that aims to take unordered …...

Syntactic language models and N-gram language models have both been used in word ordering. In this paper, we give an empirical comparison between N-gram and syntactic language models on word …...

AbstractThis work presents a straightforward method for extending or creating in-domain web corpora by focused webcrawling. The focused webcrawler uses statistical N-gram language models to estimate the relatedness of documents …...

AbstractWe propose a simple data augmentation protocol aimed at providing a compositional inductive bias in conditional and unconditional sequence models. Under this protocol, synthetic training examples are constructed by taking …...