Alignment Error Rate

We investigate a number of simple methods for improving the word-alignment accuracy of IBM Model 1. We demonstrate reduction in alignment error rate of approximately 30% resulting from (1) giving …...

We apply statistical machine translation (SMT) tools to generate novel paraphrases of input sentences in the same language. The system is trained on large volumes of sentence pairs automatically extracted …...

We investigate unsupervised techniques for acquiring monolingual sentence-level paraphrases from a corpus of temporally and topically clustered news articles collected from thousands of web-based news sources. Two techniques are employed: …...

For many years, statistical machine trans- lation relied on generative models to pro- vide bilingual word alignments. In 2005, several independent efforts showed that discriminative models could be used to …...

We present a new method for learning to parse a bilingual sentence using Inversion Transduction Grammar trained on a parallel corpus and a monolingual treebank. The method produces a parse …...

In this paper, we present a Bayesian Learn- ing based method to train word dependent transition models for HMM based word alignment. We present word alignment re- sults on the …...

AbstractThis paper presents a method for compiling a large-scale bilingual corpus from a database of movie subtitles. To create the corpus, we propose an algorithm based on Gale and Church’s …...

We propose a version of Inversion Transduction Grammar (ITG) model with IBM-style notation of fertility to improve word-alignment performance. In our approach, binary context-free grammar rules of the source language, …...

AbstractWe present a heuristic method for word alignment, which is the task of identifying corresponding words in parallel text. The heuristic method is based on parallel phrases extracted from manually …...

. Although several recent studies have shown that alignment quality is a poor in- dicator of the resulting translation quality, the word alignment models currently considered to be default (the …...

AbstractWord alignment is an important step for machine translation systems. Although the alignment performance between grammatically similar languages is reported to be very high in many studies, the case is …...

We propose a novel unsupervised word alignment model based on the Hidden Markov Tree (HMT) model. Our model assumes that the alignment variables have a tree structure which is isomorphic …...

AbstractExisting neural machine translation systems do not explicitly model what has been translated and what has not during the decoding phase. To address this problem, we propose a novel mechanism …...

AbstractIn neural machine translation, an attention model is used to identify the aligned source words for a target word (target foresight word) in order to select translation context, but it …...

AbstractAn ongoing challenge in current natural language processing is how its major advancements tend to disproportionately favor resource-rich languages, leaving a significant number of under-resourced languages behind. Due to the …...