Add to ORKGIn this paper we study in detail the relation be-tween word alignment and phrase extraction. First, we analyze different word alignments according to several characteristics and com-pare them to hand-aligned data. Secondly, we analyzed the phrase-pairs generated by these alignments. We observed that the number of unaligned words has a large impact on the characteristics of the phrase table. A manual evaluation of phrase pair quality showed that the increase in the number of unaligned words results in a lower quality. Finally, we present translation results from using the number of unaligned words as features from which we obtain up to 2BP of improvement.
We argue that learning word alignments through a compositionally-structured, joint process yields hi...
In this thesis we present the idea of using parallel phrases for word alignment. Each parallel phras...
Most statistical machine translation systems employ a word-based alignment model. In this paper we d...
In the last years, researchers conducted several studies to evaluate the machine translation quality...
which permits unrestricted use, distribution, and reproduction in any medium, provided the original ...
Statistical Word Alignments represent lexical word-to-word translations between source and target la...
The goal of a machine translation (MT) system is to automatically translate a document written in so...
We present a novel translation quality informed procedure for both extraction and scoring of phrase ...
Abstract: The phrase table, a scored list of bilingual phrases, lies at the center of phrase-based m...
A novel approach is presented for extracting syntactically motivated phrase alignments. In this meth...
In this paper, we present a phrase extraction algorithm using a translation lexicon, a fertility m...
In this paper, we examine a number of different phrase segmentation approaches for Machine Translati...
Statistical Machine Translation (SMT) developed in the late 1980s, based initially upon a word-to-wo...
When parallel or comparable corpora are harvested from the web, there is typically a tradeoff betwee...
In this paper, we present a phrase ex-traction algorithm using a translation lex-icon, a fertility m...
We argue that learning word alignments through a compositionally-structured, joint process yields hi...
In this thesis we present the idea of using parallel phrases for word alignment. Each parallel phras...
Most statistical machine translation systems employ a word-based alignment model. In this paper we d...
In the last years, researchers conducted several studies to evaluate the machine translation quality...
which permits unrestricted use, distribution, and reproduction in any medium, provided the original ...
Statistical Word Alignments represent lexical word-to-word translations between source and target la...
The goal of a machine translation (MT) system is to automatically translate a document written in so...
We present a novel translation quality informed procedure for both extraction and scoring of phrase ...
Abstract: The phrase table, a scored list of bilingual phrases, lies at the center of phrase-based m...
A novel approach is presented for extracting syntactically motivated phrase alignments. In this meth...
In this paper, we present a phrase extraction algorithm using a translation lexicon, a fertility m...
In this paper, we examine a number of different phrase segmentation approaches for Machine Translati...
Statistical Machine Translation (SMT) developed in the late 1980s, based initially upon a word-to-wo...
When parallel or comparable corpora are harvested from the web, there is typically a tradeoff betwee...
In this paper, we present a phrase ex-traction algorithm using a translation lex-icon, a fertility m...
We argue that learning word alignments through a compositionally-structured, joint process yields hi...
In this thesis we present the idea of using parallel phrases for word alignment. Each parallel phras...
Most statistical machine translation systems employ a word-based alignment model. In this paper we d...