We present a new translation model that include undecorated hierarchical-style phrase rules, decorated source-syntax rules, and partially decorated rules. Results show an increase in translation performance of up to 0.8% BLEU for German–English translation when trained on the news-commentary corpus, using syntactic annotation from a source language parser. We also experimented with annotation f...

We propose several techniques for improving statistical machine translation between closely-related languages with scarce resources. We use character-level translation trained on n-gram-character-aligned bitexts and tuned using word-level BLEU, which we further augment with character-based transliteration at the word level and combine with a word-level translation model. The evaluation on Maced...

This paper proposes a new automatic machine translation evaluation metric: AMBER, which is based on the metric BLEU but incorporates recall, extra penalties, and some text processing variants. There is very little linguistic information in AMBER. We evaluate its system-level correlation and sentence-level consistency scores with human rankings from the WMT shared evaluation task; AMBER achieves...

This paper presents a novel word reordering model that employs a shift-reduce parser for inversion transduction grammars. Our model uses rich syntax parsing features for word reordering and runs in linear time. We apply it to postordering of phrase-based machine translation (PBMT) for Japanese-to-English patent tasks. Our experimental results show that our method achieves a significant improvem...

In this paper we study the use of sentencelevel dialect identification in optimizing machine translation system selection when translating mixed dialect input. We test our approach on Arabic, a prototypical diglossic language; and we optimize the combination of four different machine translation systems. Our best result improves over the best single MT system baseline by 1.0% BLEU and over a st...

We present dependency-based n-gram models for general-purpose, widecoverage, probabilistic sentence realisation. Our method linearises unordered dependencies in input representations directly rather than via the application of grammar rules, as in traditional chartbased generators. The method is simple, efficient, and achieves competitive accuracy and complete coverage on standard English (Penn...

This paper gives an overview of the research and implementation challenges we encountered in building an endto-end natural language processing based watermarking system. With natural language watermarking, we mean embedding the watermark into a text document, using the natural language components as the carrier, in such a way that the modifications are imperceptible to the readers and the embed...

The Defense Advanced Research Projects Agency (DARPA) Spoken Language Communication and Translation System for Tactical Use (TRANSTAC) program has experimented with applying automated metrics to speech translation dialogues. For translations into English, BLEU, TER, and METEOR scores correlate well with human judgments, but scores for translation into Arabic correlate with human judgments less ...

Numerous works in Statistical Machine Translation (SMT) have attempted to identify better translation hypotheses obtained by an initial decoding using an improved, but more costly scoring function. In this work, we introduce an approach that takes the hypotheses produced by a state-ofthe-art, reranked phrase-based SMT system, and explores new parts of the search space by applying rewriting rule...

We present a unified approach to performing minimum risk training and minimum Bayes risk (MBR) decoding with BLEU in a phrase-based model. Key to our approach is the use of a Gibbs sampler that allows us to explore the entire probability distribution and maintain a strict probabilistic formulation across the pipeline. We also describe a new sampling algorithm called corpus sampling which allows...