This paper describes the resource- and system-building efforts of an
eight-week Johns Hopkins University Human Language Technology Center of
Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on
Semantically-Informed Machine Translation (SIMT). We describe a new
modality/negation (MN) annotation scheme, the creation of a (publicly
available) MN lexicon, and two automated MN taggers that we built using the
annotation scheme and lexicon. Our annotation scheme isolates three components
of modality and negation: a trigger (a word that conveys modality or negation),
a target (an action associated with modality or negation) and a holder (an
experiencer of modality). We describe how our MN lexicon was semi-automatically
produced and we demonstrate that a structure-based MN tagger results in
precision around 86% (depending on genre) for tagging of a standard LDC data
set.
We apply our MN annotation scheme to statistical machine translation using a
syntactic framework that supports the inclusion of semantic annotations.
Syntactic tags enriched with semantic annotations are assigned to parse trees
in the target-language training texts through a process of tree grafting. While
the focus of our work is modality and negation, the tree grafting procedure is
general and supports other types of semantic information. We exploit this
capability by including named entities, produced by a pre-existing tagger, in
addition to the MN elements produced by the taggers described in this paper.
The resulting system significantly outperformed a linguistically naive baseline
model (Hiero), and reached the highest scores yet reported on the NIST 2009
Urdu-English test set. This finding supports the hypothesis that both syntactic
and semantic information can improve translation quality.

本文描述了为期八周的约翰霍普金斯大学人类语言技术卓越中心应用语言探索夏令营（SCALE-2009）中语义驱动机器翻译（SIMT）的资源和系统构建工作。作者引入了一种新的模态 / 否定（MN）注释方案，基于该方案和词典生成了两个自动化的 MN 标注器，并将其应用于用于翻译的语法框架中，结果显示基于结构的 MN 标注器的标注精确率高达 86％。最终生成的系统有效地提高了翻译质量。