3,047 research outputs found
Research on Architectures for Integrated Speech/Language Systems in Verbmobil
The German joint research project Verbmobil (VM) aims at the development of a
speech to speech translation system. This paper reports on research done in our
group which belongs to Verbmobil's subproject on system architectures (TP15).
Our specific research areas are the construction of parsers for spontaneous
speech, investigations in the parallelization of parsing and to contribute to
the development of a flexible communication architecture with distributed
control.Comment: 6 pages, 2 Postscript figure
Joint Morphological and Syntactic Disambiguation
In morphologically rich languages, should morphological and syntactic disambiguation be treated sequentially or as a single problem? We describe several efficient, probabilistically interpretable ways to apply joint inference to morphological and syntactic disambiguation using lattice parsing. Joint inference is shown to compare favorably to pipeline parsing methods across a variety of component models. State-of-the-art performance on Hebrew Treebank parsing is demonstrated using the new method. The benefits of joint inference are modest with the current component models, but appear to increase as components themselves improve
Token and Type Constraints for Cross-Lingual Part-of-Speech Tagging
We consider the construction of part-of-speech taggers for resource-poor languages. Recently, manually constructed tag dictionaries from Wiktionary and dictionaries projected via bitext have been used as type constraints to overcome the scarcity of annotated data in this setting. In this paper, we show that additional token constraints can be projected from a resource-rich source language to a resource-poor target language via word-aligned bitext. We present several models to this end; in particular a partially observed conditional random field model, where coupled token and type constraints provide a partial signal for training. Averaged across eight previously studied Indo-European languages, our model achieves a 25% relative error reduction over the prior state of the art. We further present successful results on seven additional languages from different families, empirically demonstrating the applicability of coupled token and type constraints across a diverse set of languages
Learning Semantic Correspondences in Technical Documentation
We consider the problem of translating high-level textual descriptions to
formal representations in technical documentation as part of an effort to model
the meaning of such documentation. We focus specifically on the problem of
learning translational correspondences between text descriptions and grounded
representations in the target documentation, such as formal representation of
functions or code templates. Our approach exploits the parallel nature of such
documentation, or the tight coupling between high-level text and the low-level
representations we aim to learn. Data is collected by mining technical
documents for such parallel text-representation pairs, which we use to train a
simple semantic parsing model. We report new baseline results on sixteen novel
datasets, including the standard library documentation for nine popular
programming languages across seven natural languages, and a small collection of
Unix utility manuals.Comment: accepted to ACL-201
Joint morphological-lexical language modeling for processing morphologically rich languages with application to dialectal Arabic
Language modeling for an inflected language
such as Arabic poses new challenges for speech recognition and
machine translation due to its rich morphology. Rich morphology
results in large increases in out-of-vocabulary (OOV) rate and
poor language model parameter estimation in the absence of large
quantities of data. In this study, we present a joint
morphological-lexical language model (JMLLM) that takes
advantage of Arabic morphology. JMLLM combines
morphological segments with the underlying lexical items and
additional available information sources with regards to
morphological segments and lexical items in a single joint model.
Joint representation and modeling of morphological and lexical
items reduces the OOV rate and provides smooth probability
estimates while keeping the predictive power of whole words.
Speech recognition and machine translation experiments in
dialectal-Arabic show improvements over word and morpheme
based trigram language models. We also show that as the
tightness of integration between different information sources
increases, both speech recognition and machine translation
performances improve
MUSE CSP: An Extension to the Constraint Satisfaction Problem
This paper describes an extension to the constraint satisfaction problem
(CSP) called MUSE CSP (MUltiply SEgmented Constraint Satisfaction Problem).
This extension is especially useful for those problems which segment into
multiple sets of partially shared variables. Such problems arise naturally in
signal processing applications including computer vision, speech processing,
and handwriting recognition. For these applications, it is often difficult to
segment the data in only one way given the low-level information utilized by
the segmentation algorithms. MUSE CSP can be used to compactly represent
several similar instances of the constraint satisfaction problem. If multiple
instances of a CSP have some common variables which have the same domains and
constraints, then they can be combined into a single instance of a MUSE CSP,
reducing the work required to apply the constraints. We introduce the concepts
of MUSE node consistency, MUSE arc consistency, and MUSE path consistency. We
then demonstrate how MUSE CSP can be used to compactly represent lexically
ambiguous sentences and the multiple sentence hypotheses that are often
generated by speech recognition algorithms so that grammar constraints can be
used to provide parses for all syntactically correct sentences. Algorithms for
MUSE arc and path consistency are provided. Finally, we discuss how to create a
MUSE CSP from a set of CSPs which are labeled to indicate when the same
variable is shared by more than a single CSP.Comment: See http://www.jair.org/ for any accompanying file
Filling Knowledge Gaps in a Broad-Coverage Machine Translation System
Knowledge-based machine translation (KBMT) techniques yield high quality in
domains with detailed semantic models, limited vocabulary, and controlled input
grammar. Scaling up along these dimensions means acquiring large knowledge
resources. It also means behaving reasonably when definitive knowledge is not
yet available. This paper describes how we can fill various KBMT knowledge
gaps, often using robust statistical techniques. We describe quantitative and
qualitative results from JAPANGLOSS, a broad-coverage Japanese-English MT
system.Comment: 7 pages, Compressed and uuencoded postscript. To appear: IJCAI-9
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