43,136 research outputs found
The Speech-Language Interface in the Spoken Language Translator
The Spoken Language Translator is a prototype for practically useful systems
capable of translating continuous spoken language within restricted domains.
The prototype system translates air travel (ATIS) queries from spoken English
to spoken Swedish and to French. It is constructed, with as few modifications
as possible, from existing pieces of speech and language processing software.
The speech recognizer and language understander are connected by a fairly
conventional pipelined N-best interface. This paper focuses on the ways in
which the language processor makes intelligent use of the sentence hypotheses
delivered by the recognizer. These ways include (1) producing modified
hypotheses to reflect the possible presence of repairs in the uttered word
sequence; (2) fast parsing with a version of the grammar automatically
specialized to the more frequent constructions in the training corpus; and (3)
allowing syntactic and semantic factors to interact with acoustic ones in the
choice of a meaning structure for translation, so that the acoustically
preferred hypothesis is not always selected even if it is within linguistic
coverage.Comment: 9 pages, LaTeX. Published: Proceedings of TWLT-8, December 199
An Efficient Distribution of Labor in a Two Stage Robust Interpretation Process
Although Minimum Distance Parsing (MDP) offers a theoretically attractive
solution to the problem of extragrammaticality, it is often computationally
infeasible in large scale practical applications. In this paper we present an
alternative approach where the labor is distributed between a more restrictive
partial parser and a repair module. Though two stage approaches have grown in
popularity in recent years because of their efficiency, they have done so at
the cost of requiring hand coded repair heuristics. In contrast, our two stage
approach does not require any hand coded knowledge sources dedicated to repair,
thus making it possible to achieve a similar run time advantage over MDP
without losing the quality of domain independence.Comment: 9 pages, 1 Postscript figure, uses aclap.sty and psfig.tex, In
Proceedings of EMNLP 199
Low-resource machine translation using MATREX: The DCU machine translation system for IWSLT 2009
In this paper, we give a description of the Machine Translation (MT) system developed at DCU that was used for our fourth participation in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT 2009). Two techniques are deployed in our system in order to improve the translation quality in a low-resource scenario. The first technique is to use multiple segmentations in MT training and to utilise word lattices in decoding stage. The second technique is used to select the optimal training data that can be used to build MT systems. In this yearâs participation, we use three different prototype SMT systems, and the output from each system are combined using standard system combination method. Our system is the top system for ChineseâEnglish CHALLENGE task in terms of BLEU score
Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification
Connectionist temporal classification (CTC) is a powerful approach for
sequence-to-sequence learning, and has been popularly used in speech
recognition. The central ideas of CTC include adding a label "blank" during
training. With this mechanism, CTC eliminates the need of segment alignment,
and hence has been applied to various sequence-to-sequence learning problems.
In this work, we applied CTC to abstractive summarization for spoken content.
The "blank" in this case implies the corresponding input data are less
important or noisy; thus it can be ignored. This approach was shown to
outperform the existing methods in term of ROUGE scores over Chinese Gigaword
and MATBN corpora. This approach also has the nice property that the ordering
of words or characters in the input documents can be better preserved in the
generated summaries.Comment: Accepted by Interspeech 201
SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks
In this paper, we describe a so-called screening approach for learning robust
processing of spontaneously spoken language. A screening approach is a flat
analysis which uses shallow sequences of category representations for analyzing
an utterance at various syntactic, semantic and dialog levels. Rather than
using a deeply structured symbolic analysis, we use a flat connectionist
analysis. This screening approach aims at supporting speech and language
processing by using (1) data-driven learning and (2) robustness of
connectionist networks. In order to test this approach, we have developed the
SCREEN system which is based on this new robust, learned and flat analysis.
In this paper, we focus on a detailed description of SCREEN's architecture,
the flat syntactic and semantic analysis, the interaction with a speech
recognizer, and a detailed evaluation analysis of the robustness under the
influence of noisy or incomplete input. The main result of this paper is that
flat representations allow more robust processing of spontaneous spoken
language than deeply structured representations. In particular, we show how the
fault-tolerance and learning capability of connectionist networks can support a
flat analysis for providing more robust spoken-language processing within an
overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial
Intelligence Research 6(1), 199
F-structure transfer-based statistical machine translation
In this paper, we describe a statistical deep syntactic transfer decoder that is trained fully automatically on parsed bilingual corpora. Deep syntactic transfer rules are induced automatically from the f-structures of a LFG parsed bitext corpus by automatically aligning local f-structures, and inducing all rules consistent with the node alignment. The transfer decoder outputs the n-best TL f-structures given a SL f-structure as input by applying large numbers of transfer rules and searching for the best output using a
log-linear model to combine feature scores. The decoder includes a fully integrated dependency-based tri-gram language model. We include an experimental evaluation of the decoder using different parsing disambiguation
resources for the German data to provide a comparison of how the system performs with different German training and test parses
Parsing of Spoken Language under Time Constraints
Spoken language applications in natural dialogue settings place serious
requirements on the choice of processing architecture. Especially under adverse
phonetic and acoustic conditions parsing procedures have to be developed which
do not only analyse the incoming speech in a time-synchroneous and incremental
manner, but which are able to schedule their resources according to the varying
conditions of the recognition process. Depending on the actual degree of local
ambiguity the parser has to select among the available constraints in order to
narrow down the search space with as little effort as possible.
A parsing approach based on constraint satisfaction techniques is discussed.
It provides important characteristics of the desired real-time behaviour and
attempts to mimic some of the attention focussing capabilities of the human
speech comprehension mechanism.Comment: 19 pages, LaTe
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