267 research outputs found
High efficiency realization for a wide-coverage unification grammar
We give a detailed account of an algorithm for efficient tactical generation from underspecified logical-form semantics, using a wide-coverage grammar and a corpus of real-world target utterances. Some earlier claims about chart realization are critically reviewed and corrected in the light of a series of practical experiments. As well as a set of algorithmic refinements, we present two novel techniques: the integration of subsumption-based local ambiguity factoring, and a procedure to selectively unpack the generation forest according to a probability distribution given by a conditional, discriminative model
Improving Generation in Machine Translation by Separating Syntactic and Morphological Processes
Abstract-This paper presents a generation approach in a Lexical Functional Grammar (LFG) based machine translation system that subdivides the process and uses rule based modules to address the problem. The results show improvement in performance compared to the earlier work which generates the translation into Urdu using a single integrated process
Instance-based natural language generation
In recent years, ranking approaches to Natural Language Generation have become increasingly popular. They abandon the idea of generation as a deterministic decision¬
making process in favour of approaches that combine overgeneration with ranking at
some stage in processing.In this thesis, we investigate the use of instance-based ranking methods for surface
realization in Natural Language Generation. Our approach to instance-based Natural
Language Generation employs two basic components: a rule system that generates a
number of realization candidates from a meaning representation and an instance-based
ranker that scores the candidates according to their similarity to examples taken from a
training corpus. The instance-based ranker uses information retrieval methods to rank
output candidates.Our approach is corpus-based in that it uses a treebank (a subset of the Penn Treebank
II containing management succession texts) in combination with manual semantic markup to automatically produce a generation grammar. Furthermore, the corpus
is also used by the instance-based ranker. The semantic annotation of a test portion of
the compiled subcorpus serves as input to the generator.In this thesis, we develop an efficient search technique for identifying the optimal
candidate based on the A*-algorithm, detail the annotation scheme and grammar con¬
struction algorithm and show how a Rete-based production system can be used for
efficient candidate generation. Furthermore, we examine the output of the generator
and discuss issues like input coverage (completeness), fluency and faithfulness that are
relevant to surface generation in general
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