2,345 research outputs found
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Treebank-based acquisition of Chinese LFG resources for parsing and generation
This thesis describes a treebank-based approach to automatically acquire robust,wide-coverage Lexical-Functional Grammar (LFG) resources for Chinese parsing
and generation, which is part of a larger project on the rapid construction of deep, large-scale, constraint-based, multilingual grammatical resources. I present an application-oriented LFG analysis for Chinese core linguistic phenomena and (in cooperation with PARC) develop a gold-standard dependency-bank of Chinese f-structures for evaluation. Based on the Penn Chinese Treebank, I design and implement two architectures for inducing Chinese LFG resources, one annotation-based and the other dependency conversion-based. I then apply the f-structure acquisition algorithm together with external, state-of-the-art parsers to parsing new text into "proto" f-structures. In order to convert "proto" f-structures into "proper" f-structures or deep dependencies, I present a novel Non-Local Dependency (NLD) recovery algorithm using subcategorisation frames and f-structure paths linking antecedents and traces in NLDs extracted from the automatically-built LFG f-structure treebank. Based on the grammars extracted from the f-structure annotated treebank, I develop a PCFG-based chart generator and a new n-gram based pure dependency generator to realise Chinese sentences from LFG f-structures.
The work reported in this thesis is the first effort to scale treebank-based, probabilistic Chinese LFG resources from proof-of-concept research to unrestricted, real
text. Although this thesis concentrates on Chinese and LFG, many of the methodologies, e.g. the acquisition of predicate-argument structures, NLD resolution and
the PCFG- and dependency n-gram-based generation models, are largely language and formalism independent and should generalise to diverse languages as well as to labelled bilexical dependency representations other than LFG
Exploiting multi-word units in history-based probabilistic generation
We present a simple history-based model for sentence generation from LFG f-structures, which improves on the accuracy of previous models by breaking down PCFG independence assumptions so that more f-structure conditioning context is used in the prediction of grammar rule expansions. In addition, we present work on experiments with named entities and other multi-word units,
showing a statistically significant improvement of generation accuracy. Tested on section 23 of the PennWall Street Journal Treebank, the techniques described in this paper improve BLEU scores from 66.52 to 68.82, and coverage from 98.18% to 99.96%
Automatic evaluation of generation and parsing for machine translation with automatically acquired transfer rules
This paper presents a new method of evaluation for generation and parsing components of transfer-based MT systems where the transfer rules have been automatically
acquired from parsed sentence-aligned bitext corpora. The method provides a means of quantifying the upper bound imposed on the MT system by the quality of the parsing
and generation technologies for the target language. We include experiments to calculate this upper bound for both handcrafted and automatically induced parsing and generation technologies currently in use by transfer-based MT systems
Finding common ground: towards a surface realisation shared task
In many areas of NLP reuse of utility tools such as parsers and POS taggers is now common, but this is still rare in NLG. The subfield of surface realisation has perhaps come closest, but at present we still lack a basis on which different surface realisers could be compared, chiefly because of the wide variety of different input representations used by different realisers. This paper outlines an idea for a shared task in surface realisation, where inputs are provided in a common-ground representation formalism which participants map to the types of input required by their system. These inputs are derived from existing annotated corpora developed for language analysis (parsing etc.). Outputs (realisations) are evaluated by automatic comparison against the human-authored text in the
corpora as well as by human assessors
Generating Natural Language from Linked Data:Unsupervised template extraction
We propose an architecture for generating natural language from Linked Data that automatically learns sentence templates and statistical document planning from parallel RDF datasets and text. We have built a proof-of-concept system (LOD-DEF) trained on un-annotated text from the Simple English Wikipedia and RDF triples from DBpedia, focusing exclusively on factual, non-temporal information. The goal of the system is to generate short descriptions, equivalent to Wikipedia stubs, of entities found in Linked Datasets. We have evaluated the LOD-DEF system against a simple generate-from-triples baseline and human-generated output. In evaluation by humans, LOD-DEF significantly outperforms the baseline on two of three measures: non-redundancy and structure and coherence.
A rule-based translation from written Spanish to Spanish Sign Language glosses
This is the authorās version of a work that was accepted for publication in Computer Speech and Language. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Speech and Language, 28, 3 (2015) DOI: 10.1016/j.csl.2013.10.003One of the aims of Assistive Technologies is to help people with disabilities to communicate with others and to provide means of access to information. As an aid to Deaf people, we present in this work a production-quality rule-based machine system for translating from Spanish to Spanish Sign Language (LSE) glosses, which is a necessary precursor to building a full machine translation system that eventually produces animation output. The system implements a transfer-based architecture from the syntactic functions of dependency analyses. A sketch of LSE is also presented. Several topics regarding translation to sign languages are addressed: the lexical gap, the bootstrapping of a bilingual lexicon, the generation of word order for topic-oriented languages, and the treatment of classifier predicates and classifier names. The system has been evaluated with an open-domain testbed, reporting a 0.30 BLEU (BiLingual Evaluation Understudy) and 42% TER (Translation Error Rate). These results show consistent improvements over a statistical machine translation baseline, and some improvements over the same system preserving the word order in the source sentence. Finally, the linguistic analysis of errors has identified some differences due to a certain degree of structural variation in LSE
ATMS-Based architecture for stylistics-aware text generation
This thesis is concerned with the effect of surface stylistic constraints (SSC) on syntactic
and lexical choice within a unified generation architecture. Despite the fact that these
issues have been investigated by researchers in the field, little work has been done with
regard to system architectures that allow surface form constraints to influence earlier
linguistic or even semantic decisions made throughout the NLG process. By SSC we
mean those stylistic requirements that are known beforehand but cannot be tested
until after the utterance or ā in some lucky cases ā until a proper linearised part
of it has been generated. These include collocational constraints, text size limits, and
poetic aspects such as rhyme and metre to name a few.
This thesis introduces a new NLG architecture that can be sensitive to surface stylistic
requirements. It brings together a well-founded linguistic theory that has been used
in many successful NLG systems (Systemic Functional Linguistics, SFL) and an existĀ¬
ing AI search mechanism (the Assumption-based Truth Maintenance System, ATMS)
which caches important search information and avoids work duplication.
To this end, the thesis explores the logical relation between the grammar formalism and
the search technique. It designs, based on that logical connection, an algorithm for the
automatic translation of systemic grammar networks to ATMS dependency networks.
The generator then uses the translated networks to generate natural language texts
with a high paraphrasing power as a direct result of its ability to pursue multiple paths
simultaneously. The thesis approaches the crucial notion of choice differently to previĀ¬
ous systems using SFL. It relaxes the choice process in that choosers are not obliged to
deterministically choose a single alternative allowing SSC to influence the final lexical
and syntactic decisions. The thesis also develops a situation-action framework for the
specification of stylistic requirements independently of the micro-semantic input. The
user or application can state what surface requirements they wish to impose and the
ATMS-based generator then attempts to satisfy these constraints.
Finally, a prototype ATMS-based generation system embodying the ideas presented in
this thesis is implemented and evaluated. We examine the system's stylistic sensitivity
by testing it on three different sets of stylistic requirements, namely: collocational,
size, and poetic constraints
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