58,388 research outputs found
Domain transfer for deep natural language generation from abstract meaning representations
Stochastic natural language generation systems that are trained from labelled datasets are often domainspecific in their annotation and in their mapping from semantic input representations to lexical-syntactic outputs. As a result, learnt models fail to generalize across domains, heavily restricting their usability beyond single applications. In this article, we focus on the problem of domain adaptation for natural language generation. We show how linguistic knowledge from a source domain, for which labelled data is available, can be adapted to a target domain by reusing training data across domains. As a key to this, we propose to employ abstract meaning representations as a common semantic representation across domains. We model natural language generation as a long short-term memory recurrent neural network encoderdecoder, in which one recurrent neural network learns a latent representation of a semantic input, and a second recurrent neural network learns to decode it to a sequence of words. We show that the learnt representations can be transferred across domains and can be leveraged effectively to improve training on new unseen domains. Experiments in three different domains and with six datasets demonstrate that the lexical-syntactic constructions learnt in one domain can be transferred to new domains and achieve up to 75-100% of the performance of in-domain training. This is based on objective metrics such as BLEU and semantic error rate and a subjective human rating study. Training a policy from prior knowledge from a different domain is consistently better than pure in-domain training by up to 10%
Semantic Processing of Out-Of-Vocabulary Words in a Spoken Dialogue System
One of the most important causes of failure in spoken dialogue systems is
usually neglected: the problem of words that are not covered by the system's
vocabulary (out-of-vocabulary or OOV words). In this paper a methodology is
described for the detection, classification and processing of OOV words in an
automatic train timetable information system. The various extensions that had
to be effected on the different modules of the system are reported, resulting
in the design of appropriate dialogue strategies, as are encouraging evaluation
results on the new versions of the word recogniser and the linguistic
processor.Comment: 4 pages, 2 eps figures, requires LaTeX2e, uses eurospeech.sty and
epsfi
Robust Parsing of Spoken Dialogue Using Contextual Knowledge and Recognition Probabilities
In this paper we describe the linguistic processor of a spoken dialogue
system. The parser receives a word graph from the recognition module as its
input. Its task is to find the best path through the graph. If no complete
solution can be found, a robust mechanism for selecting multiple partial
results is applied. We show how the information content rate of the results can
be improved if the selection is based on an integrated quality score combining
word recognition scores and context-dependent semantic predictions. Results of
parsing word graphs with and without predictions are reported.Comment: 4 pages, LaTex source, 3 PostScript figures, uses epsf.sty and
ETRW.sty, to appear in Proceedings of ESCA Workshop on Spoken Dialogue
Systems, Denmark, May 30-June
Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation
Natural language generation (NLG) is a critical component in spoken dialogue
system, which can be divided into two phases: (1) sentence planning: deciding
the overall sentence structure, (2) surface realization: determining specific
word forms and flattening the sentence structure into a string. With the rise
of deep learning, most modern NLG models are based on a sequence-to-sequence
(seq2seq) model, which basically contains an encoder-decoder structure; these
NLG models generate sentences from scratch by jointly optimizing sentence
planning and surface realization. However, such simple encoder-decoder
architecture usually fail to generate complex and long sentences, because the
decoder has difficulty learning all grammar and diction knowledge well. This
paper introduces an NLG model with a hierarchical attentional decoder, where
the hierarchy focuses on leveraging linguistic knowledge in a specific order.
The experiments show that the proposed method significantly outperforms the
traditional seq2seq model with a smaller model size, and the design of the
hierarchical attentional decoder can be applied to various NLG systems.
Furthermore, different generation strategies based on linguistic patterns are
investigated and analyzed in order to guide future NLG research work.Comment: accepted by the 7th IEEE Workshop on Spoken Language Technology (SLT
2018). arXiv admin note: text overlap with arXiv:1808.0274
Towards Understanding Spontaneous Speech: Word Accuracy vs. Concept Accuracy
In this paper we describe an approach to automatic evaluation of both the
speech recognition and understanding capabilities of a spoken dialogue system
for train time table information. We use word accuracy for recognition and
concept accuracy for understanding performance judgement. Both measures are
calculated by comparing these modules' output with a correct reference answer.
We report evaluation results for a spontaneous speech corpus with about 10000
utterances. We observed a nearly linear relationship between word accuracy and
concept accuracy.Comment: 4 pages PS, Latex2e source importing 2 eps figures, uses icslp.cls,
caption.sty, psfig.sty; to appear in the Proceedings of the Fourth
International Conference on Spoken Language Processing (ICSLP 96
Individual and Domain Adaptation in Sentence Planning for Dialogue
One of the biggest challenges in the development and deployment of spoken
dialogue systems is the design of the spoken language generation module. This
challenge arises from the need for the generator to adapt to many features of
the dialogue domain, user population, and dialogue context. A promising
approach is trainable generation, which uses general-purpose linguistic
knowledge that is automatically adapted to the features of interest, such as
the application domain, individual user, or user group. In this paper we
present and evaluate a trainable sentence planner for providing restaurant
information in the MATCH dialogue system. We show that trainable sentence
planning can produce complex information presentations whose quality is
comparable to the output of a template-based generator tuned to this domain. We
also show that our method easily supports adapting the sentence planner to
individuals, and that the individualized sentence planners generally perform
better than models trained and tested on a population of individuals. Previous
work has documented and utilized individual preferences for content selection,
but to our knowledge, these results provide the first demonstration of
individual preferences for sentence planning operations, affecting the content
order, discourse structure and sentence structure of system responses. Finally,
we evaluate the contribution of different feature sets, and show that, in our
application, n-gram features often do as well as features based on higher-level
linguistic representations
Referential precedents in spoken language comprehension: a review and meta-analysis
Listeners’ interpretations of referring expressions are influenced by referential
precedents—temporary conventions established in a discourse that associate linguistic
expressions with referents. A number of psycholinguistic studies have investigated how
much precedent effects depend on beliefs about the speaker’s perspective versus more
egocentric, domain-general processes. We review and provide a meta-analysis of
visual-world eyetracking studies of precedent use, focusing on three principal effects: (1) a
same speaker advantage for maintained precedents; (2) a different speaker advantage for
broken precedents; and (3) an overall main effect of precedents. Despite inconsistent claims
in the literature, our combined analysis reveals surprisingly consistent evidence supporting
the existence of all three effects, but with different temporal profiles. These findings carry
important implications for existing theoretical explanations of precedent use, and challenge
explanations based solely on the use of information about speakers’ perspectives
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