160 research outputs found
Findings of the E2E NLG Challenge
This paper summarises the experimental setup and results of the first shared
task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue
systems. Recent end-to-end generation systems are promising since they reduce
the need for data annotation. However, they are currently limited to small,
delexicalised datasets. The E2E NLG shared task aims to assess whether these
novel approaches can generate better-quality output by learning from a dataset
containing higher lexical richness, syntactic complexity and diverse discourse
phenomena. We compare 62 systems submitted by 17 institutions, covering a wide
range of approaches, including machine learning architectures -- with the
majority implementing sequence-to-sequence models (seq2seq) -- as well as
systems based on grammatical rules and templates.Comment: Accepted to INLG 201
RankME: Reliable Human Ratings for Natural Language Generation
Human evaluation for natural language generation (NLG) often suffers from
inconsistent user ratings. While previous research tends to attribute this
problem to individual user preferences, we show that the quality of human
judgements can also be improved by experimental design. We present a novel
rank-based magnitude estimation method (RankME), which combines the use of
continuous scales and relative assessments. We show that RankME significantly
improves the reliability and consistency of human ratings compared to
traditional evaluation methods. In addition, we show that it is possible to
evaluate NLG systems according to multiple, distinct criteria, which is
important for error analysis. Finally, we demonstrate that RankME, in
combination with Bayesian estimation of system quality, is a cost-effective
alternative for ranking multiple NLG systems.Comment: Accepted to NAACL 2018 (The 2018 Conference of the North American
Chapter of the Association for Computational Linguistics
Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge
This paper provides a comprehensive analysis of the first shared task on
End-to-End Natural Language Generation (NLG) and identifies avenues for future
research based on the results. This shared task aimed to assess whether recent
end-to-end NLG systems can generate more complex output by learning from
datasets containing higher lexical richness, syntactic complexity and diverse
discourse phenomena. Introducing novel automatic and human metrics, we compare
62 systems submitted by 17 institutions, covering a wide range of approaches,
including machine learning architectures -- with the majority implementing
sequence-to-sequence models (seq2seq) -- as well as systems based on
grammatical rules and templates. Seq2seq-based systems have demonstrated a
great potential for NLG in the challenge. We find that seq2seq systems
generally score high in terms of word-overlap metrics and human evaluations of
naturalness -- with the winning SLUG system (Juraska et al., 2018) being
seq2seq-based. However, vanilla seq2seq models often fail to correctly express
a given meaning representation if they lack a strong semantic control mechanism
applied during decoding. Moreover, seq2seq models can be outperformed by
hand-engineered systems in terms of overall quality, as well as complexity,
length and diversity of outputs. This research has influenced, inspired and
motivated a number of recent studies outwith the original competition, which we
also summarise as part of this paper.Comment: Computer Speech and Language, final accepted manuscript (in press
Referenceless Quality Estimation for Natural Language Generation
Traditional automatic evaluation measures for natural language generation
(NLG) use costly human-authored references to estimate the quality of a system
output. In this paper, we propose a referenceless quality estimation (QE)
approach based on recurrent neural networks, which predicts a quality score for
a NLG system output by comparing it to the source meaning representation only.
Our method outperforms traditional metrics and a constant baseline in most
respects; we also show that synthetic data helps to increase correlation
results by 21% compared to the base system. Our results are comparable to
results obtained in similar QE tasks despite the more challenging setting.Comment: Accepted as a regular paper to 1st Workshop on Learning to Generate
Natural Language (LGNL), Sydney, 10 August 201
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