5 research outputs found
Learning Embeddings to lexicalise RDF Properties
International audienceA difficult task when generating text from knowledge bases (KB) consists in finding appropriate lexicalisations for KB symbols. We present an approach for lexicalis-ing knowledge base relations and apply it to DBPedia data. Our model learns low-dimensional embeddings of words and RDF resources and uses these representations to score RDF properties against candidate lexicalisations. Training our model using (i) pairs of RDF triples and automatically generated verbalisations of these triples and (ii) pairs of paraphrases extracted from various resources, yields competitive results on DBPedia data
Bootstrapping Generators from Noisy Data
A core step in statistical data-to-text generation concerns learning
correspondences between structured data representations (e.g., facts in a
database) and associated texts. In this paper we aim to bootstrap generators
from large scale datasets where the data (e.g., DBPedia facts) and related
texts (e.g., Wikipedia abstracts) are loosely aligned. We tackle this
challenging task by introducing a special-purpose content selection mechanism.
We use multi-instance learning to automatically discover correspondences
between data and text pairs and show how these can be used to enhance the
content signal while training an encoder-decoder architecture. Experimental
results demonstrate that models trained with content-specific objectives
improve upon a vanilla encoder-decoder which solely relies on soft attention.Comment: NAACL 201
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