527,310 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
Dependency parsing resources for French: Converting acquired lexical functional grammar F-Structure annotations and parsing F-Structures directly
Recent years have seen considerable success in the generation of automatically obtained wide-coverage deep grammars for natural language processing, given reliable
and large CFG-like treebanks. For research within Lexical Functional Grammar framework, these deep grammars are
typically based on an extended PCFG parsing scheme from which dependencies are extracted. However, increasing success in statistical dependency parsing suggests that such deep grammar approaches to statistical parsing could be streamlined. We explore this novel approach to deep
grammar parsing within the framework of LFG in this paper, for French, showing that best results (an f-score of 69.46) for the established integrated architecture may be obtained for French
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