10,249 research outputs found
A Hierarchical Neural Autoencoder for Paragraphs and Documents
Natural language generation of coherent long texts like paragraphs or longer
documents is a challenging problem for recurrent networks models. In this
paper, we explore an important step toward this generation task: training an
LSTM (Long-short term memory) auto-encoder to preserve and reconstruct
multi-sentence paragraphs. We introduce an LSTM model that hierarchically
builds an embedding for a paragraph from embeddings for sentences and words,
then decodes this embedding to reconstruct the original paragraph. We evaluate
the reconstructed paragraph using standard metrics like ROUGE and Entity Grid,
showing that neural models are able to encode texts in a way that preserve
syntactic, semantic, and discourse coherence. While only a first step toward
generating coherent text units from neural models, our work has the potential
to significantly impact natural language generation and
summarization\footnote{Code for the three models described in this paper can be
found at www.stanford.edu/~jiweil/
Discourse Structure in Machine Translation Evaluation
In this article, we explore the potential of using sentence-level discourse
structure for machine translation evaluation. We first design discourse-aware
similarity measures, which use all-subtree kernels to compare discourse parse
trees in accordance with the Rhetorical Structure Theory (RST). Then, we show
that a simple linear combination with these measures can help improve various
existing machine translation evaluation metrics regarding correlation with
human judgments both at the segment- and at the system-level. This suggests
that discourse information is complementary to the information used by many of
the existing evaluation metrics, and thus it could be taken into account when
developing richer evaluation metrics, such as the WMT-14 winning combined
metric DiscoTKparty. We also provide a detailed analysis of the relevance of
various discourse elements and relations from the RST parse trees for machine
translation evaluation. In particular we show that: (i) all aspects of the RST
tree are relevant, (ii) nuclearity is more useful than relation type, and (iii)
the similarity of the translation RST tree to the reference tree is positively
correlated with translation quality.Comment: machine translation, machine translation evaluation, discourse
analysis. Computational Linguistics, 201
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