5,246 research outputs found
Graph Transformer for Graph-to-Sequence Learning
The dominant graph-to-sequence transduction models employ graph neural
networks for graph representation learning, where the structural information is
reflected by the receptive field of neurons. Unlike graph neural networks that
restrict the information exchange between immediate neighborhood, we propose a
new model, known as Graph Transformer, that uses explicit relation encoding and
allows direct communication between two distant nodes. It provides a more
efficient way for global graph structure modeling. Experiments on the
applications of text generation from Abstract Meaning Representation (AMR) and
syntax-based neural machine translation show the superiority of our proposed
model. Specifically, our model achieves 27.4 BLEU on LDC2015E86 and 29.7 BLEU
on LDC2017T10 for AMR-to-text generation, outperforming the state-of-the-art
results by up to 2.2 points. On the syntax-based translation tasks, our model
establishes new single-model state-of-the-art BLEU scores, 21.3 for
English-to-German and 14.1 for English-to-Czech, improving over the existing
best results, including ensembles, by over 1 BLEU.Comment: accepted by AAAI202
Structural Neural Encoders for AMR-to-text Generation
AMR-to-text generation is a problem recently introduced to the NLP community,
in which the goal is to generate sentences from Abstract Meaning Representation
(AMR) graphs. Sequence-to-sequence models can be used to this end by converting
the AMR graphs to strings. Approaching the problem while working directly with
graphs requires the use of graph-to-sequence models that encode the AMR graph
into a vector representation. Such encoding has been shown to be beneficial in
the past, and unlike sequential encoding, it allows us to explicitly capture
reentrant structures in the AMR graphs. We investigate the extent to which
reentrancies (nodes with multiple parents) have an impact on AMR-to-text
generation by comparing graph encoders to tree encoders, where reentrancies are
not preserved. We show that improvements in the treatment of reentrancies and
long-range dependencies contribute to higher overall scores for graph encoders.
Our best model achieves 24.40 BLEU on LDC2015E86, outperforming the state of
the art by 1.1 points and 24.54 BLEU on LDC2017T10, outperforming the state of
the art by 1.24 points.Comment: Proceedings of NAACL 201
Abstract Meaning Representation for Multi-Document Summarization
Generating an abstract from a collection of documents is a desirable
capability for many real-world applications. However, abstractive approaches to
multi-document summarization have not been thoroughly investigated. This paper
studies the feasibility of using Abstract Meaning Representation (AMR), a
semantic representation of natural language grounded in linguistic theory, as a
form of content representation. Our approach condenses source documents to a
set of summary graphs following the AMR formalism. The summary graphs are then
transformed to a set of summary sentences in a surface realization step. The
framework is fully data-driven and flexible. Each component can be optimized
independently using small-scale, in-domain training data. We perform
experiments on benchmark summarization datasets and report promising results.
We also describe opportunities and challenges for advancing this line of
research.Comment: 13 page
Robust Subgraph Generation Improves Abstract Meaning Representation Parsing
The Abstract Meaning Representation (AMR) is a representation for open-domain
rich semantics, with potential use in fields like event extraction and machine
translation. Node generation, typically done using a simple dictionary lookup,
is currently an important limiting factor in AMR parsing. We propose a small
set of actions that derive AMR subgraphs by transformations on spans of text,
which allows for more robust learning of this stage. Our set of construction
actions generalize better than the previous approach, and can be learned with a
simple classifier. We improve on the previous state-of-the-art result for AMR
parsing, boosting end-to-end performance by 3 F on both the LDC2013E117 and
LDC2014T12 datasets.Comment: To appear in ACL 201
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