756 research outputs found
Automatic Accuracy Prediction for AMR Parsing
Abstract Meaning Representation (AMR) represents sentences as directed,
acyclic and rooted graphs, aiming at capturing their meaning in a machine
readable format. AMR parsing converts natural language sentences into such
graphs. However, evaluating a parser on new data by means of comparison to
manually created AMR graphs is very costly. Also, we would like to be able to
detect parses of questionable quality, or preferring results of alternative
systems by selecting the ones for which we can assess good quality. We propose
AMR accuracy prediction as the task of predicting several metrics of
correctness for an automatically generated AMR parse - in absence of the
corresponding gold parse. We develop a neural end-to-end multi-output
regression model and perform three case studies: firstly, we evaluate the
model's capacity of predicting AMR parse accuracies and test whether it can
reliably assign high scores to gold parses. Secondly, we perform parse
selection based on predicted parse accuracies of candidate parses from
alternative systems, with the aim of improving overall results. Finally, we
predict system ranks for submissions from two AMR shared tasks on the basis of
their predicted parse accuracy averages. All experiments are carried out across
two different domains and show that our method is effective.Comment: accepted at *SEM 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
Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations
We evaluate the character-level translation method for neural semantic
parsing on a large corpus of sentences annotated with Abstract Meaning
Representations (AMRs). Using a sequence-to-sequence model, and some trivial
preprocessing and postprocessing of AMRs, we obtain a baseline accuracy of 53.1
(F-score on AMR-triples). We examine five different approaches to improve this
baseline result: (i) reordering AMR branches to match the word order of the
input sentence increases performance to 58.3; (ii) adding part-of-speech tags
(automatically produced) to the input shows improvement as well (57.2); (iii)
So does the introduction of super characters (conflating frequent sequences of
characters to a single character), reaching 57.4; (iv) optimizing the training
process by using pre-training and averaging a set of models increases
performance to 58.7; (v) adding silver-standard training data obtained by an
off-the-shelf parser yields the biggest improvement, resulting in an F-score of
64.0. Combining all five techniques leads to an F-score of 71.0 on holdout
data, which is state-of-the-art in AMR parsing. This is remarkable because of
the relative simplicity of the approach.Comment: Camera ready for CLIN 2017 journa
A Transition-Based Directed Acyclic Graph Parser for UCCA
We present the first parser for UCCA, a cross-linguistically applicable
framework for semantic representation, which builds on extensive typological
work and supports rapid annotation. UCCA poses a challenge for existing parsing
techniques, as it exhibits reentrancy (resulting in DAG structures),
discontinuous structures and non-terminal nodes corresponding to complex
semantic units. To our knowledge, the conjunction of these formal properties is
not supported by any existing parser. Our transition-based parser, which uses a
novel transition set and features based on bidirectional LSTMs, has value not
just for UCCA parsing: its ability to handle more general graph structures can
inform the development of parsers for other semantic DAG structures, and in
languages that frequently use discontinuous structures.Comment: 16 pages; Accepted as long paper at ACL201
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