349 research outputs found
AMR Parsing as Graph Prediction with Latent Alignment
Abstract meaning representations (AMRs) are broad-coverage sentence-level
semantic representations. AMRs represent sentences as rooted labeled directed
acyclic graphs. AMR parsing is challenging partly due to the lack of annotated
alignments between nodes in the graphs and words in the corresponding
sentences. We introduce a neural parser which treats alignments as latent
variables within a joint probabilistic model of concepts, relations and
alignments. As exact inference requires marginalizing over alignments and is
infeasible, we use the variational auto-encoding framework and a continuous
relaxation of the discrete alignments. We show that joint modeling is
preferable to using a pipeline of align and parse. The parser achieves the best
reported results on the standard benchmark (74.4% on LDC2016E25).Comment: Accepted to ACL 201
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
Matching Natural Language Sentences with Hierarchical Sentence Factorization
Semantic matching of natural language sentences or identifying the
relationship between two sentences is a core research problem underlying many
natural language tasks. Depending on whether training data is available, prior
research has proposed both unsupervised distance-based schemes and supervised
deep learning schemes for sentence matching. However, previous approaches
either omit or fail to fully utilize the ordered, hierarchical, and flexible
structures of language objects, as well as the interactions between them. In
this paper, we propose Hierarchical Sentence Factorization---a technique to
factorize a sentence into a hierarchical representation, with the components at
each different scale reordered into a "predicate-argument" form. The proposed
sentence factorization technique leads to the invention of: 1) a new
unsupervised distance metric which calculates the semantic distance between a
pair of text snippets by solving a penalized optimal transport problem while
preserving the logical relationship of words in the reordered sentences, and 2)
new multi-scale deep learning models for supervised semantic training, based on
factorized sentence hierarchies. We apply our techniques to text-pair
similarity estimation and text-pair relationship classification tasks, based on
multiple datasets such as STSbenchmark, the Microsoft Research paraphrase
identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments
show that the proposed hierarchical sentence factorization can be used to
significantly improve the performance of existing unsupervised distance-based
metrics as well as multiple supervised deep learning models based on the
convolutional neural network (CNN) and long short-term memory (LSTM).Comment: Accepted by WWW 2018, 10 page
AMR Dependency Parsing with a Typed Semantic Algebra
We present a semantic parser for Abstract Meaning Representations which
learns to parse strings into tree representations of the compositional
structure of an AMR graph. This allows us to use standard neural techniques for
supertagging and dependency tree parsing, constrained by a linguistically
principled type system. We present two approximative decoding algorithms, which
achieve state-of-the-art accuracy and outperform strong baselines.Comment: This paper will be presented at ACL 2018 (see
https://acl2018.org/programme/papers/
Graph-based broad-coverage semantic parsing
Many broad-coverage meaning representations can be characterized as directed graphs,
where nodes represent semantic concepts and directed edges represent semantic relations among the concepts. The task of semantic parsing is to generate such a meaning
representation from a sentence. It is quite natural to adopt a graph-based approach for
parsing, where nodes are identified conditioning on the individual words, and edges
are labeled conditioning on the pairs of nodes. However, there are two issues with
applying this simple and interpretable graph-based approach for semantic parsing:
first, the anchoring of nodes to words can be implicit and non-injective in several
formalisms (Oepen et al., 2019, 2020). This means we do not know which nodes
should be generated from which individual word and how many of them. Consequently, it makes a probabilistic formulation of the training objective problematical;
second, graph-based parsers typically predict edge labels independent from each other.
Such an independence assumption, while being sensible from an algorithmic point of
view, could limit the expressiveness of statistical modeling. Consequently, it might fail
to capture the true distribution of semantic graphs.
In this thesis, instead of a pipeline approach to obtain the anchoring, we propose to
model the implicit anchoring as a latent variable in a probabilistic model. We induce
such a latent variable jointly with the graph-based parser in an end-to-end differentiable training. In particular, we test our method on Abstract Meaning Representation
(AMR) parsing (Banarescu et al., 2013). AMR represents sentence meaning with a
directed acyclic graph, where the anchoring of nodes to words is implicit and could be
many-to-one. Initially, we propose a rule-based system that circumvents the many-to-one anchoring by combing nodes in some pre-specified subgraphs in AMR and treats
the alignment as a latent variable. Next, we remove the need for such a rule-based system by treating both graph segmentation and alignment as latent variables. Still, our
graph-based parsers are parameterized by neural modules that require gradient-based
optimization. Consequently, training graph-based parsers with our discrete latent variables can be challenging. By combing deep variational inference and differentiable
sampling, our models can be trained end-to-end. To overcome the limitation of graph-based parsing and capture interdependency in the output, we further adopt iterative
refinement. Starting with an output whose parts are independently predicted, we iteratively refine it conditioning on the previous prediction. We test this method on
semantic role labeling (Gildea and Jurafsky, 2000). Semantic role labeling is the task
of predicting the predicate-argument structure. In particular, semantic roles between
the predicate and its arguments need to be labeled, and those semantic roles are interdependent. Overall, our refinement strategy results in an effective model, outperforming
strong factorized baseline models
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