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CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks
We describe the system used in our participa- tion in the AMR Parsing task for SemEval- 2016. Our parser does not rely on a syntactic pre-parse, or heavily engineered features, …...
ICL-HD at SemEval-2016 Task 8: Meaning Representation Parsing - Augmenting AMR Parsing with a Preposition Semantic Role Labeling Neural Network
We describe our submission system to the SemEval-2016 Task 8 on Abstract Meaning Representation (AMR) Parsing. We attempt to improve AMR parsing by exploiting prepo- sition semantic role labeling information …...
Neural Shift-Reduce CCG Semantic Parsing
We present a shift-reduce CCG semantic parser. Our parser uses a neural network ar- chitecture that balances model capacity and computational cost. We train by transferring a model from a …...
UofR at SemEval-2016 Task 8: Learning Synchronous Hyperedge Replacement Grammar for AMR Parsing
In this paper, we apply a synchronous-graph- grammar-based approach to SemEval-2016 Task 8, Meaning Representation Parsing. In particular, we learn Synchronous Hyperedge Replacement Grammar (SHRG) rules from aligned pairs of …...
Addressing the Data Sparsity Issue in Neural AMR Parsing
AbstractNeural attention models have achieved great success in different NLP tasks. However, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue. In …...
Getting the Most out of AMR Parsing
AbstractThis paper proposes to tackle the AMR parsing bottleneck by improving two components of an AMR parser: concept identification and alignment. We first build a Bidirectional LSTM based concept identifier …...
AMR Parsing as Sequence-to-Graph Transduction
AbstractWe propose an attention-based model that treats AMR parsing as sequence-to-graph transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser …...
Automatic Accuracy Prediction for AMR Parsing
AbstractAbstract 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 …...
SemBleu: A Robust Metric for AMR Parsing Evaluation
AbstractEvaluating AMR parsing accuracy involves comparing pairs of AMR graphs. The major evaluation metric, SMATCH (Cai and Knight, 2013), searches for one-to-one mappings between the nodes of two AMRs with …...
AMR Parsing via Graph-Sequence Iterative Inference
AbstractWe propose a new end-to-end model that treats AMR parsing as a series of dual decisions on the input sequence and the incrementally constructed graph. At each time step, our …...
Constructing Web-Accessible Semantic Role Labels and Frames for Japanese as Additions to the NPCMJ Parsed Corpus
AbstractAs part of constructing the NINJAL Parsed Corpus of Modern Japanese (NPCMJ), a web-accessible language resource, we are adding frame information for predicates, together with two types of semantic role …...
Improving AMR Parsing with Sequence-to-Sequence Pre-training
AbstractIn the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good …...
Pushing the Limits of AMR Parsing with Self-Learning
AbstractAbstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel …...
Incorporating EDS Graph for AMR Parsing
AbstractAMR (Abstract Meaning Representation) and EDS (Elementary Dependency Structures) are two popular meaning representations in NLP/NLU. AMR is more abstract and conceptual, while EDS is more low level, closer to …...
Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments
AbstractWe present algorithms for aligning components of Abstract Meaning Representation (AMR) graphs to spans in English sentences. We leverage unsupervised learning in combination with heuristics, taking the best of both …...
XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation
AbstractDue to the scarcity of annotated data, Abstract Meaning Representation (AMR) research is relatively limited and challenging for languages other than English. Upon the availability of English AMR dataset and …...
Cross-domain Generalization for AMR Parsing
AbstractAbstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input. Recently, there has been notable growth in AMR parsing performance. However, most existing work focuses on …...
Maximum Bayes Smatch Ensemble Distillation for AMR Parsing
AbstractAMR parsing has experienced an unprecendented increase in performance in the last three years, due to a mixture of effects including architecture improvements and transfer learning. Self-learning techniques have also …...