Leveraging abstract meaning representation for knowledge base question answering
Knowledge base question answering (KBQA) is an important task in Natural Language
Processing. Existing approaches face significant challenges including complex question …
Processing. Existing approaches face significant challenges including complex question …
MRP 2020: The second shared task on cross-framework and cross-lingual meaning representation parsing
Abstract The 2020 Shared Task at the Conference for Computational Language Learning
(CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and …
(CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and …
Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing
Predicting linearized Abstract Meaning Representation (AMR) graphs using pre-trained
sequence-to-sequence Transformer models has recently led to large improvements on AMR …
sequence-to-sequence Transformer models has recently led to large improvements on AMR …
Ensembling graph predictions for amr parsing
In many machine learning tasks, models are trained to predict structure data such as graphs.
For example, in natural language processing, it is very common to parse texts into …
For example, in natural language processing, it is very common to parse texts into …
Maximum Bayes Smatch ensemble distillation for AMR parsing
AMR 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 …
years, due to a mixture of effects including architecture improvements and transfer learning …
Inducing and using alignments for transition-based AMR parsing
Transition-based parsers for Abstract Meaning Representation (AMR) rely on node-to-word
alignments. These alignments are learned separately from parser training and require a …
alignments. These alignments are learned separately from parser training and require a …
A semantics-aware transformer model of relation linking for knowledge base question answering
T Naseem, S Ravishankar… - Proceedings of the …, 2021 - aclanthology.org
Relation linking is a crucial component of Knowledge Base Question Answering systems.
Existing systems use a wide variety of heuristics, or ensembles of multiple systems, heavily …
Existing systems use a wide variety of heuristics, or ensembles of multiple systems, heavily …
Widely interpretable semantic representation: Frameless meaning representation for broader applicability
This paper presents a novel semantic representation, WISeR, that overcomes challenges for
Abstract Meaning Representation (AMR). Despite its strengths, AMR is not easily applied to …
Abstract Meaning Representation (AMR). Despite its strengths, AMR is not easily applied to …
Meaning representations for natural languages: Design, models and applications
This tutorial reviews the design of common meaning representations, SoTA models for
predicting meaning representations, and the applications of meaning representations in a …
predicting meaning representations, and the applications of meaning representations in a …
Sygma: System for generalizable modular question answering overknowledge bases
Knowledge Base Question Answering (KBQA) tasks that in-volve complex reasoning are
emerging as an important re-search direction. However, most KBQA systems struggle …
emerging as an important re-search direction. However, most KBQA systems struggle …