We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a
s...
The sliding window approach provides an elegant way to handle contexts o...
Nearly all general-purpose neural semantic parsers generate logical form...
Instruction fine-tuned language models on a collection of instruction
an...
Transition-based parsers for Abstract Meaning Representation (AMR) rely ...
Knowledge bases (KBs) are often incomplete and constantly changing in
pr...
Despite extensive research on parsing of English sentences into Abstract...
We propose a transition-based system to transpile Abstract Meaning
Repre...
AMR parsing has experienced an unprecendented increase in performance in...
Most existing approaches for Knowledge Base Question Answering (KBQA) fo...
Predicting linearized Abstract Meaning Representation (AMR) graphs using...
Relation linking is essential to enable question answering over knowledg...
Transformer-based language models pre-trained on large amounts of text d...
Abstract Meaning Representation parsing is a sentence-to-graph predictio...
We develop high performance multilingualAbstract Meaning Representation ...
Knowledge base question answering (KBQA) is an important task in Natural...
Abstract Meaning Representation (AMR) parsing has experienced a notable
...
Modeling the parser state is key to good performance in transition-based...
The task of event detection and classification is central to most inform...
Abstract Meaning Representations (AMRs) are broad-coverage sentence-leve...
Our work involves enriching the Stack-LSTM transition-based AMR parser
(...