5 papers
Meta-Learning for Domain Generalization in Semantic Parsing
Bailin Wang, Mirella Lapata, Ivan Titov
The importance of building semantic parsers which can be applied to new domains and generate programs unseen at training has long been acknowledged, and datasets testing out-of-dom…
Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs
Bailin Wang, Ivan Titov, Mirella Lapata
Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution against a real-world environment produces…
Combining Spans into Entities: A Neural Two-Stage Approach for Recognizing Discontiguous Entities
Bailin Wang, Wei Lu
In medical documents, it is possible that an entity of interest not only contains a discontiguous sequence of words but also overlaps with another entity. Entities of such structur…
Neural Segmental Hypergraphs for Overlapping Mention Recognition
Bailin Wang, Wei Lu
In this work, we propose a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets. We show that our model buil…
A Neural Transition-based Model for Nested Mention Recognition
Bailin Wang, Wei Lu, Yu Wang +1
It is common that entity mentions can contain other mentions recursively. This paper introduces a scalable transition-based method to model the nested structure of mentions. We fir…