2 citations · 3 across the 2 of their papers we have counts for
10 papers · 1 filter
Structural generalization is hard for sequence-to-sequence models
Yuekun Yao, Alexander Koller
Sequence-to-sequence (seq2seq) models have been successful across many NLP tasks, including ones that require predicting linguistic structure. However, recent work on compositional…
Compositional Generalization Requires Compositional Parsers
Pia Weißenhorn, Yuekun Yao, Lucia Donatelli +1
A rapidly growing body of research on compositional generalization investigates the ability of a semantic parser to dynamically recombine linguistic elements seen in training into…
Learning compositional structures for semantic graph parsing
Jonas Groschwitz, Meaghan Fowlie, Alexander Koller
AM dependency parsing is a method for neural semantic graph parsing that exploits the principle of compositionality. While AM dependency parsers have been shown to be fast and accu…
Generating Instructions at Different Levels of Abstraction
Arne Köhn, Julia Wichlacz, Álvaro Torralba +3
When generating technical instructions, it is often convenient to describe complex objects in the world at different levels of abstraction. A novice user might need an object expla…
Fast semantic parsing with well-typedness guarantees
Matthias Lindemann, Jonas Groschwitz, Alexander Koller
AM dependency parsing is a linguistically principled method for neural semantic parsing with high accuracy across multiple graphbanks. It relies on a type system that models semant…
Normalizing Compositional Structures Across Graphbanks
Lucia Donatelli, Jonas Groschwitz, Alexander Koller +2
The emergence of a variety of graph-based meaning representations (MRs) has sparked an important conversation about how to adequately represent semantic structure. These MRs exhibi…