Selected Papers
Distributional Inclusion Hypothesis and Quantifications: Probing for Hypernymy in Functinoal Distributional Semantics
In arXiv
TL;DR: This work reveals that Functional Distributional Semantics (FDS) captures hypernymy if trained on a corpus that follows the Distributional Inclusion Hypothesis (DIH). We further propose an alternative FDS training objective that handles simple universal quantifications, which enables hypernymy learning under the reverse of the DIH and is shown to improve hypernymy detection from corpora.
Functional Distributional Semantics at Scale
In *SEM 2023
TL;DR: Functional Distributional Semantics (FDS) attempts to learn truth-conditional meanings of words from distributional information in a corpus. This work extends the applicability of FDS to sentences with more complex structures.
Semantic Composition with PSHRG for Derivation Tree Reconstruction from Graph-Based Meaning Representations
In ACL 2022
TL;DR: This work explores the use of a formal graph grammar in approximating the composition of meaning representation graphs and recovering their synatctic derivations. Surface realization becomes more explainable with the syntax trees.