1 citations · 1 across the 2 of their papers we have counts for
5 papers
Why Pretraining Fails to Share Cross-Lingual Knowledge
Adam Gaber, Uriel Dolev, Elisabeth Fittschen +3
Large Language Models (LLMs) have made remarkable progress in the processing and modeling of many languages. Yet, unlike human multilinguals, they exhibit surprisingly limited cros…
Uncovering Autoregressive LLM Knowledge of Thematic Fit in Event Representation
Safeyah Khaled Alshemali, Daniel Bauer, Yuval Marton
The thematic fit estimation task measures semantic arguments' compatibility with a given semantic role for a given predicate. We investigate if autoregressive LLMs have consistent,…
Multi-Task Learning for Joint Semantic Role and Proto-Role Labeling
Aashish Arora, Harshitha Malireddi, Daniel Bauer +2
We put forward an end-to-end multi-step machine learning model which jointly labels semantic roles and the proto-roles of Dowty (1991), given a sentence and the predicates therein.…
Where's the Learning in Representation Learning for Compositional Semantics and the Case of Thematic Fit
Mughilan Muthupari, Samrat Halder, Asad Sayeed +1
Observing that for certain NLP tasks, such as semantic role prediction or thematic fit estimation, random embeddings perform as well as pretrained embeddings, we explore what setti…
Thematic Fit Bits: Annotation Quality and Quantity Interplay for Event Participant Representation
Yuval Marton, Asad Sayeed
Modeling thematic fit (a verb--argument compositional semantics task) currently requires a very large burden of labeled data. We take a linguistically machine-annotated large corpu…