3 citations · 4 across the 6 of their papers we have counts for
6 papers
Understanding Syntactic Generalization in Structure-inducing Language Models
David Arps, Hassan Sajjad, Laura Kallmeyer
Structure-inducing Language Models (SiLM) are trained on a self-supervised language modeling task, and induce a hierarchical sentence representation as a byproduct when processing…
Seeing Syntax: Uncovering Syntactic Learning Limitations in Vision-Language Models
Sri Harsha Dumpala, David Arps, Sageev Oore +2
Vision-language models (VLMs), serve as foundation models for multi-modal applications such as image captioning and text-to-image generation. Recent studies have highlighted limita…
Dialogue Ontology Relation Extraction via Constrained Chain-of-Thought Decoding
Renato Vukovic, David Arps, Carel van Niekerk +4
State-of-the-art task-oriented dialogue systems typically rely on task-specific ontologies for fulfilling user queries. The majority of task-oriented dialogue data, such as custome…
Multilingual Nonce Dependency Treebanks: Understanding how Language Models represent and process syntactic structure
David Arps, Laura Kallmeyer, Younes Samih +1
We introduce SPUD (Semantically Perturbed Universal Dependencies), a framework for creating nonce treebanks for the multilingual Universal Dependencies (UD) corpora. SPUD data sati…
Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building
Omar Momen, David Arps, Laura Kallmeyer
In this paper, we describe our submission to the BabyLM Challenge 2023 shared task on data-efficient language model (LM) pretraining (Warstadt et al., 2023). We train transformer-b…
Probing for Constituency Structure in Neural Language Models
David Arps, Younes Samih, Laura Kallmeyer +1
In this paper, we investigate to which extent contextual neural language models (LMs) implicitly learn syntactic structure. More concretely, we focus on constituent structure as re…