Papers by David Arps
Multilingual Nonce Dependency Treebanks: Understanding how Language Models Represent and Process Syntactic Structure (2024.naacl-long)
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| Challenge: | a number of studies have focused on making explicit the linguistic information encoded in language models (LMs) however, this method has been criticized for various reasons. |
| Approach: | They introduce a framework for creating nonce treebanks for multilingual UD corpora . they investigate word co-occurrence statistics and show how nonce data affects the performance of syntactic dependency probes. |
| Outcome: | The proposed framework satisfies syntactic argument structure and ensures grammaticality via language-specific rules. |
Probing for Constituency Structure in Neural Language Models (2022.findings-emnlp)
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| Challenge: | Using standard probing techniques, we examine whether contextual neural language models implicitly learn syntactic structure. |
| Approach: | They investigate to which extent contextual neural language models implicitly learn syntactic structure. |
| Outcome: | The proposed model is able to represent constituents of different categories within the neuron activations of a LM such as RoBERTa with high performance even on manipulated data. |
A Parser for LTAG and Frame Semantics (L18-1)
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| Challenge: | Existing parsers for Lexicalized Tree Adjoining Grammars and frame semantics are difficult to use due to the size of the resources to develop. |
| Approach: | They propose a parser which uses Lexicalized Tree Adjoining Grammars and frame semantics to combine them. |
| Outcome: | The proposed grammars are based on Lexicalized Tree Adjoining Grammars and frame semantics. |