Summary of Linguistic Structure Induction From Language Models, by Omar Momen
Linguistic Structure Induction from Language Models
by Omar Momen
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates whether language models (LMs) implicitly represent syntactic hierarchies. The authors focus on producing constituency and dependency structures from LMs in an unsupervised setting, reviewing critical methods and highlighting a line of work using Syntactic Distance. They present StructFormer (SF), a transformer encoder architecture with a parser network that produces constituency and dependency structures. Six experiments are conducted to analyze challenges, including repositioning the parser network, evaluating subword-based induced trees, and benchmarking models on linguistic tasks. The results support further development in retrofitting transformer-based models to induce syntactic structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how language models represent sentences. It explores whether these models can understand the way words are organized in a sentence. Researchers use special frameworks called Constituency and Dependency to study this. They also look at a model called StructFormer, which helps create these sentence structures. The authors do many experiments to see what works best. The results show that these models can be useful for understanding sentences. |
Keywords
» Artificial intelligence » Encoder » Transformer » Unsupervised
