Summary of Do Language Models Capture Implied Discourse Meanings? An Investigation with Exhaustivity Implicatures Of Korean Morphology, by Hagyeong Shin et al.
Do language models capture implied discourse meanings? An investigation with exhaustivity implicatures of Korean morphology
by Hagyeong Shin, Sean Trott
First submitted to arxiv on: 15 May 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 The proposed research investigates whether large language models can capture implied discourse-level meanings associated with Differential Object Marking (DOM) in Korean. The study focuses on evaluating the ability of distributional language models to recover both semantic features of words and discourse features that influence DOM marker selection. The results indicate that while models can encode semantic features, capturing discourse meanings of grammatical markers proves more challenging than encoding those of discourse markers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores whether big computer programs can understand hidden meaning in sentences. In Korean, there are special markers that help show which nouns are important or not. These markers have different meanings depending on the context of a sentence. Scientists want to know if these computer programs can figure out what these markers mean when they’re used in different ways. The study shows that while these programs can understand some basic meanings, understanding more complex meanings is harder. |
Keywords
* Artificial intelligence * Discourse
