Emergent Word Order Universals from Cognitively-Motivated Language Models
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911910391185408 |
|---|---|
| author | Kuribayashi, Tatsuki Ueda, Ryo Yoshida, Ryo Oseki, Yohei Briscoe, Ted Baldwin, Timothy |
| author_facet | Kuribayashi, Tatsuki Ueda, Ryo Yoshida, Ryo Oseki, Yohei Briscoe, Ted Baldwin, Timothy |
| contents | The world's languages exhibit certain so-called typological or implicational universals; for example, Subject-Object-Verb (SOV) languages typically use postpositions. Explaining the source of such biases is a key goal of linguistics. We study word-order universals through a computational simulation with language models (LMs). Our experiments show that typologically-typical word orders tend to have lower perplexity estimated by LMs with cognitively plausible biases: syntactic biases, specific parsing strategies, and memory limitations. This suggests that the interplay of cognitive biases and predictability (perplexity) can explain many aspects of word-order universals. It also showcases the advantage of cognitively-motivated LMs, typically employed in cognitive modeling, in the simulation of language universals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12363 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Emergent Word Order Universals from Cognitively-Motivated Language Models Kuribayashi, Tatsuki Ueda, Ryo Yoshida, Ryo Oseki, Yohei Briscoe, Ted Baldwin, Timothy Computation and Language The world's languages exhibit certain so-called typological or implicational universals; for example, Subject-Object-Verb (SOV) languages typically use postpositions. Explaining the source of such biases is a key goal of linguistics. We study word-order universals through a computational simulation with language models (LMs). Our experiments show that typologically-typical word orders tend to have lower perplexity estimated by LMs with cognitively plausible biases: syntactic biases, specific parsing strategies, and memory limitations. This suggests that the interplay of cognitive biases and predictability (perplexity) can explain many aspects of word-order universals. It also showcases the advantage of cognitively-motivated LMs, typically employed in cognitive modeling, in the simulation of language universals. |
| title | Emergent Word Order Universals from Cognitively-Motivated Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.12363 |