arXiv (All)
open access
The Impact of Synthetic Data Augmentation on Discourse-Pragmatic Function Classification
Synthetic data augmentation has become a common strategy for addressing class imbalance in NLP, but most approaches focus on the quantity and diversity of generated examples rather than their geometric relationship to real training data. We investigate this question in the context of discourse pragm
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation#1062426
- Unifying Conformal Language Tasks with In-Context Ensembles#925378
- Unifying Conformal Language Tasks with In-Context Ensembles#921142
- Spoken Corpus Linguistics#341224
- A Formal Limitation on Learning Human Language From Textual Corpora#792555
Record · ID 926550
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive