Learning Beyond Limits: Multitask Learning and Synthetic Data for Low-Resource Canonical Morpheme Segmentation
We introduce a transformer-based morpheme segmentation system that augments a low-resource training signal through multitask learning and LLM-generated synthetic data. Our framework jointly predicts morphological segments and glosses from orthographic input, leveraging shared linguistic representations obtained through a common documentary process to enhance model generalization. To further address data scarcity, we integrate synthetic training data generated by large language models (LLMs) using in-context learning. Experimental results on the SIGMORPHON 2023 dataset show that our approach significantly improves word-level segmentation accuracy and morpheme-level F1-score across multiple low-resource languages.
Code (0)
등록된 구현이 없습니다.
Tasks
In-Context LearningSegmentationSimilar Papers 제목 키워드 기반
When More Data Doesn't Help: Limits of Adaptation in Multitask Learning
Multitask learning and related frameworks have achieved tremendous success in modern applications. In multitask learning problem, we are given a set of heterogeneous datasets collected from related source tasks and hope …
Scalable Multi-Agent Lab Framework for Lab Optimization
Autonomous materials research systems allow scientists to fail smarter, learn faster, and spend less resources in their studies. As these systems grow in number, capability, and complexity, a new challenge arises - how w…
Decision MakingManagementMultiTab: A Scalable Foundation for Multitask Learning on Tabular Data
Tabular data is the most abundant data type in the world, powering systems in finance, healthcare, e-commerce, and beyond. As tabular datasets grow and span multiple related targets, there is an increasing need to exploi…
Recommendation SystemsBusiness Process Simulation: Probabilistic Modeling of Intermittent Resource Availability and Multitasking Behavior
In business process simulation, resource availability is typically modeled by assigning a calendar to each resource, e.g., Monday-Friday, 9:00-18:00. Resources are assumed to be always available during each time slot in …
ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL
Despite the significant advancements in Text-to-SQL (Text2SQL) facilitated by large language models (LLMs), the latest state-of-the-art techniques are still trapped in the in-context learning of closed-source LLMs (e.g.,…
In-Context LearningText to SQLText-To-SQL