Generic Oracles for Structured Prediction
When learned without exploration, local models for structured prediction tasks are subject to exposure bias and cannot be trained without detailed guidance. Active Imitation Learning (AIL), also known in NLP as Dynamic Oracle Learning, is a general technique for working around these issues by allowing the exploration of different outputs at training time. AIL requires oracle feedback: an oracle is any algorithm which can, given a partial candidate solution and gold annotation, find the correct (minimum loss) next output to produce. This paper describes a general finite state technique for deriving oracles. The technique describe is also efficient and will greatly expand the tasks for which AIL can be used.
Code (0)
등록된 구현이 없습니다.
Tasks
Imitation LearningPredictionStructured PredictionSimilar Papers 제목 키워드 기반
Technical Report: Graph-Structured Sparse Optimization for Connected Subgraph Detection
Structured sparse optimization is an important and challenging problem for analyzing high-dimensional data in a variety of applications such as bioinformatics, medical imaging, social networks, and astronomy. Although a …
AstronomyMANGO: Automated Multi-Agent Test Oracle Generation for Vision-Language-Action Models
Vision-Language-Action (VLA) models are emerging robotic control systems that integrate perception, language understanding, and action generation in a unified architecture. Existing testing approaches for VLA-enabled rob…
Nexus: Execution-Grounded Multi-Agent Test Oracle Synthesis
Test oracle generation in non-regression testing is a longstanding challenge in software engineering, where the goal is to produce oracles that can accurately determine whether a function under test (FUT) behaves as inte…
Program RepairGraphSteal: Structural Knowledge Stealing from Graph RAG via Traversal Reconstruction
Retrieval-Augmented Generation (RAG) enhances LLMs by grounding generation in query-relevant external evidence. Beyond unstructured text corpora, Graph RAG integrates knowledge graphs into the retrieval pipeline, enablin…
Knowledge GraphsTimeGym: Debugging for Time Series Modeling in Python
We introduce the TimeGym Forecasting Debugging Toolkit, a Python library for testing and debugging time series forecasting pipelines. TimeGym simplifies the testing forecasting pipeline by providing generic tests for for…
test driven developmentTime SeriesTime Series AnalysisTime Series Forecasting