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Found 4 papers, 0 papers with code
Data augmentation for low-resource grapheme-to-phoneme mapping
In this paper we explore a very simple neural approach to mapping orthography to phonetic transcription in a low-resource context.
Automatic Correction of Syntactic Dependency Annotation Differences
Annotation inconsistencies between data sets can cause problems for low-resource NLP, where noisy or inconsistent data cannot be as easily replaced compared with resource-rich languages.
Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification
We propose a neural network architecture for QA that reranks answer justifications as an intermediate (and human-interpretable) step in answer selection.
Ranked #1 on
Question Answering
on AI2 Kaggle Dataset
Creating Causal Embeddings for Question Answering with Minimal Supervision
We argue that a better approach is to look for answers that are related to the question in a relevant way, according to the information need of the question, which may be determined through task-specific embeddings.
