no code implementations • ACL (SIGMORPHON) 2021 • Michael Hammond
In this paper we explore a very simple neural approach to mapping orthography to phonetic transcription in a low-resource context.
no code implementations • LREC 2022 • Andrew Zupon, Andrew Carnie, Michael Hammond, Mihai Surdeanu
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.
no code implementations • CONLL 2017 • Rebecca Sharp, Mihai Surdeanu, Peter Jansen, Marco A. Valenzuela-Esc{\'a}rcega, Peter Clark, Michael Hammond
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
no code implementations • EMNLP 2016 • Rebecca Sharp, Mihai Surdeanu, Peter Jansen, Peter Clark, Michael Hammond
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.