10 citations · 19 across the 4 of their papers we have counts for
4 papers · 1 filter
Semi-Supervised Few-Shot Intent Classification and Slot Filling
Samyadeep Basu, Karine lp Kiun Chong, Amr Sharaf +7
Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems. Collecting and annotating large amounts of data t…
Meta-Learning for Few-Shot NMT Adaptation
Amr Sharaf, Hany Hassan, Hal Daumé
We present META-MT, a meta-learning approach to adapt Neural Machine Translation (NMT) systems in a few-shot setting. META-MT provides a new approach to make NMT models easily adap…
Cross-Lingual Approaches to Reference Resolution in Dialogue Systems
Amr Sharaf, Arpit Gupta, Hancheng Ge +2
In the slot-filling paradigm, where a user can refer back to slots in the context during the conversation, the goal of the contextual understanding system is to resolve the referri…
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task
Amr Sharaf, Shi Feng, Khanh Nguyen +2
We describe the University of Maryland machine translation systems submitted to the WMT17 German-English Bandit Learning Task. The task is to adapt a translation system to a new do…