11 citations · 19 across the 6 of their papers we have counts for
10 papers
Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing Models
Weiqi Sun, Haidar Khan, Nicolas Guenon des Mesnards +2
Semantic parsing is a key NLP task that maps natural language to structured meaning representations. As in many other NLP tasks, SOTA performance in semantic parsing is now attaine…
Output Randomization: A Novel Defense for both White-box and Black-box Adversarial Models
Daniel Park, Haidar Khan, Azer Khan +2
Adversarial examples pose a threat to deep neural network models in a variety of scenarios, from settings where the adversary has complete knowledge of the model in a "white box" s…
Using multiple ASR hypotheses to boost i18n NLU performance
Charith Peris, Gokmen Oz, Khadige Abboud +3
Current voice assistants typically use the best hypothesis yielded by their Automatic Speech Recognition (ASR) module as input to their Natural Language Understanding (NLU) module,…
Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings
Prafull Prakash, Saurabh Kumar Shashidhar, Wenlong Zhao +3
The current state-of-the-art task-oriented semantic parsing models use BERT or RoBERTa as pretrained encoders; these models have huge memory footprints. This poses a challenge to t…
Don't Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding
Qile Zhu, Haidar Khan, Saleh Soltan +2
Semantic parsing is one of the key components of natural language understanding systems. A successful parse transforms an input utterance to an action that is easily understood by…
Optimal Mini-Batch Size Selection for Fast Gradient Descent
Michael P. Perrone, Haidar Khan, Changhoan Kim +3
This paper presents a methodology for selecting the mini-batch size that minimizes Stochastic Gradient Descent (SGD) learning time for single and multiple learner problems. By deco…