3 citations · 3 across the 2 of their papers we have counts for
7 papers
Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation
Weiting Tan, Shuoyang Ding, Huda Khayrallah +1
Neural Machine Translation (NMT) models are known to suffer from noisy inputs. To make models robust, we generate adversarial augmentation samples that attack the model and preserv…
SMRT Chatbots: Improving Non-Task-Oriented Dialog with Simulated Multiple Reference Training
Huda Khayrallah, João Sedoc
Non-task-oriented dialog models suffer from poor quality and non-diverse responses. To overcome limited conversational data, we apply Simulated Multiple Reference Training (SMRT; K…
Measuring the `I don't know' Problem through the Lens of Gricean Quantity
Huda Khayrallah, João Sedoc
We consider the intrinsic evaluation of neural generative dialog models through the lens of Grice's Maxims of Conversation (1975). Based on the maxim of Quantity (be informative),…
Simulated Multiple Reference Training Improves Low-Resource Machine Translation
Huda Khayrallah, Brian Thompson, Matt Post +1
Many valid translations exist for a given sentence, yet machine translation (MT) is trained with a single reference translation, exacerbating data sparsity in low-resource settings…
An Empirical Exploration of Curriculum Learning for Neural Machine Translation
Xuan Zhang, Gaurav Kumar, Huda Khayrallah +6
Machine translation systems based on deep neural networks are expensive to train. Curriculum learning aims to address this issue by choosing the order in which samples are presente…
Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation
Brian Thompson, Huda Khayrallah, Antonios Anastasopoulos +7
To better understand the effectiveness of continued training, we analyze the major components of a neural machine translation system (the encoder, decoder, and each embedding space…