462 citations · 2.2k across the 15 of their papers we have counts for
15 papers
Data Noising as Smoothing in Neural Network Language Models
Ziang Xie, Sida I. Wang, Jiwei Li +4
Data noising is an effective technique for regularizing neural network models. While noising is widely adopted in application domains such as vision and speech, commonly used noisi…
Data Distillation for Controlling Specificity in Dialogue Generation
Jiwei Li, Will Monroe, Dan Jurafsky
People speak at different levels of specificity in different situations. Depending on their knowledge, interlocutors, mood, etc.} A conversational agent should have this ability an…
Learning to Decode for Future Success
Jiwei Li, Will Monroe, Dan Jurafsky
We introduce a simple, general strategy to manipulate the behavior of a neural decoder that enables it to generate outputs that have specific properties of interest (e.g., sequence…
Understanding Neural Networks through Representation Erasure
Jiwei Li, Will Monroe, Dan Jurafsky
While neural networks have been successfully applied to many natural language processing tasks, they come at the cost of interpretability. In this paper, we propose a general metho…
Adversarial Learning for Neural Dialogue Generation
Jiwei Li, Will Monroe, Tianlin Shi +3
In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that ar…
A Simple, Fast Diverse Decoding Algorithm for Neural Generation
Jiwei Li, Will Monroe, Dan Jurafsky
In this paper, we propose a simple, fast decoding algorithm that fosters diversity in neural generation. The algorithm modifies the standard beam search algorithm by adding an inte…