5 citations · 8 across the 2 of their papers we have counts for
6 papers
Investigating Biases in Textual Entailment Datasets
Shawn Tan, Yikang Shen, Chin-wei Huang +1
The ability to understand logical relationships between sentences is an important task in language understanding. To aid in progress for this task, researchers have collected datas…
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
Yikang Shen, Shawn Tan, Alessandro Sordoni +1
Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger constituent ends, all of the smaller cons…
BanditSum: Extractive Summarization as a Contextual Bandit
Yue Dong, Yikang Shen, Eric Crawford +2
In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels. We call…
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
Yikang Shen, Zhouhan Lin, Athul Paul Jacob +3
In this work, we propose a novel constituency parsing scheme. The model predicts a vector of real-valued scalars, named syntactic distances, for each split position in the input se…
Generating Contradictory, Neutral, and Entailing Sentences
Yikang Shen, Shawn Tan, Chin-Wei Huang +1
Learning distributed sentence representations remains an interesting problem in the field of Natural Language Processing (NLP). We want to learn a model that approximates the condi…
Self-organized Hierarchical Softmax
Yikang Shen, Shawn Tan, Chrisopher Pal +1
We propose a new self-organizing hierarchical softmax formulation for neural-network-based language models over large vocabularies. Instead of using a predefined hierarchical struc…