activity
20132024
most citedA Deep Reinforced Model for Abstractive Summarization

1.3k citations · 12.3k across the 104 of their papers we have counts for

collaborators
Showing 2020Show all

38 papers · 1 filter

cs.CL2020★ 16 cited

Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing

Xi Victoria Lin, Richard Socher, Caiming Xiong

We present BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing. BRIDGE rep…

cs.LG2020★ 13 cited

Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization

Stanislaw Jastrzebski, Devansh Arpit, Oliver Astrand +6

The early phase of training a deep neural network has a dramatic effect on the local curvature of the loss function. For instance, using a small learning rate does not guarantee st…

cs.CL2020★ 20 cited

Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference

Jian-Guo Zhang, Kazuma Hashimoto, Wenhao Liu +5

Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is…

cs.LG2020★ 8 cited

Online Structured Meta-learning

Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi +3

Learning quickly is of great importance for machine intelligence deployed in online platforms. With the capability of transferring knowledge from learned tasks, meta-learning has s…

cs.CL2020★ 5 cited

GeDi: Generative Discriminator Guided Sequence Generation

Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann +4

While large-scale language models (LMs) are able to imitate the distribution of natural language well enough to generate realistic text, it is difficult to control which regions of…

cs.CL2020★ 28 cited

Explaining and Improving Model Behavior with k Nearest Neighbor Representations

Nazneen Fatema Rajani, Ben Krause, Wengpeng Yin +3

Interpretability techniques in NLP have mainly focused on understanding individual predictions using attention visualization or gradient-based saliency maps over tokens. We propose…