565 citations · 818 across the 33 of their papers we have counts for
15 papers · 1 filter
Unsupervised Label Refinement Improves Dataless Text Classification
Zewei Chu, Karl Stratos, Kevin Gimpel
Dataless text classification is capable of classifying documents into previously unseen labels by assigning a score to any document paired with a label description. While promising…
WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections
Mingda Chen, Sam Wiseman, Kevin Gimpel
Datasets for data-to-text generation typically focus either on multi-domain, single-sentence generation or on single-domain, long-form generation. In this work, we cast generating…
Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks
Shubham Toshniwal, Sam Wiseman, Allyson Ettinger +2
Long document coreference resolution remains a challenging task due to the large memory and runtime requirements of current models. Recent work doing incremental coreference resolu…
Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference
Xiaoan Ding, Tianyu Liu, Baobao Chang +2
While discriminative neural network classifiers are generally preferred, recent work has shown advantages of generative classifiers in term of data efficiency and robustness. In th…
On the Role of Supervision in Unsupervised Constituency Parsing
Haoyue Shi, Karen Livescu, Kevin Gimpel
We analyze several recent unsupervised constituency parsing models, which are tuned with respect to the parsing score on the Wall Street Journal (WSJ) development set (1,700…
An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks
Lifu Tu, Tianyu Liu, Kevin Gimpel
Many tasks in natural language processing involve predicting structured outputs, e.g., sequence labeling, semantic role labeling, parsing, and machine translation. Researchers are…