6 citations · 8 across the 7 of their papers we have counts for
12 papers · 1 filter
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…
Mining Knowledge for Natural Language Inference from Wikipedia Categories
Mingda Chen, Zewei Chu, Karl Stratos +1
Accurate lexical entailment (LE) and natural language inference (NLI) often require large quantities of costly annotations. To alleviate the need for labeled data, we introduce Wik…
Exemplar-Controllable Paraphrasing and Translation using Bitext
Mingda Chen, Sam Wiseman, Kevin Gimpel
Most prior work on exemplar-based syntactically controlled paraphrase generation relies on automatically-constructed large-scale paraphrase datasets, which are costly to create. We…
Learning Probabilistic Sentence Representations from Paraphrases
Mingda Chen, Kevin Gimpel
Probabilistic word embeddings have shown effectiveness in capturing notions of generality and entailment, but there is very little work on doing the analogous type of investigation…
How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions
Zewei Chu, Mingda Chen, Jing Chen +4
We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one. Our multi-domain question rewriting MQR dataset is construc…
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman +3
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases be…