54 citations · 77 across the 7 of their papers we have counts for
6 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…
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…
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…
Discrete Latent Variable Representations for Low-Resource Text Classification
Shuning Jin, Sam Wiseman, Karl Stratos +1
While much work on deep latent variable models of text uses continuous latent variables, discrete latent variables are interesting because they are more interpretable and typically…
ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation
Lifu Tu, Richard Yuanzhe Pang, Sam Wiseman +1
We propose to train a non-autoregressive machine translation model to minimize the energy defined by a pretrained autoregressive model. In particular, we view our non-autoregressiv…
Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information
Karl Stratos, Sam Wiseman
We propose learning discrete structured representations from unlabeled data by maximizing the mutual information between a structured latent variable and a target variable. Calcula…