54 citations · 77 across the 7 of their papers we have counts for
14 papers · 1 filter
On Generalization in Coreference Resolution
Shubham Toshniwal, Patrick Xia, Sam Wiseman +2
While coreference resolution is defined independently of dataset domain, most models for performing coreference resolution do not transfer well to unseen domains. We consolidate a…
Data-to-text Generation by Splicing Together Nearest Neighbors
Sam Wiseman, Arturs Backurs, Karl Stratos
We propose to tackle data-to-text generation tasks by directly splicing together retrieved segments of text from "neighbor" source-target pairs. Unlike recent work that conditions…
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…