Sam Wiseman | DeepAI

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∙ 05/23/2023

BM25 Query Augmentation Learned End-to-End

Given BM25's enduring competitiveness as an information retrieval baseli...

0 Xiaoyin Chen, et al. ∙

∙ 05/03/2023

Approximating CKY with Transformers

We investigate the ability of transformer models to approximate the CKY ...

0 Ghazal Khalighinejad, et al. ∙

∙ 11/10/2022

CREATIVESUMM: Shared Task on Automatic Summarization for Creative Writing

This paper introduces the shared task of summarizing documents in severa...

0 Divyansh Agarwal, et al. ∙

∙ 09/20/2021

On Generalization in Coreference Resolution

While coreference resolution is defined independently of dataset domain,...

5 Shubham Toshniwal, et al. ∙

∙ 04/14/2021

SummScreen: A Dataset for Abstractive Screenplay Summarization

We introduce SummScreen, a summarization dataset comprised of pairs of T...

0 Mingda Chen, et al. ∙

∙ 02/26/2021

Learning Chess Blindfolded: Evaluating Language Models on State Tracking

Transformer language models have made tremendous strides in natural lang...

5 Shubham Toshniwal, et al. ∙

∙ 01/20/2021

Generating (Formulaic) Text by Splicing Together Nearest Neighbors

We propose to tackle conditional text generation tasks, especially those...

0 Sam Wiseman, et al. ∙

∙ 12/29/2020

Generating Wikipedia Article Sections from Diverse Data Sources

Datasets for data-to-text generation typically focus either on multi-dom...

0 Mingda Chen, et al. ∙

∙ 10/12/2020

Controllable Paraphrasing and Translation with a Syntactic Exemplar

Most prior work on exemplar-based syntactically controlled paraphrase ge...

0 Mingda Chen, et al. ∙

∙ 10/06/2020

Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks

Long document coreference resolution remains a challenging task due to t...

0 Shubham Toshniwal, et al. ∙

∙ 06/11/2020

Discrete Latent Variable Representations for Low-Resource Text Classification

While much work on deep latent variable models of text uses continuous l...

0 Shuning Jin, et al. ∙

∙ 05/02/2020

ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation

We propose to train a non-autoregressive machine translation model to mi...

0 Lifu Tu, et al. ∙

∙ 04/08/2020

Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information

We propose learning discrete structured representations from unlabeled d...

14 Karl Stratos, et al. ∙

∙ 06/14/2019

Amortized Bethe Free Energy Minimization for Learning MRFs

We propose to learn deep undirected graphical models (i.e., MRFs), with ...

0 Sam Wiseman, et al. ∙

∙ 06/10/2019

Label-Agnostic Sequence Labeling by Copying Nearest Neighbors

Retrieve-and-edit based approaches to structured prediction, where struc...

0 Sam Wiseman, et al. ∙

∙ 06/03/2019

Controllable Paraphrase Generation with a Syntactic Exemplar

Prior work on controllable text generation usually assumes that the cont...

0 Mingda Chen, et al. ∙

∙ 04/02/2019

A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations

We propose a generative model for a sentence that uses two latent variab...

0 Mingda Chen, et al. ∙

∙ 12/17/2018

A Tutorial on Deep Latent Variable Models of Natural Language

There has been much recent, exciting work on combining the complementary...

0 Yoon Kim, et al. ∙

∙ 10/05/2018

Entity Tracking Improves Cloze-style Reading Comprehension

Reading comprehension tasks test the ability of models to process long-t...

0 Luong Hoang, et al. ∙

∙ 08/30/2018

Learning Neural Templates for Text Generation

While neural, encoder-decoder models have had significant empirical succ...

0 Sam Wiseman, et al. ∙

∙ 02/07/2018

Semi-Amortized Variational Autoencoders

Amortized variational inference (AVI) replaces instance-specific local i...

0 Yoon Kim, et al. ∙

∙ 07/25/2017

Challenges in Data-to-Document Generation

Recent neural models have shown significant progress on the problem of g...

0 Sam Wiseman, et al. ∙

∙ 02/15/2017

Training Language Models Using Target-Propagation

While Truncated Back-Propagation through Time (BPTT) is the most popular...

0 Sam Wiseman, et al. ∙

∙ 06/09/2016

Sequence-to-Sequence Learning as Beam-Search Optimization

Sequence-to-Sequence (seq2seq) modeling has rapidly become an important ...

0 Sam Wiseman, et al. ∙

∙ 04/11/2016

Learning Global Features for Coreference Resolution

There is compelling evidence that coreference prediction would benefit f...

0 Sam Wiseman, et al. ∙

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