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natural-language-processing-10

Text Infilling

3260 papers • 126 benchmarks • 313 datasets

Text Infilling is the task of predicting missing spans of text which are consistent with the preceding and subsequent text. Text Infilling is a generalization of the cloze task—cloze historically refers to infilling individual words. Source: Enabling Language Models to Fill in the Blanks

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Most implemented papers

Enabling Language Models to Fill in the Blanks

Percy Liang, Chris Donahue, Mina Lee•Sun May 10 2020

It is shown that humans have difficulty identifying sentences infilled by the approach, which can enable LMs to infill entire sentences effectively on three different domains: short stories, scientific abstracts, and lyrics.

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LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation

Minlie Huang, Jian Guan, Zhuoer Feng, Xiaoxi Mao, Changjie Fan, Yamei Chen, Ru He•Sun Aug 29 2021

A story-centric benchmark named LOT is proposed for evaluating Chinese long text modeling, which aggregates two understanding tasks and two generation tasks and shows that LongLM outperforms similar-sized pretraining models substantially on both the understanding and generation tasks in LOT.

35 0

Nutribullets Hybrid: Multi-document Health Summarization

R. Barzilay, Darsh J. Shah, L. Yu, Tao Lei•Wed Apr 07 2021

This work introduces a hybrid generation approach inspired by traditional concept-to-text systems for generating comparative summaries that leads to more faithful, relevant and aggregation-sensitive summarization -- while being equally fluent.

4 0

Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange

Aaron W. Li, Steven Y. Feng, J. Hoey•Thu Aug 29 2019

A novel method for measurably adjusting the semantics of text while preserving its sentiment and fluency, a task the authors call semantic text exchange is presented and a pipeline called SMERTI is introduced that combines entity replacement, similarity masking, and text infilling.

30 0

Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning

Antoine Bosselut, Yejin Choi, Peter West, Jena D. Hwang, Ronan Le Bras, Chandra Bhagavatula, Lianhui Qin, Vered Shwartz•Sun Oct 11 2020

This paper proposes DeLorean, a new unsupervised decoding algorithm that can flexibly incorporate both the past and future contexts using only off-the-shelf, left-to-right language models and no supervision.

90 0

TIGS: An Inference Algorithm for Text Infilling with Gradient Search

Pengfei Liu, Jie Fu, Dayiheng Liu, Jiancheng Lv•Sat May 25 2019

An iterative inference algorithm based on gradient search is proposed, which could be the first inference algorithm that can be broadly applied to any neural sequence generative models for text infilling tasks.

28 0

Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting

Furu Wei, Tao Ge, Wangchunshu Zhou, Ke Xu•Fri Jan 01 2021

This paper conducts extensive experiments by using SSR to improve the typical Seq2Seq pre-trained model T5 in a continual pre-training setting and show substantial improvements over T5 on various natural language generation tasks.

16 0

Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet

Xingwei He, V. Li•Mon May 17 2021

This work used a classifier to instruct the MCMC-based models where and how to refine the candidate sentences, and proposed a two-step approach, “Predict and Revise”, for constrained sentence generation.

25 0

Conformal prediction for text infilling and part-of-speech prediction

N. Dey, Jing Ding, Jack G. Ferrell, Carolina Kapper, Maxwell Lovig, Emiliano Planchon, Jonathan P. Williams•Wed Nov 03 2021

This paper proposes inductive conformal prediction (ICP) algorithms for the tasks of text infilling and part-of-speech (POS) prediction for natural language data and designs new ICP-enhanced algorithms for POS tagging based on BERT and BiLSTM models.

22 0

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Text Infilling | State-of-the-Art