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20192022
most citedThe GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

52 citations · 57 across the 6 of their papers we have counts for

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cs.CL2022

Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks

Zae Myung Kim, Wanyu Du, Vipul Raheja +2

Iterative text revision improves text quality by fixing grammatical errors, rephrasing for better readability or contextual appropriateness, or reorganizing sentence structures thr…

cs.CL2022

GRS: Combining Generation and Revision in Unsupervised Sentence Simplification

Mohammad Dehghan, Dhruv Kumar, Lukasz Golab

We propose GRS: an unsupervised approach to sentence simplification that combines text generation and text revision. We start with an iterative framework in which an input sentence…

cs.CL202152 cited

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal +53

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of auto…

cs.CL2020

Optimizing Deeper Transformers on Small Datasets

Peng Xu, Dhruv Kumar, Wei Yang +6

It is a common belief that training deep transformers from scratch requires large datasets. Consequently, for small datasets, people usually use shallow and simple additional layer…

cs.CL20204 cited

Generation of lyrics lines conditioned on music audio clips

Olga Vechtomova, Gaurav Sahu, Dhruv Kumar

We present a system for generating novel lyrics lines conditioned on music audio. A bimodal neural network model learns to generate lines conditioned on any given short audio clip.…

cs.CL2020

Iterative Edit-Based Unsupervised Sentence Simplification

Dhruv Kumar, Lili Mou, Lukasz Golab +1

We present a novel iterative, edit-based approach to unsupervised sentence simplification. Our model is guided by a scoring function involving fluency, simplicity, and meaning pres…