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20192023
most citedBERTScore: Evaluating Text Generation with BERT

2.4k citations · 6.2k across the 17 of their papers we have counts for

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5 papers · 1 filter

cs.CL2023

The Effect of Masking Strategies on Knowledge Retention by Language Models

Jonas Wallat, Tianyi Zhang, Avishek Anand

Language models retain a significant amount of world knowledge from their pre-training stage. This allows knowledgeable models to be applied to knowledge-intensive tasks prevalent…

cs.CL2022

TempLM: Distilling Language Models into Template-Based Generators

Tianyi Zhang, Mina Lee, Lisa Li +2

While pretrained language models (PLMs) have greatly improved text generation, they have also been known to produce unfaithful or inappropriate content. In contrast, classic templa…

cs.CL2021★ 3 cited

On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies

Tianyi Zhang, Tatsunori Hashimoto

We study how masking and predicting tokens in an unsupervised fashion can give rise to linguistic structures and downstream performance gains. Recent theories have suggested that p…

cs.CL2020★ 213 cited

Revisiting Few-sample BERT Fine-tuning

Tianyi Zhang, Felix Wu, Arzoo Katiyar +2

This paper is a study of fine-tuning of BERT contextual representations, with focus on commonly observed instabilities in few-sample scenarios. We identify several factors that cau…

cs.CL2019★ 2.4k cited

BERTScore: Evaluating Text Generation with BERT

Tianyi Zhang, Varsha Kishore, Felix Wu +2

We propose BERTScore, an automatic evaluation metric for text generation. Analogously to common metrics, BERTScore computes a similarity score for each token in the candidate sente…