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20182025
most citedMoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance

20 citations · 44 across the 5 of their papers we have counts for

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8 papers · 1 filter

cs.CL2025

zip2zip: Inference-Time Adaptive Tokenization via Online Compression

Saibo Geng, Nathan Ranchin, Yunzhen yao +4

Tokenization efficiency plays a critical role in the performance and cost of large language models (LLMs), yet most models rely on static tokenizers optimized on general-purpose co…

cs.CL202114 cited

Better than Average: Paired Evaluation of NLP Systems

Maxime Peyrard, Wei Zhao, Steffen Eger +1

Evaluation in NLP is usually done by comparing the scores of competing systems independently averaged over a common set of test instances. In this work, we question the use of aver…

cs.CL2021

Laughing Heads: Can Transformers Detect What Makes a Sentence Funny?

Maxime Peyrard, Beatriz Borges, Kristina Gligorić +1

The automatic detection of humor poses a grand challenge for natural language processing. Transformer-based systems have recently achieved remarkable results on this task, but they…

cs.CL2020

KLearn: Background Knowledge Inference from Summarization Data

Maxime Peyrard, Robert West

The goal of text summarization is to compress documents to the relevant information while excluding background information already known to the receiver. So far, summarization rese…

cs.CL20204 cited

On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation

Wei Zhao, Goran Glavaš, Maxime Peyrard +3

Evaluation of cross-lingual encoders is usually performed either via zero-shot cross-lingual transfer in supervised downstream tasks or via unsupervised cross-lingual textual simil…

cs.CL201920 cited

MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance

Wei Zhao, Maxime Peyrard, Fei Liu +3

A robust evaluation metric has a profound impact on the development of text generation systems. A desirable metric compares system output against references based on their semantic…