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A Call for Clarity in Reporting BLEU Scores
v1v2 (latest)

A Call for Clarity in Reporting BLEU Scores

Conference on Machine Translation (WMT), 2018
23 April 2018
Matt Post
ArXiv (abs)PDFHTML

Papers citing "A Call for Clarity in Reporting BLEU Scores"

50 / 1,823 papers shown
Title
CULL-MT: Compression Using Language and Layer pruning for Machine
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Towards Building Large Scale Datasets and State-of-the-Art Automatic Speech Translation Systems for 14 Indian Languages
Towards Building Large Scale Datasets and State-of-the-Art Automatic Speech Translation Systems for 14 Indian Languages
Sparsh Jain
Ashwin Sankar
Devilal Choudhary
Dhairya Suman
Nikhil Narasimhan
Mohammed Safi Ur Rahman Khan
Anoop Kunchukuttan
Mitesh M. Khapra
Mary Dabre
427
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Mitigating Metric Bias in Minimum Bayes Risk Decoding
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Geza Kovacs
Daniel Deutsch
Markus Freitag
247
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Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study
Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study
André Storhaug
Jingyue Li
ALM
298
8
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04 Nov 2024
MoCE: Adaptive Mixture of Contextualization Experts for Byte-based Neural Machine Translation
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Langlin Huang
Mengyu Bu
Yang Feng
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SPES: Spectrogram Perturbation for Explainable Speech-to-Text Generation
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Dennis Fucci
Marco Gaido
Beatrice Savoldi
Matteo Negri
Mauro Cettolo
L. Bentivogli
555
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Open LLMs are Necessary for Current Private Adaptations and Outperform
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Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed AlternativesNeural Information Processing Systems (NeurIPS), 2024
Vincent Hanke
Tom Blanchard
Franziska Boenisch
Iyiola Emmanuel Olatunji
Michael Backes
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Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large
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NeuZip: Memory-Efficient Training and Inference with Dynamic Compression
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Lili Mou
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Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned
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Fengchen Liu
Jordan Jung
Wei Feinstein
Jeff DAmbrogia
Gary Jung
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87
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GrammaMT: Improving Machine Translation with Grammar-Informed In-Context Learning
GrammaMT: Improving Machine Translation with Grammar-Informed In-Context LearningAnnual Meeting of the Association for Computational Linguistics (ACL), 2024
Rita Ramos
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Maartje ter Hoeve
Natalie Schluter
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How Good Are LLMs for Literary Translation, Really? Literary Translation Evaluation with Humans and LLMs
How Good Are LLMs for Literary Translation, Really? Literary Translation Evaluation with Humans and LLMsNorth American Chapter of the Association for Computational Linguistics (NAACL), 2024
Ran Zhang
Wei Zhao
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Subword Embedding from Bytes Gains Privacy without Sacrificing Accuracy
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Systematic Exploration of Dialogue Summarization Approaches for
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Generalized Probabilistic Attention Mechanism in Transformers
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signwriting-evaluation: Effective Sign Language Evaluation via
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Meta-DiffuB: A Contextualized Sequence-to-Sequence Text Diffusion Model
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ChakmaNMT: A Low-resource Machine Translation On Chakma Language
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