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MBR and QE Finetuning: Training-time Distillation of the Best and Most
  Expensive Decoding Methods

MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods

19 September 2023
M. Finkelstein
Subhajit Naskar
Mehdi Mirzazadeh
Apurva Shah
Markus Freitag
ArXivPDFHTML

Papers citing "MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods"

6 / 6 papers shown
Title
Better Instruction-Following Through Minimum Bayes Risk
Better Instruction-Following Through Minimum Bayes Risk
Ian Wu
Patrick Fernandes
Amanda Bertsch
Seungone Kim
Sina Pakazad
Graham Neubig
48
9
0
03 Oct 2024
Don't Throw Away Data: Better Sequence Knowledge Distillation
Don't Throw Away Data: Better Sequence Knowledge Distillation
Jun Wang
Eleftheria Briakou
Hamid Dadkhahi
Rishabh Agarwal
Colin Cherry
Trevor Cohn
53
5
0
15 Jul 2024
AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment
AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment
Yilong Lai
Jialong Wu
Congzhi Zhang
Haowen Sun
Deyu Zhou
52
2
0
02 Jul 2024
Chasing COMET: Leveraging Minimum Bayes Risk Decoding for Self-Improving
  Machine Translation
Chasing COMET: Leveraging Minimum Bayes Risk Decoding for Self-Improving Machine Translation
Kamil Guttmann
Miko Pokrywka
Adrian Charkiewicz
Artur Nowakowski
58
3
0
20 May 2024
Linear-time Minimum Bayes Risk Decoding with Reference Aggregation
Linear-time Minimum Bayes Risk Decoding with Reference Aggregation
Jannis Vamvas
Rico Sennrich
50
15
0
06 Feb 2024
Google's Neural Machine Translation System: Bridging the Gap between
  Human and Machine Translation
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Yonghui Wu
M. Schuster
Zhehuai Chen
Quoc V. Le
Mohammad Norouzi
...
Alex Rudnick
Oriol Vinyals
G. Corrado
Macduff Hughes
J. Dean
AIMat
718
6,750
0
26 Sep 2016
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