ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2203.01927
  4. Cited By
As Little as Possible, as Much as Necessary: Detecting Over- and
  Undertranslations with Contrastive Conditioning

As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning

3 March 2022
Jannis Vamvas
Rico Sennrich
ArXivPDFHTML

Papers citing "As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning"

12 / 12 papers shown
Title
Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine Translation
Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine Translation
Chiara Manna
Afra Alishahi
Frédéric Blain
Eva Vanmassenhove
24
0
0
13 May 2025
Multi-property Steering of Large Language Models with Dynamic Activation
  Composition
Multi-property Steering of Large Language Models with Dynamic Activation Composition
Daniel Scalena
Gabriele Sarti
Malvina Nissim
KELM
LLMSV
AI4CE
27
13
0
25 Jun 2024
MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of
  Metaphorical Language
MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language
Shun Wang
Ge Zhang
Han Wu
Tyler Loakman
Wenhao Huang
Chenghua Lin
40
2
0
19 Jun 2024
Word Alignment as Preference for Machine Translation
Word Alignment as Preference for Machine Translation
Qiyu Wu
Masaaki Nagata
Zhongtao Miao
Yoshimasa Tsuruoka
52
5
0
15 May 2024
Quantifying the Plausibility of Context Reliance in Neural Machine
  Translation
Quantifying the Plausibility of Context Reliance in Neural Machine Translation
Gabriele Sarti
Grzegorz Chrupala
Malvina Nissim
Arianna Bisazza
29
5
0
02 Oct 2023
Mitigating Hallucinations and Off-target Machine Translation with
  Source-Contrastive and Language-Contrastive Decoding
Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decoding
Rico Sennrich
Jannis Vamvas
Alireza Mohammadshahi
HILM
30
38
0
13 Sep 2023
With a Little Help from the Authors: Reproducing Human Evaluation of an
  MT Error Detector
With a Little Help from the Authors: Reproducing Human Evaluation of an MT Error Detector
Ondvrej Plátek
Mateusz Lango
Ondrej Dusek
30
3
0
12 Aug 2023
HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination
  and Omission Detection in Machine Translation
HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translation
David Dale
Elena Voita
Janice Lam
Prangthip Hansanti
C. Ropers
Elahe Kalbassi
Cynthia Gao
Loïc Barrault
Marta R. Costa-jussá
HILM
32
27
0
19 May 2023
Towards Fine-Grained Information: Identifying the Type and Location of
  Translation Errors
Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors
Keqin Bao
Boyi Deng
Dayiheng Liu
Baosong Yang
Wenqiang Lei
Xiangnan He
Derek F.Wong
Jun Xie
29
4
0
17 Feb 2023
ACES: Translation Accuracy Challenge Sets for Evaluating Machine
  Translation Metrics
ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics
Chantal Amrhein
Nikita Moghe
Liane Guillou
ELM
31
22
0
27 Oct 2022
Stanza: A Python Natural Language Processing Toolkit for Many Human
  Languages
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
Peng Qi
Yuhao Zhang
Yuhui Zhang
Jason Bolton
Christopher D. Manning
AI4TS
207
1,654
0
16 Mar 2020
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
Z. Chen
Quoc V. Le
Mohammad Norouzi
...
Alex Rudnick
Oriol Vinyals
G. Corrado
Macduff Hughes
J. Dean
AIMat
716
6,743
0
26 Sep 2016
1