105 citations · 349 across the 67 of their papers we have counts for
10 papers · 1 filter
Findings of the IWSLT 2024 Evaluation Campaign
Ibrahim Said Ahmad, Antonios Anastasopoulos, Ondřej Bojar +42
This paper reports on the shared tasks organized by the 21st IWSLT Conference. The shared tasks address 7 scientific challenges in spoken language translation: simultaneous and off…
SpeechQE: Estimating the Quality of Direct Speech Translation
HyoJung Han, Kevin Duh, Marine Carpuat
Recent advances in automatic quality estimation for machine translation have exclusively focused on written language, leaving the speech modality underexplored. In this work, we fo…
Plausibly Problematic Questions in Multiple-Choice Benchmarks for Commonsense Reasoning
Shramay Palta, Nishant Balepur, Peter Rankel +3
Questions involving commonsense reasoning about everyday situations often admit many or answers. In contrast, multiple-choice question (MCQ…
Adapters for Altering LLM Vocabularies: What Languages Benefit the Most?
HyoJung Han, Akiko Eriguchi, Haoran Xu +3
Vocabulary adaptation, which integrates new vocabulary into pre-trained language models, enables expansion to new languages and mitigates token over-fragmentation. However, existin…
The Prompt Report: A Systematic Survey of Prompt Engineering Techniques
Sander Schulhoff, Michael Ilie, Nishant Balepur +28
Generative Artificial Intelligence (GenAI) systems are increasingly being deployed across diverse industries and research domains. Developers and end-users interact with these syst…
How Multilingual Are Large Language Models Fine-Tuned for Translation?
Aquia Richburg, Marine Carpuat
A new paradigm for machine translation has recently emerged: fine-tuning large language models (LLM) on parallel text has been shown to outperform dedicated translation systems tra…
Keep It Private: Unsupervised Privatization of Online Text
Calvin Bao, Marine Carpuat
Authorship obfuscation techniques hold the promise of helping people protect their privacy in online communications by automatically rewriting text to hide the identity of the orig…
How often are errors in natural language reasoning due to paraphrastic variability?
Neha Srikanth, Marine Carpuat, Rachel Rudinger
Large language models have been shown to behave inconsistently in response to meaning-preserving paraphrastic inputs. At the same time, researchers evaluate the knowledge and reaso…
Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations
Dayeon Ki, Marine Carpuat
Machine Translation (MT) remains one of the last NLP tasks where large language models (LLMs) have not yet replaced dedicated supervised systems. This work exploits the complementa…
XLAVS-R: Cross-Lingual Audio-Visual Speech Representation Learning for Noise-Robust Speech Perception
HyoJung Han, Mohamed Anwar, Juan Pino +4
Speech recognition and translation systems perform poorly on noisy inputs, which are frequent in realistic environments. Augmenting these systems with visual signals has the potent…