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Found 9 papers, 5 papers with code
Quality-Aware Decoding for Neural Machine Translation
Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model (MAP decoding), approximated with beam search.
CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task
We present the joint contribution of IST and Unbabel to the WMT 2022 Shared Task on Quality Estimation (QE).
Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation
Many recent advances in natural language generation have been fueled by training large language models on internet-scale data.
Scaling up COMETKIWI: Unbabel-IST 2023 Submission for the Quality Estimation Shared Task
Our team participated on all tasks: sentence- and word-level quality prediction (task 1) and fine-grained error span detection (task 2).
An Empirical Study of Translation Hypothesis Ensembling with Large Language Models
Large language models (LLMs) are becoming a one-fits-many solution, but they sometimes hallucinate or produce unreliable output.
Steering Large Language Models for Machine Translation with Finetuning and In-Context Learning
Experiments on 10 language pairs show that our proposed approach recovers the original few-shot capabilities while keeping the added benefits of finetuning.
Tower: An Open Multilingual Large Language Model for Translation-Related Tasks
While general-purpose large language models (LLMs) demonstrate proficiency on multiple tasks within the domain of translation, approaches based on open LLMs are competitive only when specializing on a single task.
IST-Unbabel 2021 Submission for the Quality Estimation Shared Task
We present the joint contribution of IST and Unbabel to the WMT 2021 Shared Task on Quality Estimation.
