We present the joint contribution of IST and Unbabel to the WMT 2021 Shared Task on Quality Estimation.
Experiments on 10 language pairs show that our proposed approach recovers the original few-shot capabilities while keeping the added benefits of finetuning.
Large language models (LLMs) are becoming a one-fits-many solution, but they sometimes hallucinate or produce unreliable output.
Our team participated on all tasks: sentence- and word-level quality prediction (task 1) and fine-grained error span detection (task 2).
no code implementations • 1 May 2023 • Patrick Fernandes, Aman Madaan, Emmy Liu, António Farinhas, Pedro Henrique Martins, Amanda Bertsch, José G. C. de Souza, Shuyan Zhou, Tongshuang Wu, Graham Neubig, André F. T. Martins
Many recent advances in natural language generation have been fueled by training large language models on internet-scale data.
1 code implementation • 13 Sep 2022 • Ricardo Rei, Marcos Treviso, Nuno M. Guerreiro, Chrysoula Zerva, Ana C. Farinha, Christine Maroti, José G. C. de Souza, Taisiya Glushkova, Duarte M. Alves, Alon Lavie, Luisa Coheur, André F. T. Martins
We present the joint contribution of IST and Unbabel to the WMT 2022 Shared Task on Quality Estimation (QE).
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.