Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation

Autor: Kanojia, D., Fomicheva, M., Tharindu Ranasinghe, Blain, F., Orăsan, C., Specia, L.

Publikováno v: Scopus-Elsevier

Current Machine Translation (MT) systems achieve very good results on a growing variety of language pairs and datasets. However, they are known to produce fluent translation outputs that can contain important meaning errors, thus undermining their re

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::292362d8128906ae11a1e3bd77254774
http://arxiv.org/abs/2109.10859

Is Information Density Uniform in Task-Oriented Dialogues?

Autor: Giulianelli, M., Sinclair, A., Fernández, R., Moens, M.-C., Huang, X., Specia, L., Yih, S.W.

Publikováno v: 2021 Conference on Empirical Methods in Natural Language Processing: EMNLP 2021 : proceedings of the conference : November 7-11, 2021
2021 Conference on Empirical Methods in Natural Language Processing
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

The Uniform Information Density principle states that speakers plan their utterances to reduce fluctuations in the density of the information transmitted. In this paper, we test whether, and within which contextual units this principle holds in task-

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cc20bef680533bd56bda19ea8743af53
https://doi.org/10.18653/v1/2021.emnlp-main.652

Watch and learn: mapping language and noisy real-world videos with self-supervision

Autor: Zhong, Y, Xie, L, Wang, S, Specia, L, Miao, Y

In this paper, we teach machines to understand visuals and natural language by learning the mapping between sentences and noisy video snippets without explicit annotations. Firstly, we define a self-supervised learning framework that captures the cro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______1032::988739c60a3d94efa942b566ac06ac9e
http://hdl.handle.net/10044/1/84555

Unsupervised quality estimation for neural machine translation

Autor: Fomicheva, M, Sun, S, Yankovskaya, L, Blain, F, Guzmán, F, Fishel, M, Aletras, N, Chaudhary, V, Specia, L

Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time. Existing approaches require large amounts of exper

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______1032::d52b125b9b77a0186eb15815faaa8d53
http://hdl.handle.net/10044/1/79483

Multimodal machine translation through visuals and speech

Autor: Sulubacak, U, Caglayan, O, Grönroos, S-A, Rouhe, A, Elliott, D, Specia, L, Tiedemann, J

Multimodal machine translation involves drawing information from more than one modality, based on the assumption that the additional modalities will contain useful alternative views of the input data. The most prominent tasks in this area are spoken

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______1032::27c35620445ff32d0916a47be59423ef
http://hdl.handle.net/10044/1/79496