Search Results for author: Alexandra Birch

Found 38 papers, 17 papers with code

GoURMET – Machine Translation for Low-Resourced Languages

no code implementations EAMT 2022 Peggy van der Kreeft, Alexandra Birch, Sevi Sariisik, Felipe Sánchez-Martínez, Wilker Aziz

The GoURMET project, funded by the European Commission’s H2020 program (under grant agreement 825299), develops models for machine translation, in particular for low-resourced languages.

Machine Translation Translation

The University of Edinburgh’s English-Tamil and English-Inuktitut Submissions to the WMT20 News Translation Task

no code implementations WMT (EMNLP) 2020 Rachel Bawden, Alexandra Birch, Radina Dobreva, Arturo Oncevay, Antonio Valerio Miceli Barone, Philip Williams

We describe the University of Edinburgh’s submissions to the WMT20 news translation shared task for the low resource language pair English-Tamil and the mid-resource language pair English-Inuktitut.

Language Modelling Machine Translation +1

The Samsung and University of Edinburgh’s submission to IWSLT17

no code implementations IWSLT 2017 Pawel Przybysz, Marcin Chochowski, Rico Sennrich, Barry Haddow, Alexandra Birch

This paper describes the joint submission of Samsung Research and Development, Warsaw, Poland and the University of Edinburgh team to the IWSLT MT task for TED talks.

Domain Adaptation Translation

Samsung and University of Edinburgh’s System for the IWSLT 2018 Low Resource MT Task

no code implementations IWSLT (EMNLP) 2018 Philip Williams, Marcin Chochowski, Pawel Przybysz, Rico Sennrich, Barry Haddow, Alexandra Birch

This paper describes the joint submission to the IWSLT 2018 Low Resource MT task by Samsung R&D Institute, Poland, and the University of Edinburgh.

Exploring Diversity in Back Translation for Low-Resource Machine Translation

1 code implementation1 Jun 2022 Laurie Burchell, Alexandra Birch, Kenneth Heafield

We also find evidence that lexical diversity is more important than syntactic for back translation performance.

Machine Translation Translation

Non-Autoregressive Machine Translation: It's Not as Fast as it Seems

no code implementations4 May 2022 Jindřich Helcl, Barry Haddow, Alexandra Birch

In this paper, we point out flaws in the evaluation methodology present in the literature on NAR models and we provide a fair comparison between a state-of-the-art NAR model and the autoregressive submissions to the shared task.

Machine Translation Translation

Distributionally Robust Recurrent Decoders with Random Network Distillation

no code implementations RepL4NLP (ACL) 2022 Antonio Valerio Miceli-Barone, Alexandra Birch, Rico Sennrich

Neural machine learning models can successfully model language that is similar to their training distribution, but they are highly susceptible to degradation under distribution shift, which occurs in many practical applications when processing out-of-domain (OOD) text.

Language Modelling OOD Detection

Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking

1 code implementation EMNLP 2021 Nikita Moghe, Mark Steedman, Alexandra Birch

In this work, we enhance the transfer learning process by intermediate fine-tuning of pretrained multilingual models, where the multilingual models are fine-tuned with different but related data and/or tasks.

Cross-Lingual Transfer Dialogue State Tracking +2

Exploring Unsupervised Pretraining Objectives for Machine Translation

no code implementations Findings (ACL) 2021 Christos Baziotis, Ivan Titov, Alexandra Birch, Barry Haddow

Unsupervised cross-lingual pretraining has achieved strong results in neural machine translation (NMT), by drastically reducing the need for large parallel data.

Language Modelling Machine Translation +2

Few-shot learning through contextual data augmentation

1 code implementation EACL 2021 Farid Arthaud, Rachel Bawden, Alexandra Birch

Machine translation (MT) models used in industries with constantly changing topics, such as translation or news agencies, need to adapt to new data to maintain their performance over time.

Data Augmentation Few-Shot Learning +3

Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations

1 code implementation EMNLP 2020 Arturo Oncevay, Barry Haddow, Alexandra Birch

Sparse language vectors from linguistic typology databases and learned embeddings from tasks like multilingual machine translation have been investigated in isolation, without analysing how they could benefit from each other's language characterisation.

Machine Translation Translation

Towards Making the Most of Context in Neural Machine Translation

1 code implementation19 Feb 2020 Zaixiang Zheng, Xiang Yue, Shu-Jian Huang, Jia-Jun Chen, Alexandra Birch

Document-level machine translation manages to outperform sentence level models by a small margin, but have failed to be widely adopted.

Document Level Machine Translation Machine Translation +1

A Latent Morphology Model for Open-Vocabulary Neural Machine Translation

1 code implementation ICLR 2020 Duygu Ataman, Wilker Aziz, Alexandra Birch

Translation into morphologically-rich languages challenges neural machine translation (NMT) models with extremely sparse vocabularies where atomic treatment of surface forms is unrealistic.

Machine Translation Morphological Inflection +1

Findings of the Third Workshop on Neural Generation and Translation

no code implementations WS 2019 Hiroaki Hayashi, Yusuke Oda, Alexandra Birch, Ioannis Konstas, Andrew Finch, Minh-Thang Luong, Graham Neubig, Katsuhito Sudoh

This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019).

Machine Translation Natural Language Processing +1

On the Importance of Word Boundaries in Character-level Neural Machine Translation

1 code implementation WS 2019 Duygu Ataman, Orhan Firat, Mattia A. Di Gangi, Marcello Federico, Alexandra Birch

Neural Machine Translation (NMT) models generally perform translation using a fixed-size lexical vocabulary, which is an important bottleneck on their generalization capability and overall translation quality.

Machine Translation Translation

Findings of the Second Workshop on Neural Machine Translation and Generation

no code implementations WS 2018 Alexandra Birch, Andrew Finch, Minh-Thang Luong, Graham Neubig, Yusuke Oda

This document describes the findings of the Second Workshop on Neural Machine Translation and Generation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2018).

Data Augmentation Domain Adaptation +2

Marian: Fast Neural Machine Translation in C++

2 code implementations ACL 2018 Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, André F. T. Martins, Alexandra Birch

We present Marian, an efficient and self-contained Neural Machine Translation framework with an integrated automatic differentiation engine based on dynamic computation graphs.

Machine Translation Translation

Evaluating Discourse Phenomena in Neural Machine Translation

no code implementations NAACL 2018 Rachel Bawden, Rico Sennrich, Alexandra Birch, Barry Haddow

Despite gains using BLEU, multi-encoder models give limited improvement in the handling of discourse phenomena: 50% accuracy on our coreference test set and 53. 5% for coherence/cohesion (compared to a non-contextual baseline of 50%).

Machine Translation Translation

Predicting Target Language CCG Supertags Improves Neural Machine Translation

no code implementations WS 2017 Maria Nadejde, Siva Reddy, Rico Sennrich, Tomasz Dwojak, Marcin Junczys-Dowmunt, Philipp Koehn, Alexandra Birch

Our results on WMT data show that explicitly modeling target-syntax improves machine translation quality for German->English, a high-resource pair, and for Romanian->English, a low-resource pair and also several syntactic phenomena including prepositional phrase attachment.

Machine Translation Prepositional Phrase Attachment +1

HUME: Human UCCA-Based Evaluation of Machine Translation

1 code implementation EMNLP 2016 Alexandra Birch, Omri Abend, Ondrej Bojar, Barry Haddow

Human evaluation of machine translation normally uses sentence-level measures such as relative ranking or adequacy scales.

Machine Translation Translation

Edinburgh Neural Machine Translation Systems for WMT 16

1 code implementation WS 2016 Rico Sennrich, Barry Haddow, Alexandra Birch

We participated in the WMT 2016 shared news translation task by building neural translation systems for four language pairs, each trained in both directions: English<->Czech, English<->German, English<->Romanian and English<->Russian.

Machine Translation Translation

Neural Machine Translation of Rare Words with Subword Units

24 code implementations ACL 2016 Rico Sennrich, Barry Haddow, Alexandra Birch

Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem.

Translation

Cannot find the paper you are looking for? You can Submit a new open access paper.