Alexandra Birch


2022

pdf bib
GoURMET – Machine Translation for Low-Resourced Languages
Peggy van der Kreeft | Alexandra Birch | Sevi Sariisik | Felipe Sánchez-Martínez | Wilker Aziz
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

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. Data, models and software releases as well as the GoURMET Translate Tool are made available as open source.

pdf bib
Quantifying Synthesis and Fusion and their Impact on Machine Translation
Arturo Oncevay | Duygu Ataman | Niels Van Berkel | Barry Haddow | Alexandra Birch | Johannes Bjerva
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Theoretical work in morphological typology offers the possibility of measuring morphological diversity on a continuous scale. However, literature in Natural Language Processing (NLP) typically labels a whole language with a strict type of morphology, e.g. fusional or agglutinative. In this work, we propose to reduce the rigidity of such claims, by quantifying morphological typology at the word and segment level. We consider Payne (2017)’s approach to classify morphology using two indices: synthesis (e.g. analytic to polysynthetic) and fusion (agglutinative to fusional). For computing synthesis, we test unsupervised and supervised morphological segmentation methods for English, German and Turkish, whereas for fusion, we propose a semi-automatic method using Spanish as a case study. Then, we analyse the relationship between machine translation quality and the degree of synthesis and fusion at word (nouns and verbs for English-Turkish, and verbs in English-Spanish) and segment level (previous language pairs plus English-German in both directions). We complement the word-level analysis with human evaluation, and overall, we observe a consistent impact of both indexes on machine translation quality.

pdf bib
Non-Autoregressive Machine Translation: It’s Not as Fast as it Seems
Jindřich Helcl | Barry Haddow | Alexandra Birch
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Efficient machine translation models are commercially important as they can increase inference speeds, and reduce costs and carbon emissions. Recently, there has been much interest in non-autoregressive (NAR) models, which promise faster translation. In parallel to the research on NAR models, there have been successful attempts to create optimized autoregressive models as part of the WMT shared task on efficient translation. 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. We make the case for consistent evaluation of NAR models, and also for the importance of comparing NAR models with other widely used methods for improving efficiency. We run experiments with a connectionist-temporal-classification-based (CTC) NAR model implemented in C++ and compare it with AR models using wall clock times. Our results show that, although NAR models are faster on GPUs, with small batch sizes, they are almost always slower under more realistic usage conditions. We call for more realistic and extensive evaluation of NAR models in future work.

pdf bib
Horses to Zebras: Ontology-Guided Data Augmentation and Synthesis for ICD-9 Coding
Matúš Falis | Hang Dong | Alexandra Birch | Beatrice Alex
Proceedings of the 21st Workshop on Biomedical Language Processing

Medical document coding is the process of assigning labels from a structured label space (ontology – e.g., ICD-9) to medical documents. This process is laborious, costly, and error-prone. In recent years, efforts have been made to automate this process with neural models. The label spaces are large (in the order of thousands of labels) and follow a big-head long-tail label distribution, giving rise to few-shot and zero-shot scenarios. Previous efforts tried to address these scenarios within the model, leading to improvements on rare labels, but worse results on frequent ones. We propose data augmentation and synthesis techniques in order to address these scenarios. We further introduce an analysis technique for this setting inspired by confusion matrices. This analysis technique points to the positive impact of data augmentation and synthesis, but also highlights more general issues of confusion within families of codes, and underprediction.

pdf bib
Exploring diversity in back translation for low-resource machine translation
Laurie Burchell | Alexandra Birch | Kenneth Heafield
Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing

t

pdf bib
Distributionally Robust Recurrent Decoders with Random Network Distillation
Antonio Valerio Miceli Barone | Alexandra Birch | Rico Sennrich
Proceedings of the 7th Workshop on Representation Learning for NLP

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. This has been attributed to “shortcut learning”":" relying on weak correlations over arbitrary large contexts. We propose a method based on OOD detection with Random Network Distillation to allow an autoregressive language model to automatically disregard OOD context during inference, smoothly transitioning towards a less expressive but more robust model as the data becomes more OOD, while retaining its full context capability when operating in-distribution. We apply our method to a GRU architecture, demonstrating improvements on multiple language modeling (LM) datasets.

2021

pdf bib
The University of Edinburgh’s English-German and English-Hausa Submissions to the WMT21 News Translation Task
Pinzhen Chen | Jindřich Helcl | Ulrich Germann | Laurie Burchell | Nikolay Bogoychev | Antonio Valerio Miceli Barone | Jonas Waldendorf | Alexandra Birch | Kenneth Heafield
Proceedings of the Sixth Conference on Machine Translation

This paper presents the University of Edinburgh’s constrained submissions of English-German and English-Hausa systems to the WMT 2021 shared task on news translation. We build En-De systems in three stages: corpus filtering, back-translation, and fine-tuning. For En-Ha we use an iterative back-translation approach on top of pre-trained En-De models and investigate vocabulary embedding mapping.

pdf bib
Proceedings of the 1st Workshop on Multilingual Representation Learning
Duygu Ataman | Alexandra Birch | Alexis Conneau | Orhan Firat | Sebastian Ruder | Gozde Gul Sahin
Proceedings of the 1st Workshop on Multilingual Representation Learning

pdf bib
Exploring Unsupervised Pretraining Objectives for Machine Translation
Christos Baziotis | Ivan Titov | Alexandra Birch | Barry Haddow
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

pdf bib
Surprise Language Challenge: Developing a Neural Machine Translation System between Pashto and English in Two Months
Alexandra Birch | Barry Haddow | Antonio Valerio Miceli Barone | Jindrich Helcl | Jonas Waldendorf | Felipe Sánchez Martínez | Mikel Forcada | Víctor Sánchez Cartagena | Juan Antonio Pérez-Ortiz | Miquel Esplà-Gomis | Wilker Aziz | Lina Murady | Sevi Sariisik | Peggy van der Kreeft | Kay Macquarrie
Proceedings of Machine Translation Summit XVIII: Research Track

In the media industry and the focus of global reporting can shift overnight. There is a compelling need to be able to develop new machine translation systems in a short period of time and in order to more efficiently cover quickly developing stories. As part of the EU project GoURMET and which focusses on low-resource machine translation and our media partners selected a surprise language for which a machine translation system had to be built and evaluated in two months(February and March 2021). The language selected was Pashto and an Indo-Iranian language spoken in Afghanistan and Pakistan and India. In this period we completed the full pipeline of development of a neural machine translation system: data crawling and cleaning and aligning and creating test sets and developing and testing models and and delivering them to the user partners. In this paperwe describe rapid data creation and experiments with transfer learning and pretraining for this low-resource language pair. We find that starting from an existing large model pre-trained on 50languages leads to far better BLEU scores than pretraining on one high-resource language pair with a smaller model. We also present human evaluation of our systems and which indicates that the resulting systems perform better than a freely available commercial system when translating from English into Pashto direction and and similarly when translating from Pashto into English.

pdf bib
CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification
Matúš Falis | Hang Dong | Alexandra Birch | Beatrice Alex
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Large-Scale Multi-Label Text Classification (LMTC) includes tasks with hierarchical label spaces, such as automatic assignment of ICD-9 codes to discharge summaries. Performance of models in prior art is evaluated with standard precision, recall, and F1 measures without regard for the rich hierarchical structure. In this work we argue for hierarchical evaluation of the predictions of neural LMTC models. With the example of the ICD-9 ontology we describe a structural issue in the representation of the structured label space in prior art, and propose an alternative representation based on the depth of the ontology. We propose a set of metrics for hierarchical evaluation using the depth-based representation. We compare the evaluation scores from the proposed metrics with previously used metrics on prior art LMTC models for ICD-9 coding in MIMIC-III. We also propose further avenues of research involving the proposed ontological representation.

pdf bib
Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking
Nikita Moghe | Mark Steedman | Alexandra Birch
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Recent progress in task-oriented neural dialogue systems is largely focused on a handful of languages, as annotation of training data is tedious and expensive. Machine translation has been used to make systems multilingual, but this can introduce a pipeline of errors. Another promising solution is using cross-lingual transfer learning through pretrained multilingual models. Existing methods train multilingual models with additional code-mixed task data or refine the cross-lingual representations through parallel ontologies. 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. Specifically, we use parallel and conversational movie subtitles datasets to design cross-lingual intermediate tasks suitable for downstream dialogue tasks. We use only 200K lines of parallel data for intermediate fine-tuning which is already available for 1782 language pairs. We test our approach on the cross-lingual dialogue state tracking task for the parallel MultiWoZ (English -> Chinese, Chinese -> English) and Multilingual WoZ (English -> German, English -> Italian) datasets. We achieve impressive improvements (> 20% on joint goal accuracy) on the parallel MultiWoZ dataset and the Multilingual WoZ dataset over the vanilla baseline with only 10% of the target language task data and zero-shot setup respectively.

pdf bib
Few-shot learning through contextual data augmentation
Farid Arthaud | Rachel Bawden | Alexandra Birch
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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. Our aim is to teach a pre-trained MT model to translate previously unseen words accurately, based on very few examples. We propose (i) an experimental setup allowing us to simulate novel vocabulary appearing in human-submitted translations, and (ii) corresponding evaluation metrics to compare our approaches. We extend a data augmentation approach using a pretrained language model to create training examples with similar contexts for novel words. We compare different fine-tuning and data augmentation approaches and show that adaptation on the scale of one to five examples is possible. Combining data augmentation with randomly selected training sentences leads to the highest BLEU score and accuracy improvements. Impressively, with only 1 to 5 examples, our model reports better accuracy scores than a reference system trained with on average 313 parallel examples.

2020

pdf bib
Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations
Arturo Oncevay | Barry Haddow | Alexandra Birch
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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. We propose to fuse both views using singular vector canonical correlation analysis and study what kind of information is induced from each source. By inferring typological features and language phylogenies, we observe that our representations embed typology and strengthen correlations with language relationships. We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy in tasks that require information about language similarities, such as language clustering and ranking candidates for multilingual transfer. With our method, we can easily project and assess new languages without expensive retraining of massive multilingual or ranking models, which are major disadvantages of related approaches.

pdf bib
Language Model Prior for Low-Resource Neural Machine Translation
Christos Baziotis | Barry Haddow | Alexandra Birch
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The scarcity of large parallel corpora is an important obstacle for neural machine translation. A common solution is to exploit the knowledge of language models (LM) trained on abundant monolingual data. In this work, we propose a novel approach to incorporate a LM as prior in a neural translation model (TM). Specifically, we add a regularization term, which pushes the output distributions of the TM to be probable under the LM prior, while avoiding wrong predictions when the TM “disagrees” with the LM. This objective relates to knowledge distillation, where the LM can be viewed as teaching the TM about the target language. The proposed approach does not compromise decoding speed, because the LM is used only at training time, unlike previous work that requires it during inference. We present an analysis of the effects that different methods have on the distributions of the TM. Results on two low-resource machine translation datasets show clear improvements even with limited monolingual data.

pdf bib
Proceedings of the Fourth Workshop on Neural Generation and Translation
Alexandra Birch | Andrew Finch | Hiroaki Hayashi | Kenneth Heafield | Marcin Junczys-Dowmunt | Ioannis Konstas | Xian Li | Graham Neubig | Yusuke Oda
Proceedings of the Fourth Workshop on Neural Generation and Translation

pdf bib
Findings of the Fourth Workshop on Neural Generation and Translation
Kenneth Heafield | Hiroaki Hayashi | Yusuke Oda | Ioannis Konstas | Andrew Finch | Graham Neubig | Xian Li | Alexandra Birch
Proceedings of the Fourth Workshop on Neural Generation and Translation

We describe the finding of the Fourth Workshop on Neural Generation and Translation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2020). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the three shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document-level generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language and 3) STAPLE task: creation of as many possible translations of a given input text. This last shared task was organised by Duolingo.

pdf bib
Multiword Expression aware Neural Machine Translation
Andrea Zaninello | Alexandra Birch
Proceedings of the 12th Language Resources and Evaluation Conference

Multiword Expressions (MWEs) are a frequently occurring phenomenon found in all natural languages that is of great importance to linguistic theory, natural language processing applications, and machine translation systems. Neural Machine Translation (NMT) architectures do not handle these expressions well and previous studies have rarely addressed MWEs in this framework. In this work, we show that annotation and data augmentation, using external linguistic resources, can improve both translation of MWEs that occur in the source, and the generation of MWEs on the target, and increase performance by up to 5.09 BLEU points on MWE test sets. We also devise a MWE score to specifically assess the quality of MWE translation which agrees with human evaluation. We make available the MWE score implementation – along with MWE-annotated training sets and corpus-based lists of MWEs – for reproduction and extension.

pdf bib
The University of Edinburgh’s English-Tamil and English-Inuktitut Submissions to the WMT20 News Translation Task
Rachel Bawden | Alexandra Birch | Radina Dobreva | Arturo Oncevay | Antonio Valerio Miceli Barone | Philip Williams
Proceedings of the Fifth Conference on Machine Translation

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. We use the neural machine translation transformer architecture for all submissions and explore a variety of techniques to improve translation quality to compensate for the lack of parallel training data. For the very low-resource English-Tamil, this involves exploring pretraining, using both language model objectives and translation using an unrelated high-resource language pair (German-English), and iterative backtranslation. For English-Inuktitut, we explore the use of multilingual systems, which, despite not being part of the primary submission, would have achieved the best results on the test set.

pdf bib
Architecture of a Scalable, Secure and Resilient Translation Platform for Multilingual News Media
Susie Coleman | Andrew Secker | Rachel Bawden | Barry Haddow | Alexandra Birch
Proceedings of the 1st International Workshop on Language Technology Platforms

This paper presents an example architecture for a scalable, secure and resilient Machine Translation (MT) platform, using components available via Amazon Web Services (AWS). It is increasingly common for a single news organisation to publish and monitor news sources in multiple languages. A growth in news sources makes this increasingly challenging and time-consuming but MT can help automate some aspects of this process. Building a translation service provides a single integration point for news room tools that use translation technology allowing MT models to be integrated into a system once, rather than each time the translation technology is needed. By using a range of services provided by AWS, it is possible to architect a platform where multiple pre-existing technologies are combined to build a solution, as opposed to developing software from scratch for deployment on a single virtual machine. This increases the speed at which a platform can be developed and allows the use of well-maintained services. However, a single service also provides challenges. It is key to consider how the platform will scale when handling many users and how to ensure the platform is resilient.

2019

pdf bib
Proceedings of the 3rd Workshop on Neural Generation and Translation
Alexandra Birch | Andrew Finch | Hiroaki Hayashi | Ioannis Konstas | Thang Luong | Graham Neubig | Yusuke Oda | Katsuhito Sudoh
Proceedings of the 3rd Workshop on Neural Generation and Translation

pdf bib
Findings of the Third Workshop on Neural Generation and Translation
Hiroaki Hayashi | Yusuke Oda | Alexandra Birch | Ioannis Konstas | Andrew Finch | Minh-Thang Luong | Graham Neubig | Katsuhito Sudoh
Proceedings of the 3rd Workshop on Neural Generation and Translation

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). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language.

pdf bib
On the Importance of Word Boundaries in Character-level Neural Machine Translation
Duygu Ataman | Orhan Firat | Mattia A. Di Gangi | Marcello Federico | Alexandra Birch
Proceedings of the 3rd Workshop on Neural Generation and Translation

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. The standard approach to overcome this limitation is to segment words into subword units, typically using some external tools with arbitrary heuristics, resulting in vocabulary units not optimized for the translation task. Recent studies have shown that the same approach can be extended to perform NMT directly at the level of characters, which can deliver translation accuracy on-par with subword-based models, on the other hand, this requires relatively deeper networks. In this paper, we propose a more computationally-efficient solution for character-level NMT which implements a hierarchical decoding architecture where translations are subsequently generated at the level of words and characters. We evaluate different methods for open-vocabulary NMT in the machine translation task from English into five languages with distinct morphological typology, and show that the hierarchical decoding model can reach higher translation accuracy than the subword-level NMT model using significantly fewer parameters, while demonstrating better capacity in learning longer-distance contextual and grammatical dependencies than the standard character-level NMT model.

pdf bib
The University of Edinburgh’s Submissions to the WMT19 News Translation Task
Rachel Bawden | Nikolay Bogoychev | Ulrich Germann | Roman Grundkiewicz | Faheem Kirefu | Antonio Valerio Miceli Barone | Alexandra Birch
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

The University of Edinburgh participated in the WMT19 Shared Task on News Translation in six language directions: English↔Gujarati, English↔Chinese, German→English, and English→Czech. For all translation directions, we created or used back-translations of monolingual data in the target language as additional synthetic training data. For English↔Gujarati, we also explored semi-supervised MT with cross-lingual language model pre-training, and translation pivoting through Hindi. For translation to and from Chinese, we investigated character-based tokenisation vs. sub-word segmentation of Chinese text. For German→English, we studied the impact of vast amounts of back-translated training data on translation quality, gaining a few additional insights over Edunov et al. (2018). For English→Czech, we compared different preprocessing and tokenisation regimes.

pdf bib
Global Under-Resourced Media Translation (GoURMET)
Alexandra Birch | Barry Haddow | Ivan Tito | Antonio Valerio Miceli Barone | Rachel Bawden | Felipe Sánchez-Martínez | Mikel L. Forcada | Miquel Esplà-Gomis | Víctor Sánchez-Cartagena | Juan Antonio Pérez-Ortiz | Wilker Aziz | Andrew Secker | Peggy van der Kreeft
Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks

pdf bib
Samsung and University of Edinburgh’s System for the IWSLT 2019
Joanna Wetesko | Marcin Chochowski | Pawel Przybysz | Philip Williams | Roman Grundkiewicz | Rico Sennrich | Barry Haddow | Barone | Valerio Miceli | Alexandra Birch
Proceedings of the 16th International Conference on Spoken Language Translation

This paper describes the joint submission to the IWSLT 2019 English to Czech task by Samsung RD Institute, Poland, and the University of Edinburgh. Our submission was ultimately produced by combining four Transformer systems through a mixture of ensembling and reranking.

2018

pdf bib
Marian: Fast Neural Machine Translation in C++
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
Proceedings of ACL 2018, System Demonstrations

We present Marian, an efficient and self-contained Neural Machine Translation framework with an integrated automatic differentiation engine based on dynamic computation graphs. Marian is written entirely in C++. We describe the design of the encoder-decoder framework and demonstrate that a research-friendly toolkit can achieve high training and translation speed.

pdf bib
Samsung and University of Edinburgh’s System for the IWSLT 2018 Low Resource MT Task
Philip Williams | Marcin Chochowski | Pawel Przybysz | Rico Sennrich | Barry Haddow | Alexandra Birch
Proceedings of the 15th International Conference on Spoken Language Translation

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. We focused on supplementing the very limited in-domain Basque-English training data with out-of-domain data, with synthetic data, and with data for other language pairs. We also experimented with a variety of model architectures and features, which included the development of extensions to the Nematus toolkit. Our submission was ultimately produced by a system combination in which we reranked translations from our strongest individual system using multiple weaker systems.

pdf bib
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation
Alexandra Birch | Andrew Finch | Thang Luong | Graham Neubig | Yusuke Oda
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

pdf bib
Findings of the Second Workshop on Neural Machine Translation and Generation
Alexandra Birch | Andrew Finch | Minh-Thang Luong | Graham Neubig | Yusuke Oda
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

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). First, we summarize the research trends of papers presented in the proceedings, and note that there is particular interest in linguistic structure, domain adaptation, data augmentation, handling inadequate resources, and analysis of models. Second, we describe the results of the workshop’s shared task on efficient neural machine translation, where participants were tasked with creating MT systems that are both accurate and efficient.

pdf bib
Exploring gap filling as a cheaper alternative to reading comprehension questionnaires when evaluating machine translation for gisting
Mikel L. Forcada | Carolina Scarton | Lucia Specia | Barry Haddow | Alexandra Birch
Proceedings of the Third Conference on Machine Translation: Research Papers

A popular application of machine translation (MT) is gisting: MT is consumed as is to make sense of text in a foreign language. Evaluation of the usefulness of MT for gisting is surprisingly uncommon. The classical method uses reading comprehension questionnaires (RCQ), in which informants are asked to answer professionally-written questions in their language about a foreign text that has been machine-translated into their language. Recently, gap-filling (GF), a form of cloze testing, has been proposed as a cheaper alternative to RCQ. In GF, certain words are removed from reference translations and readers are asked to fill the gaps left using the machine-translated text as a hint. This paper reports, for the first time, a comparative evaluation, using both RCQ and GF, of translations from multiple MT systems for the same foreign texts, and a systematic study on the effect of variables such as gap density, gap-selection strategies, and document context in GF. The main findings of the study are: (a) both RCQ and GF clearly identify MT to be useful; (b) global RCQ and GF rankings for the MT systems are mostly in agreement; (c) GF scores vary very widely across informants, making comparisons among MT systems hard, and (d) unlike RCQ, which is framed around documents, GF evaluation can be framed at the sentence level. These findings support the use of GF as a cheaper alternative to RCQ.

pdf bib
Evaluating Discourse Phenomena in Neural Machine Translation
Rachel Bawden | Rico Sennrich | Alexandra Birch | Barry Haddow
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

For machine translation to tackle discourse phenomena, models must have access to extra-sentential linguistic context. There has been recent interest in modelling context in neural machine translation (NMT), but models have been principally evaluated with standard automatic metrics, poorly adapted to evaluating discourse phenomena. In this article, we present hand-crafted, discourse test sets, designed to test the models’ ability to exploit previous source and target sentences. We investigate the performance of recently proposed multi-encoder NMT models trained on subtitles for English to French. We also explore a novel way of exploiting context from the previous sentence. 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%). A simple strategy of decoding the concatenation of the previous and current sentence leads to good performance, and our novel strategy of multi-encoding and decoding of two sentences leads to the best performance (72.5% for coreference and 57% for coherence/cohesion), highlighting the importance of target-side context.

2017

pdf bib
Nematus: a Toolkit for Neural Machine Translation
Rico Sennrich | Orhan Firat | Kyunghyun Cho | Alexandra Birch | Barry Haddow | Julian Hitschler | Marcin Junczys-Dowmunt | Samuel Läubli | Antonio Valerio Miceli Barone | Jozef Mokry | Maria Nădejde
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

We present Nematus, a toolkit for Neural Machine Translation. The toolkit prioritizes high translation accuracy, usability, and extensibility. Nematus has been used to build top-performing submissions to shared translation tasks at WMT and IWSLT, and has been used to train systems for production environments.

pdf bib
The SUMMA Platform Prototype
Renars Liepins | Ulrich Germann | Guntis Barzdins | Alexandra Birch | Steve Renals | Susanne Weber | Peggy van der Kreeft | Hervé Bourlard | João Prieto | Ondřej Klejch | Peter Bell | Alexandros Lazaridis | Alfonso Mendes | Sebastian Riedel | Mariana S. C. Almeida | Pedro Balage | Shay B. Cohen | Tomasz Dwojak | Philip N. Garner | Andreas Giefer | Marcin Junczys-Dowmunt | Hina Imran | David Nogueira | Ahmed Ali | Sebastião Miranda | Andrei Popescu-Belis | Lesly Miculicich Werlen | Nikos Papasarantopoulos | Abiola Obamuyide | Clive Jones | Fahim Dalvi | Andreas Vlachos | Yang Wang | Sibo Tong | Rico Sennrich | Nikolaos Pappas | Shashi Narayan | Marco Damonte | Nadir Durrani | Sameer Khurana | Ahmed Abdelali | Hassan Sajjad | Stephan Vogel | David Sheppey | Chris Hernon | Jeff Mitchell
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.

pdf bib
The Samsung and University of Edinburgh’s submission to IWSLT17
Pawel Przybysz | Marcin Chochowski | Rico Sennrich | Barry Haddow | Alexandra Birch
Proceedings of the 14th International Conference on Spoken Language Translation

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. We took part in two translation directions, en-de and de-en. We also participated in the en-de and de-en lectures SLT task. The models have been trained with an attentional encoder-decoder model using the BiDeep model in Nematus. We filtered the training data to reduce the problem of noisy data, and we use back-translated monolingual data for domain-adaptation. We demonstrate the effectiveness of the different techniques that we applied via ablation studies. Our submission system outperforms our baseline, and last year’s University of Edinburgh submission to IWSLT, by more than 5 BLEU.

pdf bib
Proceedings of the First Workshop on Neural Machine Translation
Thang Luong | Alexandra Birch | Graham Neubig | Andrew Finch
Proceedings of the First Workshop on Neural Machine Translation

pdf bib
Predicting Target Language CCG Supertags Improves Neural Machine Translation
Maria Nădejde | Siva Reddy | Rico Sennrich | Tomasz Dwojak | Marcin Junczys-Dowmunt | Philipp Koehn | Alexandra Birch
Proceedings of the Second Conference on Machine Translation

pdf bib
Deep architectures for Neural Machine Translation
Antonio Valerio Miceli Barone | Jindřich Helcl | Rico Sennrich | Barry Haddow | Alexandra Birch
Proceedings of the Second Conference on Machine Translation

pdf bib
The University of Edinburgh’s Neural MT Systems for WMT17
Rico Sennrich | Alexandra Birch | Anna Currey | Ulrich Germann | Barry Haddow | Kenneth Heafield | Antonio Valerio Miceli Barone | Philip Williams
Proceedings of the Second Conference on Machine Translation

2016

pdf bib
Improving Neural Machine Translation Models with Monolingual Data
Rico Sennrich | Barry Haddow | Alexandra Birch
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Neural Machine Translation of Rare Words with Subword Units
Rico Sennrich | Barry Haddow | Alexandra Birch
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
HUME: Human UCCA-Based Evaluation of Machine Translation
Alexandra Birch | Omri Abend | Ondřej Bojar | Barry Haddow
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

pdf bib
Modeling Selectional Preferences of Verbs and Nouns in String-to-Tree Machine Translation
Maria Nădejde | Alexandra Birch | Philipp Koehn
Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers

pdf bib
Edinburgh Neural Machine Translation Systems for WMT 16
Rico Sennrich | Barry Haddow | Alexandra Birch
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

pdf bib
Controlling Politeness in Neural Machine Translation via Side Constraints
Rico Sennrich | Barry Haddow | Alexandra Birch
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

pdf bib
A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation
Maria Nădejde | Alexandra Birch | Philipp Koehn
Proceedings of the 13th International Conference on Spoken Language Translation

String-to-tree MT systems translate verbs without lexical or syntactic context on the source side and with limited target-side context. The lack of context is one reason why verb translation recall is as low as 45.5%. We propose a verb lexicon model trained with a feed-forward neural network that predicts the target verb conditioned on a wide source-side context. We show that a syntactic context extracted from the dependency parse of the source sentence improves the model’s accuracy by 1.5% over a baseline trained on a window context. When used as an extra feature for re-ranking the n-best list produced by the string-to-tree MT system, the verb lexicon model improves verb translation recall by more than 7%.

pdf bib
The University of Edinburgh’s systems submission to the MT task at IWSLT
Marcin Junczys-Dowmunt | Alexandra Birch
Proceedings of the 13th International Conference on Spoken Language Translation

This paper describes the submission of the University of Edinburgh team to the IWSLT MT task for TED talks. We took part in four translation directions, en-de, de-en, en-fr, and fr-en. The models have been trained with an attentional encoder-decoder model using Nematus, training data filtering and back-translation have been applied for domain-adaptation purposes.

2015

pdf bib
The Edinburgh machine translation systems for IWSLT 2015
Matthias Huck | Alexandra Birch
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign

pdf bib
Mixed domain vs. multi-domain statistical machine translation
Matthias Huck | Alexandra Birch | Barry Haddow
Proceedings of Machine Translation Summit XV: Papers

pdf bib
The Edinburgh/JHU Phrase-based Machine Translation Systems for WMT 2015
Barry Haddow | Matthias Huck | Alexandra Birch | Nikolay Bogoychev | Philipp Koehn
Proceedings of the Tenth Workshop on Statistical Machine Translation

2014

pdf bib
Edinburgh SLT and MT system description for the IWSLT 2014 evaluation
Alexandra Birch | Matthias Huck | Nadir Durrani | Nikolay Bogoychev | Philipp Koehn
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the University of Edinburgh’s spoken language translation (SLT) and machine translation (MT) systems for the IWSLT 2014 evaluation campaign. In the SLT track, we participated in the German↔English and English→French tasks. In the MT track, we participated in the German↔English, English→French, Arabic↔English, Farsi→English, Hebrew→English, Spanish↔English, and Portuguese-Brazil↔English tasks. For our SLT submissions, we experimented with comparing operation sequence models with bilingual neural network language models. For our MT submissions, we explored using unsupervised transliteration for languages which have a different script than English, in particular for Arabic, Farsi, and Hebrew. We also investigated syntax-based translation and system combination.

pdf bib
Combined spoken language translation
Markus Freitag | Joern Wuebker | Stephan Peitz | Hermann Ney | Matthias Huck | Alexandra Birch | Nadir Durrani | Philipp Koehn | Mohammed Mediani | Isabel Slawik | Jan Niehues | Eunach Cho | Alex Waibel | Nicola Bertoldi | Mauro Cettolo | Marcello Federico
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

EU-BRIDGE is a European research project which is aimed at developing innovative speech translation technology. One of the collaborative efforts within EU-BRIDGE is to produce joint submissions of up to four different partners to the evaluation campaign at the 2014 International Workshop on Spoken Language Translation (IWSLT). We submitted combined translations to the German→English spoken language translation (SLT) track as well as to the German→English, English→German and English→French machine translation (MT) tracks. In this paper, we present the techniques which were applied by the different individual translation systems of RWTH Aachen University, the University of Edinburgh, Karlsruhe Institute of Technology, and Fondazione Bruno Kessler. We then show the combination approach developed at RWTH Aachen University which combined the individual systems. The consensus translations yield empirical gains of up to 2.3 points in BLEU and 1.2 points in TER compared to the best individual system.

pdf bib
Generalizing a Strongly Lexicalized Parser using Unlabeled Data
Tejaswini Deoskar | Christos Christodoulopoulos | Alexandra Birch | Mark Steedman
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

2013

pdf bib
English SLT and MT system description for the IWSLT 2013 evaluation
Alexandra Birch | Nadir Durrani | Philipp Koehn
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper gives a description of the University of Edinburgh’s (UEDIN) systems for IWSLT 2013. We participated in all the MT tracks and the German-to-English and Englishto-French SLT tracks. Our SLT submissions experimented with including ASR uncertainty into the decoding process via confusion networks, and looked at different ways of punctuating ASR output. Our MT submissions are mainly based on a system used in the recent evaluation campaign at the Workshop on Statistical Machine Translation [1]. We additionally explored the use of generalized representations (Brown clusters, POS and morphological tags) translating out of English into European languages.

pdf bib
The UEDIN English ASR system for the IWSLT 2013 evaluation
Peter Bell | Fergus McInnes | Siva Reddy Gangireddy | Mark Sinclair | Alexandra Birch | Steve Renals
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the University of Edinburgh (UEDIN) English ASR system for the IWSLT 2013 Evaluation. Notable features of the system include deep neural network acoustic models in both tandem and hybrid configuration, cross-domain adaptation with multi-level adaptive networks, and the use of a recurrent neural network language model. Improvements to our system since the 2012 evaluation – which include the use of a significantly improved n-gram language model – result in a 19% relative WER reduction on the tst2012 set.

pdf bib
The Feasibility of HMEANT as a Human MT Evaluation Metric
Alexandra Birch | Barry Haddow | Ulrich Germann | Maria Nadejde | Christian Buck | Philipp Koehn
Proceedings of the Eighth Workshop on Statistical Machine Translation

2011

pdf bib
Simple Semi-Supervised Learning for Prepositional Phrase Attachment
Gregory F. Coppola | Alexandra Birch | Tejaswini Deoskar | Mark Steedman
Proceedings of the 12th International Conference on Parsing Technologies

pdf bib
Reordering Metrics for MT
Alexandra Birch | Miles Osborne
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

pdf bib
Soft Dependency Constraints for Reordering in Hierarchical Phrase-Based Translation
Yang Gao | Philipp Koehn | Alexandra Birch
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

pdf bib
LRscore for Evaluating Lexical and Reordering Quality in MT
Alexandra Birch | Miles Osborne
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

2009

pdf bib
462 Machine Translation Systems for Europe
Philipp Koehn | Alexandra Birch | Ralf Steinberger
Proceedings of Machine Translation Summit XII: Papers

pdf bib
A Quantitative Analysis of Reordering Phenomena
Alexandra Birch | Phil Blunsom | Miles Osborne
Proceedings of the Fourth Workshop on Statistical Machine Translation

2008

pdf bib
Predicting Success in Machine Translation
Alexandra Birch | Miles Osborne | Philipp Koehn
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

pdf bib
CCG Supertags in Factored Statistical Machine Translation
Alexandra Birch | Miles Osborne | Philipp Koehn
Proceedings of the Second Workshop on Statistical Machine Translation

pdf bib
Moses: Open Source Toolkit for Statistical Machine Translation
Philipp Koehn | Hieu Hoang | Alexandra Birch | Chris Callison-Burch | Marcello Federico | Nicola Bertoldi | Brooke Cowan | Wade Shen | Christine Moran | Richard Zens | Chris Dyer | Ondřej Bojar | Alexandra Constantin | Evan Herbst
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

2006

pdf bib
Constraining the Phrase-Based, Joint Probability Statistical Translation Model
Alexandra Birch | Chris Callison-Burch | Miles Osborne | Philipp Koehn
Proceedings on the Workshop on Statistical Machine Translation

pdf bib
Constraining the Phrase-Based, Joint Probability Statistical Translation Model
Alexandra Birch | Chris Callison-Burch | Miles Osborne
Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers

The Joint Probability Model proposed by Marcu and Wong (2002) provides a probabilistic framework for modeling phrase-based statistical machine transla- tion (SMT). The model’s usefulness is, however, limited by the computational complexity of estimating parameters at the phrase level. We present a method of constraining the search space of the Joint Probability Model based on statistically and linguistically motivated word align- ments. This method reduces the complexity and size of the Joint Model and allows it to display performance superior to the standard phrase-based models for small amounts of training material.
Search
Co-authors