Findings of the 2014 Workshop on Statistical Machine Translation

This paper presents the results of the WMT14 shared tasks, which included a standard news translation task, a separate medical translation task, a task for run-time estimation of machine translation quality, and a metrics task. This year, 143 machine translation systems from 23 institutions were submitted to the ten translation directions in the standard translation task. An additional 6 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had four subtasks, with a total of 10 teams, submitting 57 entries

Proceedings of the 17th Annual Conference of the European Association for Machine Translation

Proceedings of the 17th Annual Conference of the European Association for Machine Translation (EAMT

Hybrid machine translation using binary classification models trained on joint, binarised feature vectors

We describe the design and implementation of a system combination method for machine translation output. It is based on sentence selection using binary classification models estimated on joint, binarised feature vectors. By contrast to existing system combination methods which work by dividing candidate translations into n-grams, i.e., sequences of n words or tokens, our framework performs sentence selection which does not alter the selected, best translation. First, we investigate the potential performance gain attainable by optimal sentence selection. To do so, we conduct the largest meta-study on data released by the yearly Workshop on Statistical Machine Translation (WMT). Second, we introduce so-called joint, binarised feature vectors which explicitly model feature value comparison for two systems A, B. We compare different settings for training binary classifiers using single, joint, as well as joint, binarised feature vectors. After having shown the potential of both selection and binarisation as methodological paradigms, we combine these two into a combination framework which applies pairwise comparison of all candidate systems to determine the best translation for each individual sentence. Our system is able to outperform other state-of-the-art system combination approaches; this is confirmed by our experiments. We conclude by summarising the main findings and contributions of our thesis and by giving an outlook to future research directions.Wir beschreiben den Entwurf und die Implementierung eines Systems zur Kombination von Übersetzungen auf Basis nicht modifizierender Auswahl gegebener Kandidaten. Die zugehörigen, binären Klassifikationsmodelle werden unter Verwendung von gemeinsamen, binärisierten Merkmalsvektoren trainiert. Im Gegensatz zu anderen Methoden zur Systemkombination, die die gegebenen Kandidatenübersetzungen in n-Gramme, d.h., Sequenzen von n Worten oder Symbolen zerlegen, funktioniert unser Ansatz mit Hilfe von nicht modifizierender Auswahl der besten Übersetzung. Zuerst untersuchen wir das Potenzial eines solches Ansatzes im Hinblick auf die maximale theoretisch mögliche Verbesserung und führen die größte Meta-Studie auf Daten, welche jährlich im Rahmen der Arbeitstreffen zur Statistischen Maschinellen Übersetzung (WMT) veröffentlicht worden sind, durch. Danach definieren wir sogenannte gemeinsame, binärisierte Merkmalsvektoren, welche explizit den Merkmalsvergleich zweier Systeme A, B modellieren. Wir vergleichen verschiedene Konfigurationen zum Training binärer Klassifikationsmodelle basierend auf einfachen, gemeinsamen, sowie gemeinsamen, binärisierten Merkmalsvektoren. Abschließend kombinieren wir beide Verfahren zu einer Methodik, die paarweise Vergleiche aller Quellsysteme zur Bestimmung der besten Übesetzung einsetzt. Wir schließen mit einer Zusammenfassung und einem Ausblick auf zukünftige Forschungsthemen

Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding

Coping with low data availability for social media crisis message categorisation

During crisis situations, social media allows people to quickly share information, including messages requesting help. This can be valuable to emergency responders, who need to categorise and prioritise these messages based on the type of assistance being requested. However, the high volume of messages makes it difficult to filter and prioritise them without the use of computational techniques. Fully supervised filtering techniques for crisis message categorisation typically require a large amount of annotated training data, but this can be difficult to obtain during an ongoing crisis and is expensive in terms of time and labour to create. This thesis focuses on addressing the challenge of low data availability when categorising crisis messages for emergency response. It first presents domain adaptation as a solution for this problem, which involves learning a categorisation model from annotated data from past crisis events (source domain) and adapting it to categorise messages from an ongoing crisis event (target domain). In many-to-many adaptation, where the model is trained on multiple past events and adapted to multiple ongoing events, a multi-task learning approach is proposed using pre-trained language models. This approach outperforms baselines and an ensemble approach further improves performance..