Multi-modal post-editing of machine translation

As MT quality continues to improve, more and more translators switch from traditional translation from scratch to PE of MT output, which has been shown to save time and reduce errors. Instead of mainly generating text, translators are now asked to correct errors within otherwise helpful translation proposals, where repetitive MT errors make the process tiresome, while hard-to-spot errors make PE a cognitively demanding activity. Our contribution is three-fold: first, we explore whether interaction modalities other than mouse and keyboard could well support PE by creating and testing the MMPE translation environment. MMPE allows translators to cross out or hand-write text, drag and drop words for reordering, use spoken commands or hand gestures to manipulate text, or to combine any of these input modalities. Second, our interviews revealed that translators see value in automatically receiving additional translation support when a high CL is detected during PE. We therefore developed a sensor framework using a wide range of physiological and behavioral data to estimate perceived CL and tested it in three studies, showing that multi-modal, eye, heart, and skin measures can be used to make translation environments cognition-aware. Third, we present two multi-encoder Transformer architectures for APE and discuss how these can adapt MT output to a domain and thereby avoid correcting repetitive MT errors.Angesichts der stetig steigenden Qualität maschineller Übersetzungssysteme (MÜ) post-editieren (PE) immer mehr Übersetzer die MÜ-Ausgabe, was im Vergleich zur herkömmlichen Übersetzung Zeit spart und Fehler reduziert. Anstatt primär Text zu generieren, müssen Übersetzer nun Fehler in ansonsten hilfreichen Übersetzungsvorschlägen korrigieren. Dennoch bleibt die Arbeit durch wiederkehrende MÜ-Fehler mühsam und schwer zu erkennende Fehler fordern die Übersetzer kognitiv. Wir tragen auf drei Ebenen zur Verbesserung des PE bei: Erstens untersuchen wir, ob andere Interaktionsmodalitäten als Maus und Tastatur das PE unterstützen können, indem wir die Übersetzungsumgebung MMPE entwickeln und testen. MMPE ermöglicht es, Text handschriftlich, per Sprache oder über Handgesten zu verändern, Wörter per Drag & Drop neu anzuordnen oder all diese Eingabemodalitäten zu kombinieren. Zweitens stellen wir ein Sensor-Framework vor, das eine Vielzahl physiologischer und verhaltensbezogener Messwerte verwendet, um die kognitive Last (KL) abzuschätzen. In drei Studien konnten wir zeigen, dass multimodale Messung von Augen-, Herz- und Hautmerkmalen verwendet werden kann, um Übersetzungsumgebungen an die KL der Übersetzer anzupassen. Drittens stellen wir zwei Multi-Encoder-Transformer-Architekturen für das automatische Post-Editieren (APE) vor und erörtern, wie diese die MÜ-Ausgabe an eine Domäne anpassen und dadurch die Korrektur von sich wiederholenden MÜ-Fehlern vermeiden können.Deutsche Forschungsgemeinschaft (DFG), Projekt MMP

Towards a better integration of fuzzy matches in neural machine translation through data augmentation

We identify a number of aspects that can boost the performance of Neural Fuzzy Repair (NFR), an easy-to-implement method to integrate translation memory matches and neural machine translation (NMT). We explore various ways of maximising the added value of retrieved matches within the NFR paradigm for eight language combinations, using Transformer NMT systems. In particular, we test the impact of different fuzzy matching techniques, sub-word-level segmentation methods and alignment-based features on overall translation quality. Furthermore, we propose a fuzzy match combination technique that aims to maximise the coverage of source words. This is supplemented with an analysis of how translation quality is affected by input sentence length and fuzzy match score. The results show that applying a combination of the tested modifications leads to a significant increase in estimated translation quality over all baselines for all language combinations

Incorporating visual information into neural machine translation

In this work, we study different ways to enrich Machine Translation (MT) models using information obtained from images. Specifically, we propose different models to incorporate images into MT by transferring learning from pre-trained convolutional neural networks (CNN) trained for classifying images. We use these pre-trained CNNs for image feature extraction, and use two different types of visual features: global visual features, that encode an entire image into one single real-valued feature vector; and local visual features, that encode different areas of an image into separate real-valued vectors, therefore also encoding spatial information. We first study how to train embeddings that are both multilingual and multi-modal, and use global visual features and multilingual sentences for training. Second, we propose different models to incorporate global visual features into state-of-the-art Neural Machine Translation (NMT): (i) as words in the source sentence, (ii) to initialise the encoder hidden state, and (iii) as additional data to initialise the decoder hidden state. Finally, we put forward one model to incorporate local visual features into NMT: (i) a NMT model with an independent visual attention mechanism integrated into the same decoder Recurrent Neural Network (RNN) as the source-language attention mechanism. We evaluate our models on the Multi30k, a publicly available, general domain data set, and also on a proprietary data set of product listings and images built by eBay Inc., which was made available for the purpose of this research. We report state-of-the-art results on the publicly available Multi30k data set. Our best models also significantly improve on comparable phrase-based Statistical MT (PBSMT) models trained on the same data set, according to widely adopted MT metrics

The way out of the box

Synopsis: Cognitive aspects of the translation process have become central in Translation and Interpreting Studies in recent years, further establishing the field of Cognitive Translatology. Empirical and interdisciplinary studies investigating translation and interpreting processes promise a hitherto unprecedented predictive and explanatory power. This collection contains such studies which observe behaviour during translation and interpreting. The contributions cover a vast area and investigate behaviour during translation and interpreting – with a focus on training of future professionals, on language processing more generally, on the role of technology in the practice of translation and interpreting, on translation of multimodal media texts, on aspects of ergonomics and usability, on emotions, self-concept and psychological factors, and finally also on revision and post-editing. For the present publication, we selected a number of contributions presented at the Second International Congress on Translation, Interpreting and Cognition hosted by the Tra&Co Lab at the Johannes Gutenberg University of Mainz

Translation, interpreting, cognition

Cognitive aspects of the translation process have become central in Translation and Interpreting Studies in recent years, further establishing the field of Cognitive Translatology. Empirical and interdisciplinary studies investigating translation and interpreting processes promise a hitherto unprecedented predictive and explanatory power. This collection contains such studies which observe behaviour during translation and interpreting. The contributions cover a vast area and investigate behaviour during translation and interpreting – with a focus on training of future professionals, on language processing more generally, on the role of technology in the practice of translation and interpreting, on translation of multimodal media texts, on aspects of ergonomics and usability, on emotions, self-concept and psychological factors, and finally also on revision and post-editing. For the present publication, we selected a number of contributions presented at the Second International Congress on Translation, Interpreting and Cognition hosted by the Tra&Co Lab at the Johannes Gutenberg University of Mainz. Most of the papers in this volume are formulated in a particular constraint-based grammar framework, Head-driven Phrase Structure Grammar. The contributions investigate how the lexical and constructional aspects of this theory can be combined to provide an answer to this question across different linguistic sub-theories

Representations of Idioms for Natural Language Processing: Idiom type and token identification, Language Modelling and Neural Machine Translation

An idiom is a multiword expression (MWE) whose meaning is non- compositional, i.e., the meaning of the expression is different from the meaning of its individual components. Idioms are complex construc- tions of language used creatively across almost all text genres. Idioms pose problems to natural language processing (NLP) systems due to their non-compositional nature, and the correct processing of idioms can improve a wide range of NLP systems. Current approaches to idiom processing vary in terms of the amount of discourse history required to extract the features necessary to build representations for the expressions. These features are, in general, stat- istics extracted from the text and often fail to capture all the nuances involved in idiom usage. We argue in this thesis that a more flexible representations must be used to process idioms in a range of idiom related tasks. We demonstrate that high-dimensional representations allow idiom classifiers to better model the interactions between global and local features and thereby improve the performance of these systems with regard to processing idioms. In support of this thesis we demonstrate that distributed representations of sentences, such as those generated by a Recurrent Neural Network (RNN) greatly reduce the amount of discourse history required to process idioms and that by using those representations a “general” classifier, that can take any expression as input and classify it as either an idiomatic or literal usage, is feasible. We also propose and evaluate a novel technique to add an attention module to a language model in order to bring forward past information in a RNN-based Language Model (RNN-LM). The results of our evaluation experiments demonstrate that this attention module increases the performance of such models in terms of the perplexity achieved when processing idioms. Our analysis also shows that it improves the performance of RNN-LMs on literal language and, at the same time, helps to bridge long-distance dependencies and reduce the number of parameters required in RNN-LMs to achieve state-of-the-art performance. We investigate the adaptation of this novel RNN-LM to Neural Machine Translation (NMT) systems and we show that, despite the mixed results, it improves the translation of idioms into languages that require distant reordering such as German. We also show that these models are suited to small corpora for in-domain translations for language pairs such as English/Brazilian-Portuguese

Domain adaptation for statistical machine translation and neural machine translation

Both Statistical Machine Translation and Neural Machine Translation (NMT) are data-dependent learning approaches to Machine Translation (MT). The prerequisite is a large volume of training data in order to generate good statistical models. However, even if large volume of training corpora are available for MT, finding training data which are similar to the specific domains is still difficult. The MT models trained using the limited specific domain data cannot have sufficient coverage on the linguistic phenomena in that domain, which makes this a very challenging task. Because word meanings, genres or topics differ between domains, using the additional data from other domains can increase the dissimilar- ities between the training and testing data, and result in reduced translation quality. Such a challenge is defined as ‘domain adaptation’ challenge in the literature. In this thesis, we investigate domain adaptation in two different scenarios, namely a domain-awareness scenario and a domain-unawareness scenario. In a domain-awareness scenario, the domain information is given explicitly in the training data. We are interested in developing domain-adaptation techniques which transfer knowledge gained from the other domains to a desired domain. In the approach proposed here probabilistic values indicating the domain-likeness features for words are estimated by the context rather than by the words themselves. We then apply those features to the combined translation models in an MT system. We empirically show that translation quality can be significantly improved compared with previous related work. We then turn our interest to the recently proposed neural network training. We describe a domain-adaptation approach which can exploit large pre-trained word vector models. We evaluate our approach on both language modelling and machine translation tasks to demonstrate its efficiency, effectiveness and flexibility in a domain-awareness scenario. xiiIn a domain-unawareness scenario, the domain information is not given explicitly in the training data. The training data is heterogeneous, e.g. originated from tens or even hundreds of different resources without well-defined domain labels. We overcome such a challenge by deriving the topic information from the training corpora using well-estimated topic modelling algorithms. In this scenario, we pay particular attention to the most recent NMT framework. We are concerning with making a better lexical choice and improving the overall translation quality. Experimentally, we show that our model can perform better lexical choice, improve the overall translation quality and reduce the number of unknown words