Incorporating source-language paraphrases into phrase-based SMT with confusion networks

To increase the model coverage, sourcelanguage paraphrases have been utilized to boost SMT system performance. Previous work showed that word lattices constructed from paraphrases are able to reduce out-ofvocabulary words and to express inputs in different ways for better translation quality. However, such a word-lattice-based method suffers from two problems: 1) path duplications in word lattices decrease the capacities for potential paraphrases; 2) lattice decoding in SMT dramatically increases the search space and results in poor time efficiency. Therefore, in this paper, we adopt word confusion networks as the input structure to carry source-language paraphrase information. Similar to previous work, we use word lattices to build word confusion networks for merging of duplicated paths and faster decoding. Experiments are carried out on small-, medium- and large-scale English– Chinese translation tasks, and we show that compared with the word-lattice-based method, the decoding time on three tasks is reduced significantly (up to 79%) while comparable translation quality is obtained on the largescale task

InitialGAN: A Language GAN with Completely Random Initialization

Text generative models trained via Maximum Likelihood Estimation (MLE) suffer from the notorious exposure bias problem, and Generative Adversarial Networks (GANs) are shown to have potential to tackle this problem. Existing language GANs adopt estimators like REINFORCE or continuous relaxations to model word distributions. The inherent limitations of such estimators lead current models to rely on pre-training techniques (MLE pre-training or pre-trained embeddings). Representation modeling methods which are free from those limitations, however, are seldomly explored because of their poor performance in previous attempts. Our analyses reveal that invalid sampling methods and unhealthy gradients are the main contributors to such unsatisfactory performance. In this work, we present two techniques to tackle these problems: dropout sampling and fully normalized LSTM. Based on these two techniques, we propose InitialGAN whose parameters are randomly initialized in full. Besides, we introduce a new evaluation metric, Least Coverage Rate, to better evaluate the quality of generated samples. The experimental results demonstrate that InitialGAN outperforms both MLE and other compared models. To the best of our knowledge, it is the first time a language GAN can outperform MLE without using any pre-training techniques

Large-scale Hierarchical Alignment for Data-driven Text Rewriting

We propose a simple unsupervised method for extracting pseudo-parallel monolingual sentence pairs from comparable corpora representative of two different text styles, such as news articles and scientific papers. Our approach does not require a seed parallel corpus, but instead relies solely on hierarchical search over pre-trained embeddings of documents and sentences. We demonstrate the effectiveness of our method through automatic and extrinsic evaluation on text simplification from the normal to the Simple Wikipedia. We show that pseudo-parallel sentences extracted with our method not only supplement existing parallel data, but can even lead to competitive performance on their own.Comment: RANLP 201

Proceedings

Proceedings of the Workshop on Annotation and Exploitation of Parallel Corpora AEPC 2010. Editors: Lars Ahrenberg, Jörg Tiedemann and Martin Volk. NEALT Proceedings Series, Vol. 10 (2010), 98 pages. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15893

Resourcing machine translation with parallel treebanks

The benefits of syntax-based approaches to data-driven machine translation (MT) are clear: given the right model, a combination of hierarchical structure, constituent labels and morphological information can be exploited to produce more fluent, grammatical translation output. This has been demonstrated by the recent shift in research focus towards such linguistically motivated approaches. However, one issue facing developers of such models that is not encountered in the development of state-of-the-art string-based statistical MT (SMT) systems is the lack of available syntactically annotated training data for many languages. In this thesis, we propose a solution to the problem of limited resources for syntax-based MT by introducing a novel sub-sentential alignment algorithm for the induction of translational equivalence links between pairs of phrase structure trees. This algorithm, which operates on a language pair-independent basis, allows for the automatic generation of large-scale parallel treebanks which are useful not only for machine translation, but also across a variety of natural language processing tasks. We demonstrate the viability of our automatically generated parallel treebanks by means of a thorough evaluation process during which they are compared to a manually annotated gold standard parallel treebank both intrinsically and in an MT task. Following this, we hypothesise that these parallel treebanks are not only useful in syntax-based MT, but also have the potential to be exploited in other paradigms of MT. To this end, we carry out a large number of experiments across a variety of data sets and language pairs, in which we exploit the information encoded within the parallel treebanks in various components of phrase-based statistical MT systems. We demonstrate that improvements in translation accuracy can be achieved by enhancing SMT phrase tables with linguistically motivated phrase pairs extracted from a parallel treebank, while showing that a number of other features in SMT can also be supplemented with varying degrees of effectiveness. Finally, we examine ways in which synchronous grammars extracted from parallel treebanks can improve the quality of translation output, focussing on real translation examples from a syntax-based MT system

Multilingual representations and models for improved low-resource language processing

Word representations are the cornerstone of modern NLP. Representing words or characters using real-valued vectors as static representations that can capture the Semantics and encode the meaning has been popular among researchers. In more recent years, Pretrained Language Models using large amounts of data and creating contextualized representations achieved great performance in various tasks such as Semantic Role Labeling. These large pretrained language models are capable of storing and generalizing information and can be used as knowledge bases. Language models can produce multilingual representations while only using monolingual data during training. These multilingual representations can be beneficial in many tasks such as Machine Translation. Further, knowledge extraction models that only relied on information extracted from English resources, can now benefit from extra resources in other languages. Although these results were achieved for high-resource languages, there are thousands of languages that do not have large corpora. Moreover, for other tasks such as machine translation, if large monolingual data is not available, the models need parallel data, which is scarce for most languages. Further, many languages lack tokenization models, and splitting the text into meaningful segments such as words is not trivial. Although using subwords helps the models to have better coverage over unseen data and new words in the vocabulary, generalizing over low-resource languages with different alphabets and grammars is still a challenge. This thesis investigates methods to overcome these issues for low-resource languages. In the first publication, we explore the degree of multilinguality in multilingual pretrained language models. We demonstrate that these language models can produce high-quality word alignments without using parallel training data, which is not available for many languages. In the second paper, we extract word alignments for all available language pairs in the public bible corpus (PBC). Further, we created a tool for exploring these alignments which are especially helpful in studying low-resource languages. The third paper investigates word alignment in multiparallel corpora and exploits graph algorithms for extracting new alignment edges. In the fourth publication, we propose a new model to iteratively generate cross-lingual word embeddings and extract word alignments when only small parallel corpora are available. Lastly, the fifth paper finds that aggregation of different granularities of text can improve word alignment quality. We propose using subword sampling to produce such granularities

A Hybrid Machine Translation Framework for an Improved Translation Workflow

Over the past few decades, due to a continuing surge in the amount of content being translated and ever increasing pressure to deliver high quality and high throughput translation, translation industries are focusing their interest on adopting advanced technologies such as machine translation (MT), and automatic post-editing (APE) in their translation workflows. Despite the progress of the technology, the roles of humans and machines essentially remain intact as MT/APE are moving from the peripheries of the translation field closer towards collaborative human-machine based MT/APE in modern translation workflows. Professional translators increasingly become post-editors correcting raw MT/APE output instead of translating from scratch which in turn increases productivity in terms of translation speed. The last decade has seen substantial growth in research and development activities on improving MT; usually concentrating on selected aspects of workflows starting from training data pre-processing techniques to core MT processes to post-editing methods. To date, however, complete MT workflows are less investigated than the core MT processes. In the research presented in this thesis, we investigate avenues towards achieving improved MT workflows. We study how different MT paradigms can be utilized and integrated to best effect. We also investigate how different upstream and downstream component technologies can be hybridized to achieve overall improved MT. Finally we include an investigation into human-machine collaborative MT by taking humans in the loop. In many of (but not all) the experiments presented in this thesis we focus on data scenarios provided by low resource language settings.Aufgrund des stetig ansteigenden Übersetzungsvolumens in den letzten Jahrzehnten und gleichzeitig wachsendem Druck hohe Qualität innerhalb von kürzester Zeit liefern zu müssen sind Übersetzungsdienstleister darauf angewiesen, moderne Technologien wie Maschinelle Übersetzung (MT) und automatisches Post-Editing (APE) in den Übersetzungsworkflow einzubinden. Trotz erheblicher Fortschritte dieser Technologien haben sich die Rollen von Mensch und Maschine kaum verändert. MT/APE ist jedoch nunmehr nicht mehr nur eine Randerscheinung, sondern wird im modernen Übersetzungsworkflow zunehmend in Zusammenarbeit von Mensch und Maschine eingesetzt. Fachübersetzer werden immer mehr zu Post-Editoren und korrigieren den MT/APE-Output, statt wie bisher Übersetzungen komplett neu anzufertigen. So kann die Produktivität bezüglich der Übersetzungsgeschwindigkeit gesteigert werden. Im letzten Jahrzehnt hat sich in den Bereichen Forschung und Entwicklung zur Verbesserung von MT sehr viel getan: Einbindung des vollständigen Übersetzungsworkflows von der Vorbereitung der Trainingsdaten über den eigentlichen MT-Prozess bis hin zu Post-Editing-Methoden. Der vollständige Übersetzungsworkflow wird jedoch aus Datenperspektive weit weniger berücksichtigt als der eigentliche MT-Prozess. In dieser Dissertation werden Wege hin zum idealen oder zumindest verbesserten MT-Workflow untersucht. In den Experimenten wird dabei besondere Aufmertsamfit auf die speziellen Belange von sprachen mit geringen ressourcen gelegt. Es wird untersucht wie unterschiedliche MT-Paradigmen verwendet und optimal integriert werden können. Des Weiteren wird dargestellt wie unterschiedliche vor- und nachgelagerte Technologiekomponenten angepasst werden können, um insgesamt einen besseren MT-Output zu generieren. Abschließend wird gezeigt wie der Mensch in den MT-Workflow intergriert werden kann. Das Ziel dieser Arbeit ist es verschiedene Technologiekomponenten in den MT-Workflow zu integrieren um so einen verbesserten Gesamtworkflow zu schaffen. Hierfür werden hauptsächlich Hybridisierungsansätze verwendet. In dieser Arbeit werden außerdem Möglichkeiten untersucht, Menschen effektiv als Post-Editoren einzubinden

Investigating Language Impact in Bilingual Approaches for Computational Language Documentation

For endangered languages, data collection campaigns have to accommodate the challenge that many of them are from oral tradition, and producing transcriptions is costly. Therefore, it is fundamental to translate them into a widely spoken language to ensure interpretability of the recordings. In this paper we investigate how the choice of translation language affects the posterior documentation work and potential automatic approaches which will work on top of the produced bilingual corpus. For answering this question, we use the MaSS multilingual speech corpus (Boito et al., 2020) for creating 56 bilingual pairs that we apply to the task of low-resource unsupervised word segmentation and alignment. Our results highlight that the choice of language for translation influences the word segmentation performance, and that different lexicons are learned by using different aligned translations. Lastly, this paper proposes a hybrid approach for bilingual word segmentation, combining boundary clues extracted from a non-parametric Bayesian model (Goldwater et al., 2009a) with the attentional word segmentation neural model from Godard et al. (2018). Our results suggest that incorporating these clues into the neural models' input representation increases their translation and alignment quality, specially for challenging language pairs.Comment: Accepted to 1st Joint SLTU and CCURL Worksho