Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Application of pre-training and fine-tuning AI models to machine translation: a case study of multilingual text classification in Baidu

With the development of international information technology, we are producing a huge amount of information all the time. The processing ability of information in various languages is gradually replacing information and becoming a rarer resource. How to obtain the most effective information in such a large and complex amount of multilingual textual information is a major goal of multilingual information processing. Multilingual text classification helps users to break the language barrier and accurately locate the required information and triage information. At the same time, the rapid development of the Internet has accelerated the communication among users of various languages, giving rise to a large number of multilingual texts, such as book and movie reviews, online chats, product introductions and other forms, which contain a large amount of valuable implicit information and urgently need automated tools to categorize and process those multilingual texts. This work describes the Natural Language Process (NLP) sub-task known as Multilingual Text Classification (MTC) performed within the context of Baidu, a Chinese leading AI company with a strong Internet base, whose NLP division led the industry in deep learning technology to go online in Machine Translation (MT) and search. Multilingual text classification is an important module in NLP machine translation and a basic module in NLP tasks. It can be applied to many fields, such as Fake Reviews Detection, News Headlines Categories Classification, Analysis of positive and negative reviews and so on. In the following work, we will first define the AI model paradigm of 'pre-training and fine-tuning' in deep learning in the Baidu NLP department. Then investigated the application scenarios of multilingual text classification. Most of the text classification systems currently available in the Chinese market are designed for a single language, such as Alibaba's text classification system. If users need to classify texts of the same category in multiple languages, they need to train multiple single text classification systems and then classify them one by one. However, many internationalized products do not have a single text language, such as AliExpress cross-border e-commerce business, Airbnb B&B business, etc. Industry needs to understand and classify users’ reviews in various languages, and have conducted in-depth statistics and marketing strategy development, and multilingual text classification is particularly important in this scenario. Therefore, we focus on interpreting the methodology of multilingual text classification model of machine translation in Baidu NLP department, and capture sets of multilingual data of reviews, news headlines and other data for manual classification and labeling, use the labeling results for fine-tuning of multilingual text classification model, and output the quality evaluation data of Baidu multilingual text classification model after fine-tuning. We will discuss if the pre-training and fine-tuning of the large model can substantially improve the quality and performance of multilingual text classification. Finally, based on the machine translation-multilingual text classification model, we derive the application method of pre-training and fine-tuning paradigm in the current cutting-edge deep learning AI model under the NLP system and verify the generality and cutting-edge of the pre-training and fine-tuning paradigm in the deep learning-intelligent search field.Com o desenvolvimento da tecnologia de informação internacional, estamos sempre a produzir uma enorme quantidade de informação e o recurso mais escasso já não é a informação, mas a capacidade de processar informação em cada língua. A maior parte da informação multilingue é expressa sob a forma de texto. Como obter a informação mais eficaz numa quantidade tão considerável e complexa de informação textual multilingue é um dos principais objetivos do processamento de informação multilingue. A classificação de texto multilingue ajuda os utilizadores a quebrar a barreira linguística e a localizar com precisão a informação necessária e a classificá-la. Ao mesmo tempo, o rápido desenvolvimento da Internet acelerou a comunicação entre utilizadores de várias línguas, dando origem a um grande número de textos multilingues, tais como críticas de livros e filmes, chats, introduções de produtos e outros distintos textos, que contêm uma grande quantidade de informação implícita valiosa e necessitam urgentemente de ferramentas automatizadas para categorizar e processar esses textos multilingues. Este trabalho descreve a subtarefa do Processamento de Linguagem Natural (PNL) conhecida como Classificação de Texto Multilingue (MTC), realizada no contexto da Baidu, uma empresa chinesa líder em IA, cuja equipa de PNL levou a indústria em tecnologia baseada em aprendizagem neuronal a destacar-se em Tradução Automática (MT) e pesquisa científica. A classificação multilingue de textos é um módulo importante na tradução automática de PNL e um módulo básico em tarefas de PNL. A MTC pode ser aplicada a muitos campos, tais como análise de sentimentos multilingues, categorização de notícias, filtragem de conteúdos indesejados (do inglês spam), entre outros. Neste trabalho, iremos primeiro definir o paradigma do modelo AI de 'pré-treino e afinação' em aprendizagem profunda no departamento de PNL da Baidu. Em seguida, realizaremos a pesquisa sobre outros produtos no mercado com capacidade de classificação de texto — a classificação de texto levada a cabo pela Alibaba. Após a pesquisa, verificamos que a maioria dos sistemas de classificação de texto atualmente disponíveis no mercado chinês são concebidos para uma única língua, tal como o sistema de classificação de texto Alibaba. Se os utilizadores precisarem de classificar textos da mesma categoria em várias línguas, precisam de aplicar vários sistemas de classificação de texto para cada língua e depois classificá-los um a um. No entanto, muitos produtos internacionalizados não têm uma única língua de texto, tais como AliExpress comércio eletrónico transfronteiriço, Airbnb B&B business, etc. A indústria precisa compreender e classificar as revisões dos utilizadores em várias línguas. Esta necessidade conduziu a um desenvolvimento aprofundado de estatísticas e estratégias de marketing, e a classificação de textos multilingues é particularmente importante neste cenário. Desta forma, concentrar-nos-emos na interpretação da metodologia do modelo de classificação de texto multilingue da tradução automática no departamento de PNL Baidu. Colhemos para o efeito conjuntos de dados multilingues de comentários e críticas, manchetes de notícias e outros dados para classificação manual, utilizamos os resultados dessa classificação para o aperfeiçoamento do modelo de classificação de texto multilingue e produzimos os dados de avaliação da qualidade do modelo de classificação de texto multilingue da Baidu. Discutiremos se o pré-treino e o aperfeiçoamento do modelo podem melhorar substancialmente a qualidade e o desempenho da classificação de texto multilingue. Finalmente, com base no modelo de classificação de texto multilingue de tradução automática, derivamos o método de aplicação do paradigma de pré-formação e afinação no atual modelo de IA de aprendizagem profunda de ponta sob o sistema de PNL, e verificamos a robustez e os resultados positivos do paradigma de pré-treino e afinação no campo de pesquisa de aprendizagem profunda

Online Incremental Machine Translation

In this thesis we investigate the automatic improvements of statistical machine translation systems at runtime based on user feedback. We also propose a framework to use the proposed algorithms in large scale translation settings

Recycling texts: human evaluation of example-based machine translation subtitles for DVD

This project focuses on translation reusability in audiovisual contexts. Specifically, the project seeks to establish (1) whether target language subtitles produced by an EBMT system are considered intelligible and acceptable by viewers of movies on DVD, and (2)whether a relationship exists between the ‘profiles’ of corpora used to train an EBMT system, on the one hand, and viewers’ judgements of the intelligibility and acceptability of the subtitles produced by the system, on the other. The impact of other factors, namely: whether movie-viewing subjects have knowledge of the soundtrack language; subjects’ linguistic background; and subjects’ prior knowledge of the (Harry Potter) movie clips viewed; is also investigated. Corpus profiling is based on measurements (partly using corpus-analysis tools) of three characteristics of the corpora used to train the EBMT system: the number of source language repetitions they contain; the size of the corpus; and the homogeneity of the corpus (independent variables). As a quality control measure in this prospective profiling phase, we also elicit human judgements (through a combined questionnaire and interview) on the quality of the corpus data and on the reusability in new contexts of the TL subtitles. The intelligibility and acceptability of EBMT-produced subtitles (dependent variables) are, in turn, established through end-user evaluation sessions. In these sessions 44 native German-speaking subjects view short movie clips containing EBMT-generated German subtitles, and following each clip answer questions (again, through a combined questionnaire and interview) relating to the quality characteristics mentioned above. The findings of the study suggest that an increase in corpus size along with a concomitant increase in the number of source language repetitions and a decrease in corpus homogeneity, improves the readability of the EBMT-generated subtitles. It does not, however, have a significant effect on the comprehensibility, style or wellformedness of the EBMT-generated subtitles. Increasing corpus size and SL repetitions also results in a higher number of alternative TL translations in the corpus that are deemed acceptable by evaluators in the corpus profiling phase. The research also finds that subjects are more critical of subtitles when they do not understand the soundtrack language, while subjects’ linguistic background does not have a significant effect on their judgements of the quality of EBMT-generated subtitles. Prior knowledge of the Harry Potter genre, on the other hand, appears to have an effect on how viewing subjects rate the severity of observed errors in the subtitles, and on how they rate the style of subtitles, although this effect is training corpus-dependent. The introduction of repeated subtitles did not reduce the intelligibility or acceptability of the subtitles. Overall, the findings indicate that the subtitles deemed the most acceptable when evaluated in a non-AVT environment (albeit one in which rich contextual information was available) were the same as the subtitles deemed the most acceptable in an AVT environment, although richer data were gathered from the AVT environment

Final Report on User Interface Studies, Cognitive and User Modelling

D1.3 marks the final CASMACAT report on user interface studies, cognitive and user modelling covering the completion of tasks T1.5 (Cognitive Modelling) and T1.6 (User Modelling) as part of Work Package 1. Within tasks T1.1 to T1.4, a series of experiments have established a solid understanding of human behaviour in computer-aided translation, focusing on the use of visualization options, different translation modalities, individual differences in translation production, translator types and translation/postediting styles. Additionally, the bulk of this experimental data has been released as a publicly available database under a creative common license and further details on this can be found in D1.4. In parallel to these more holistic studies, a second set of experiments aimed to examine some of these factors in a constrained laboratory setting. These focused on the underlying psycholinguistic processing and cognitive modelling of translators’ activity to capture reading difficulty, verification and perplexity during translation and post-editing. This deliverable combines these earlier empirical findings with experiments conducted in Year 3 of the project and grounds translation within a broader theoretical framework associated with human sentence processing and communication. As well as broadening our general understanding of bilingual cognitive processing, there were two major objectives behind the experimental investigations in Year 3. The first was to evaluate the utility of providing translators with Source-Target word alignment information through spatially-direct visual cues. The second was to determine what, if any, differences arise from expertise by comparing the results between a group of bilinguals and a group of professionally trained translators on the same translation-related tasks

Empirical modelling of translation and interpreting

"Empirical research is carried out in a cyclic way: approaching a research area bottom-up, data lead to interpretations and ideally to the abstraction of laws, on the basis of which a theory can be derived. Deductive research is based on a theory, on the basis of which hypotheses can be formulated and tested against the background of empirical data. Looking at the state-of-the-art in translation studies, either theories as well as models are designed or empirical data are collected and interpreted. However, the final step is still lacking: so far, empirical data has not lead to the formulation of theories or models, whereas existing theories and models have not yet been comprehensively tested with empirical methods. This publication addresses these issues from several perspectives: multi-method product- as well as process-based research may gain insights into translation as well as interpreting phenomena. These phenomena may include cognitive and organizational processes, procedures and strategies, competence and performance, translation properties and universals, etc. Empirical findings about the deeper structures of translation and interpreting will reduce the gap between translation and interpreting practice and model and theory building. Furthermore, the availability of more large-scale empirical testing triggers the development of models and theories concerning translation and interpreting phenomena and behavior based on quantifiable, replicable and transparent data.

The Circle of Meaning: From Translation to Paraphrasing and Back

The preservation of meaning between inputs and outputs is perhaps the most ambitious and, often, the most elusive goal of systems that attempt to process natural language. Nowhere is this goal of more obvious importance than for the tasks of machine translation and paraphrase generation. Preserving meaning between the input and the output is paramount for both, the monolingual vs bilingual distinction notwithstanding. In this thesis, I present a novel, symbiotic relationship between these two tasks that I term the "circle of meaning''. Today's statistical machine translation (SMT) systems require high quality human translations for parameter tuning, in addition to large bi-texts for learning the translation units. This parameter tuning usually involves generating translations at different points in the parameter space and obtaining feedback against human-authored reference translations as to how good the translations. This feedback then dictates what point in the parameter space should be explored next. To measure this feedback, it is generally considered wise to have multiple (usually 4) reference translations to avoid unfair penalization of translation hypotheses which could easily happen given the large number of ways in which a sentence can be translated from one language to another. However, this reliance on multiple reference translations creates a problem since they are labor intensive and expensive to obtain. Therefore, most current MT datasets only contain a single reference. This leads to the problem of reference sparsity---the primary open problem that I address in this dissertation---one that has a serious effect on the SMT parameter tuning process. Bannard and Callison-Burch (2005) were the first to provide a practical connection between phrase-based statistical machine translation and paraphrase generation. However, their technique is restricted to generating phrasal paraphrases. I build upon their approach and augment a phrasal paraphrase extractor into a sentential paraphraser with extremely broad coverage. The novelty in this augmentation lies in the further strengthening of the connection between statistical machine translation and paraphrase generation; whereas Bannard and Callison-Burch only relied on SMT machinery to extract phrasal paraphrase rules and stopped there, I take it a few steps further and build a full English-to-English SMT system. This system can, as expected, ``translate'' any English input sentence into a new English sentence with the same degree of meaning preservation that exists in a bilingual SMT system. In fact, being a state-of-the-art SMT system, it is able to generate n-best "translations" for any given input sentence. This sentential paraphraser, built almost entirely from existing SMT machinery, represents the first 180 degrees of the circle of meaning. To complete the circle, I describe a novel connection in the other direction. I claim that the sentential paraphraser, once built in this fashion, can provide a solution to the reference sparsity problem and, hence, be used to improve the performance a bilingual SMT system. I discuss two different instantiations of the sentential paraphraser and show several results that provide empirical validation for this connection

Idiom treatment experiments in machine translation

Idiomatic expressions pose a particular challenge for the today\u27;s Machine Translation systems, because their translation mostly does not result literally, but logically. The present dissertation shows, how with the help of a corpus, and morphosyntactic rules, such idiomatic expressions can be recognized and finally correctly translated. The work leads the reader in the first chapter generally to the field of Machine Translation and following that, it focuses on the special field of Example-based Machine Translation. Next, an important part of the doctoral thesis dissertation is devoted to the theory of idiomatic expressions. The practical part of the thesis describes how the hybrid Example-based Machine Translation system METIS-II, with the help of morphosyntactic rules, is able to correctly process certain idiomatic expressions and finally, to translate them. The following chapter deals with the function of the transfer system CAT2 and its handling of the idiomatic expressions. The last part of the thesis includes the evaluation of three commercial systems, namely SYSTRAN, T1 Langenscheidt, and Power Translator Pro, with respect to continuous and discontinuous idiomatic expressions. For this, both small corpora and a part of the extensive corpus Europarl and the Digital Lexicon of the German Language in 20th century were processed, firstly manually and then automatically. The dissertation concludes with results from this evaluation.Idiomatische Redewendungen stellen für heutige maschinelle Übersetzungssysteme eine besondere Herausforderung dar, da ihre Übersetzung nicht wörtlich, sondern stets sinngemäß erfolgen muss. Die vorliegende Dissertation zeigt, wie mit Hilfe eines Korpus sowie morphosyntaktischer Regeln solche idiomatische Redewendungen erkannt und am Ende richtig übersetzt werden können. Die Arbeit führt den Leser im ersten Kapitel allgemein in das Gebiet der Maschinellen Übersetzung ein und vertieft im Anschluss daran das Spezialgebiet der Beispielbasierten Maschinellen Übersetzung. Im Folgenden widmet sich ein wesentlicher Teil der Doktorarbeit der Theorie über idiomatische Redewendungen. Der praktische Teil der Arbeit beschreibt wie das hybride Beispielbasierte Maschinelle Übersetzungssystem METIS-II mit Hilfe von morphosyntaktischen Regeln befähigt wurde, bestimmte idiomatische Redewendungen korrekt zu bearbeiten und am Ende zu übersetzen. Das nachfolgende Kapitel behandelt die Funktion des Transfersystems CAT2 und dessen Umgang mit idiomatischen Wendungen. Der letzte Teil der Arbeit beinhaltet die Evaluation von drei kommerzielle Systemen, nämlich SYSTRAN, T1 Langenscheidt und Power Translator Pro, in Bezug auf deren Umgang mit kontinuierlichen und diskontinuierlichen idiomatischen Redewendungen. Hierzu wurden sowohl kleine Korpora als auch ein Teil des umfangreichen Korpus Europarl und des Digatalen Wörterbuchs der deutschen Sprache des 20. Jh. erst manuell und dann maschinell bearbeitet. Die Dissertation wird mit Folgerungen aus der Evaluation abgeschlossen