Invite you to consult the content chapter 11 "Management of translation exposure" of lecture International financial management below to capture the content: Alternative currency translation methods, translation exposure, financial statement impact of translation alternative,...
The BTL aims to stimulate research and training in translation and interpreting studies. The Library provides a forum for a variety of approaches (which may sometimes be conflicting) in a socio-cultural, historical, theoretical, applied and pedagogical context. The Library includes scholarly works, reference books, postgraduate text books and readers in the English language.
The European Society for Translation Studies (EST) Subseries is a publication channel within the Library to optimize EST’s function as a forum for the translation and interpreting research community.
The present paper proposes a method by which to translate outputs of a robust HPSG parser into semantic representations of Typed Dynamic Logic (TDL), a dynamic plural semantics defined in typed lambda calculus. With its higher-order representations of contexts, TDL analyzes and describes the inherently inter-sentential nature of quantification and anaphora in a strictly lexicalized and compositional manner. The present study shows that the proposed translation method successfully combines robustness and descriptive adequacy of contemporary semantics. ...
We deﬁne noun phrase translation as a subtask of machine translation. This enables us to build a dedicated noun phrase translation subsystem that improves over the currently best general statistical machine translation methods by incorporating special modeling and special features. We achieved 65.5% translation accuracy in a German-English translation task vs. 53.2% with IBM Model 4.
Real-time electronic business transaction system and method for reporting stfc/fct data to
Title: Real-time electronic business transaction system and method for reporting stfc/fct data to
This thesis was done with a view to find out students‟ difficulties in learning written translation in order to orient students who begin to study this subject in good manner of study. the study also aims at understanding the students‟ expectation in learning the subject and then suggesting some possible solutions to overcome difficulties as well as satisfy their expectations to improve and adjust both learning‟s style and teaching method.
We decided to write this ebook in response to the many positive feedbacks we received from freelance translators. They told us we made their business so simple yet so different. They said that after implementing our methods, they started enjoying their working hours while doubling their output. We believe you are already the best at what you do - you have all the skills of translation. Instead, we are going to show you how to make the best of your translation skills. Be forewarned – at www.Tomedes.com, we think differently; some of the translators defined it as thinking outside...
Decoding algorithm is a crucial part in statistical machine translation. We describe a stack decoding algorithm in this paper. We present the hypothesis scoring method and the heuristics used in our algorithm. We report several techniques deployed to improve the performance of the decoder. We also introduce a simplified model to moderate the sparse data problem and to speed up the decoding process. We evaluate and compare these techniques/models in our statistical machine translation system.
State-of-the-art statistical machine translation (MT) systems have made signiﬁcant progress towards producing user-acceptable translation output. However, there is still no efﬁcient way for MT systems to inform users which words are likely translated correctly and how conﬁdent it is about the whole sentence. We propose a novel framework to predict wordlevel and sentence-level MT errors with a large number of novel features. Experimental results show that the MT error prediction accuracy is increased from 69.1 to 72.2 in F-score. ...
A topic model outputs a set of multinomial distributions over words for each topic. In this paper, we investigate the value of bilingual topic models, i.e., a bilingual Latent Dirichlet Allocation model for ﬁnding translations of terms in comparable corpora without using any linguistic resources. Experiments on a document-aligned English-Italian Wikipedia corpus conﬁrm that the developed methods which only use knowledge from word-topic distributions outperform methods based on similarity measures in the original word-document space.
This book addresses state-of-the-art systems and achievements in various topics in the research field of speech and language technologies. Book chapters are organized in different sections covering diverse problems, which have to be solved in speech recognition and language understanding systems. In the first section machine translation systems based on large parallel corpora using rule-based and statistical-based translation methods are presented.
We present a novel method to improve word alignment quality and eventually the translation performance by producing and combining complementary word alignments for low-resource languages. Instead of focusing on the improvement of a single set of word alignments, we generate multiple sets of diversiﬁed alignments based on different motivations, such as linguistic knowledge, morphology and heuristics.
We investigate authorship attribution using classiﬁers based on frame semantics. The purpose is to discover whether adding semantic information to lexical and syntactic methods for authorship attribution will improve them, speciﬁcally to address the difﬁcult problem of authorship attribution of translated texts.
We present B LAST, an open source tool for error analysis of machine translation (MT) output. We believe that error analysis, i.e., to identify and classify MT errors, should be an integral part of MT development, since it gives a qualitative view, which is not obtained by standard evaluation methods. B LAST can aid MT researchers and users in this process, by providing an easy-to-use graphical user interface.
This paper revisits the pivot language approach for machine translation. First, we investigate three different methods for pivot translation. Then we employ a hybrid method combining RBMT and SMT systems to ﬁll up the data gap for pivot translation, where the sourcepivot and pivot-target corpora are independent. Experimental results on spoken language translation show that this hybrid method signiﬁcantly improves the translation quality, which outperforms the method using a source-target corpus of the same size. ...
This paper addresses the task of handling unknown terms in SMT. We propose using source-language monolingual models and resources to paraphrase the source text prior to translation. We further present a conceptual extension to prior work by allowing translations of entailed texts rather than paraphrases only. A method for performing this process efﬁciently is presented and applied to some 2500 sentences with unknown terms. Our experiments show that the proposed approach substantially increases the number of properly translated texts. ...
Recently confusion network decoding shows the best performance in combining outputs from multiple machine translation (MT) systems. However, overcoming different word orders presented in multiple MT systems during hypothesis alignment still remains the biggest challenge to confusion network-based MT system combination. In this paper, we compare four commonly used word alignment methods, namely GIZA++, TER, CLA and IHMM, for hypothesis alignment.
This paper presents a partial matching strategy for phrase-based statistical machine translation (PBSMT). Source phrases which do not appear in the training corpus can be translated by word substitution according to partially matched phrases. The advantage of this method is that it can alleviate the data sparseness problem if the amount of bilingual corpus is limited.
Inspired by previous preprocessing approaches to SMT, this paper proposes a novel, probabilistic approach to reordering which combines the merits of syntax and phrase-based SMT. Given a source sentence and its parse tree, our method generates, by tree operations, an n-best list of reordered inputs, which are then fed to standard phrase-based decoder to produce the optimal translation. Experiments show that, for the NIST MT-05 task of Chinese-toEnglish translation, the proposal leads to BLEU improvement of 1.56%. ...
It is important to correct the errors in the results of speech recognition to increase the performance of a speech translation system. This paper proposes a method for correcting errors using the statistical features of character co-occurrence, and evaluates the method. The proposed method comprises two successive correcting processes. The first process uses pairs of strings: the first string is an erroneous substring of the utterance predicted by speech recognition, the second string is the corresponding section of the actual utterance.