Articles

We address the problem of mistranslated predicate-argument structures in syntax- based machine translation. This paper ex- plores whether knowledge about semantic affinities between the target predicates and their argument fillers is useful for translat- ing ambiguous predicates and arguments. We propose a selectional preference fea- ture based on the selectional …...

German verbal inflection is frequently wrong in standard statistical machine translation approaches. German verbs agree with subjects in person and num- ber, and they bear information about mood and tense. For subject–verb agreement, we parse German MT output to iden- tify subject–verb pairs and ensure that the verb agrees with …...

Neural network language and translation models have recently shown their great po- tentials in improving the performance of phrase-based machine translation. At the same time, word representations using dif- ferent word factors have been translation quality and are part of many state-of-the- art machine translation systems. used in many state-of-the-art …...

Information about the antecedents of pro- nouns is considered essential to solve cer- tain translation divergencies, such as those concerning the English pronoun it when translated into gendered languages, e.g. for French into il, elle, or several other options. However, no machine translation system using anaphora resolution has so far …...

We present a simple neural network for word alignment that builds source and tar- get word window representations to com- pute alignment scores for sentence pairs. To enable unsupervised training, we use an aggregation operation that summarizes the alignment scores for a given target word. A soft-margin objective increases scores …...

Tree-to-tree machine translation (MT) that utilizes syntactic parse trees on both source and target sides suffers from the non-isomorphism of the parse trees due to parsing errors and the difference of an- notation criterion between the two lan- guages. In this paper, we present a method that projects dependency parse …...

In this paper, we capture the argument rela- tionships for Chinese semantic role labeling task, and improve the task’s performance with the help of argument relationships. We split the relationship between two candidate argu- ments into two categories: (1) Compatible ar- guments: if one candidate argument belongs to a given …...

We present the Stanford Question Answer- ing Dataset (SQuAD), a new reading compre- hension dataset consisting of 100,000+ ques- tions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the cor- responding reading passage. We analyze the dataset …...

AbstractMost diacritics in Arabic represent short vowels. In Arabic orthography, such diacritics are considered optional. The absence of these diacritics naturally leads to significant word ambiguity to top the inherent ambiguity present in fully diacritized words. Word ambiguity is a significant impediment for machine translation. Despite the ambiguity presented by …...

AbstractStatistical Machine Translation (SMT) of highly inflected, low-resource languages suffers from the problem of low bitext availability, which is exacerbated by large inflectional paradigms. When translating into English, rich source inflections have a high chance of being poorly estimated or out-of-vocabulary (OOV). We present a source language-agnostic system for automatically …...

AbstractIn the recent years interest in Deep Neural Networks (DNN) has grown in the field of Natural Language Processing, as new training methods have been proposed. The usage of DNN has achieved state-of-the-art performance in various areas. Neural Machine Translation (NMT) described by Bahdanau et al. (2014) and its successive …...

AbstractIn this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models. We show that our novel guided alignment training approach improves translation quality on real-life e-commerce texts consisting of product titles and descriptions, …...

AbstractWe present an interactive translation prediction method based on neural machine translation. Even with the same translation quality of the underlying machine translation systems, the neural prediction method yields much higher word prediction accuracy (61.6% vs. 43.3%) than the traditional method based on search graphs, mainly due to better recovery …...

AbstractDomain adaptation is a major challenge when applying machine translation to practical tasks. In this paper, we present domain adaptation methods for machine translation that assume multiple domains. The proposed methods combine two model types: a corpus-concatenated model covering multiple domains and single-domain models that are accurate but sparse in …...

AbstractThe objective of interactive translation prediction (ITP), a paradigm of computer-aided translation, is to assist professional translators by offering context-based computer-generated suggestions as they type. While most state-of-the-art ITP systems are tightly coupled to a machine translation (MT) system (often created ad-hoc for this purpose), our proposal follows a resourceagnostic …...

AbstractLexicalized and hierarchical reordering models use relative frequencies of fully lexicalized phrase pairs to learn phrase reordering distributions. This results in unreliable estimation for infrequent phrase pairs which also tend to be longer phrases. There are some smoothing techniques used to smooth the distributions in these models. But these techniques …...

AbstractComputer-aided translation (CAT) tools often use a translation memory (TM) as the key resource to assist translators. A TM contains translation units (TU) which are made up of source and target language segments; translators use the target segments in the TU suggested by the CAT tool by converting them into …...

AbstractThe utilization of statistical machine translation (SMT) has grown enormously over the last decade, many using open-source software developed by the NLP community. As commercial use has increased, there is need for software that is optimized for commercial requirements, in particular, fast phrase-based decoding and more efficient utilization of modern …...

AbstractWe assessed how different machine translation (MT) systems affect the post-editing (PE) process and product of professional English–Spanish translators. Our model found that for each 1-point increase in BLEU, there is a PE time decrease of 0.16 seconds per word, about 3-4%. The MT system with the lowest BLEU score …...

AbstractIn this paper, we propose a new data selection method which uses semi-supervised convolutional neural networks based on bitokens (Bi-SSCNNs) for training machine translation systems from a large bilingual corpus. In earlier work, we devised a data selection method based on semi-supervised convolutional neural networks (SSCNNs). The new method, Bi-SSCNN, …...