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AbstractStatistical machine translation to morphologically richer languages is a challenging task and more so if the source and target languages differ in word order. Current state-of-the-art MT systems thus deliver …...

The translation model of statistical ma- chine translation systems is trained on par- allel data coming from various sources and domains. These corpora are usually con- catenated, word alignments are …...

High-quality parallel data is crucial for a range of multilingual applications, from tuning and evaluating machine translation systems to cross-lingual annotation pro- jection. Unfortunately, automatically ob- tained parallel data (which …...

In this paper we improve Urdu→Hindi⇄English machine trans- lation through triangulation and translit- eration. First we built an Urdu→Hindi SMT system by inducing triangulated and transliterated phrase-tables from Urdu–English and …...

We present a novel technique for learn- ing semantic representations, which ex- tends the distributional hypothesis to mul- tilingual data and joint-space embeddings. Our models leverage parallel data and learn …...

In this paper we address the problem of multilingual part-of-speech tagging for resource-poor languages. We use par- allel data to transfer part-of-speech in- formation from resource-rich to resource- poor languages. …...

In this paper, we address the challenge of creating accurate and robust part- of-speech taggers for low-resource lan- guages. We propose a method that lever- ages existing parallel data between …...

AbstractThis paper explores the role of tense information in Chinese causal analysis. Both tasks of causal type classification and causal directionality identification are experimented to show the significant improvement gained …...

AbstractCross-lingual sentiment classification (CLSC) seeks to use resources from a source language in order to detect sentiment and classify text in a target language. Almost all research into CLSC has …...

We focus on two leading state-of-the-art approaches to grammatical error correc- tion – machine learning classification and machine translation. Based on the com- parative study of the two learning frame- …...

Cross lingual projection of linguistic an- notation suffers from many sources of bias and noise, leading to unreliable annota- tions that cannot be used directly. In this paper, we introduce …...

AbstractThis paper presents an approach for building large monolingual corpora and, at the same time, extracting parallel data by crawling the top-level domain of a given language of interest. For …...

AbstractShared Task 1 at SemEval-2017 deals with assessing the semantic similarity between sentences, either in the same or in different languages. In our system submission, we employ multilingual word representations, …...

We describe the University of Mary- land machine translation systems submit- ted to the WMT17 German-English Ban- dit Learning Task. The task is to adapt a translation system to a …...

AbstractA prerequisite for training corpus-based machine translation (MT) systems – either Statistical MT (SMT) or Neural MT (NMT) – is the availability of high-quality parallel data. This is arguably more …...

AbstractSemantic role labeling (SRL) is an important task for understanding natural languages, where the objective is to analyse propositions expressed by the verb and to identify each word that bears …...

AbstractMachine learning has shown promise for automatic detection of Alzheimer’s disease (AD) through speech; however, efforts are hampered by a scarcity of data, especially in languages other than English. We …...

AbstractNeural machine translation models have shown to achieve high quality when trained and fed with well structured and punctuated input texts. Unfortunately, the latter condition is not met in spoken …...

AbstractText style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has …...

AbstractFor machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available. Besides the technical challenges of learning with limited …...

AbstractWe review motivations, definition, approaches, and methodology for unsupervised cross-lingual learning and call for a more rigorous position in each of them. An existing rationale for such research is based …...

AbstractLectures translation is a case of spoken language translation and there is a lack of publicly available parallel corpora for this purpose. To address this, we examine a framework for …...

AbstractConventional approaches for formality style transfer borrow models from neural machine translation, which typically requires massive parallel data for training. However, the dataset for formality style transfer is considerably smaller …...

AbstractThe main barrier to progress in the task of Formality Style Transfer is the inadequacy of training data. In this paper, we study how to augment parallel data and propose …...

AbstractAppTek and RWTH Aachen University team together to participate in the offline and simultaneous speech translation tracks of IWSLT 2020. For the offline task, we create both cascaded and end-to-end …...

AbstractThe scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages. Fortunately, some low-resource languages are linguistically related or similar to high-resource languages; …...

AbstractWe present Miðeind’s submission for the English→Icelandic and Icelandic→English subsets of the 2021 WMT news translation task. Transformer-base models are trained for translation on parallel data to generate backtranslations teratively. …...

AbstractGenerating open-domain conversational responses in the desired style usually suffers from the lack of parallel data in the style. Meanwhile, using monolingual stylistic data to increase style intensity often leads …...

AbstractWhen building machine translation systems, one often needs to make the best out of heterogeneous sets of parallel data in training, and to robustly handle inputs from unexpected domains in …...

AbstractQuality estimation (QE) of machine translation (MT) aims to evaluate the quality of machine-translated sentences without references and is important in practical applications of MT. Training QE models require massive …...

AbstractThe principal task in supervised neural machine translation (NMT) is to learn to generate target sentences conditioned on the source inputs from a set of parallel sentence pairs, and thus …...

AbstractWe tackle the task of automatically discriminating between human and machine translations. As opposed to most previous work, we perform experiments in a multilingual setting, considering multiple languages and multilingual …...