SwePub - search: WFRF:(Tiedemann Jörg)

Enumeration	Reference	Cover	Find
1.	Agić, Zeljko, et al. (author) Cross-lingual Dependency Parsing of Related Languages with Rich Morphosyntactic Tagsets 2014 In: Proceedings of the EMNLP’2014 Workshop on Language Technology for Closely Related Languages and Language Variants. ; , s. 13-24 Conference paper (peer-reviewed)
2.	Ahrenberg, Lars and Merkel, Magnus and Ridings, Daniel and Sågvall Hein, Anna and Tiedemann, Jörg (author) Automatic processing of parallel corpora: A Swedish perspective. 1999 Reports (other academic/artistic)abstract As empirical methods have come to the fore in language technology and translation studies, the processing of parallel texts and parallel corpora have become a major issue. In this article we review the state of the art in alignment and data extraction tec
3.	Ahrenberg, Lars, 1948-, et al. (author) Automatic Processing of Parallel Corpora: A Swedish Perspective 1999 Reports (other academic/artistic)abstract As empirical methods have come to the fore in multilingual language technology and translation studies, the processing of parallel texts and parallel corpora have become a major research area in computational linguistics. In this article we review the state of the art in alignment and data extraction techniques for parallel texts, and give an overview of current work in Sweden in this area. In a final section, we summarize the results achieved so far and make some proposals for future research.
4.	Ahrenberg, Lars, et al. (author) Evaluation of LWA and UWA 1999 Reports (other academic/artistic)
5.	Ahrenberg, Lars, 1948-, et al. (author) Evaluation of word alignment systems 2000 In: Proceedings of the Second International Conference on Linguistic Resources and Evaluation (LREC-2000). - Paris, France : European Language Resources Association (ELRA). ; , s. 1255-1261 Conference paper (peer-reviewed)
6.	Bertels, Ann, et al. (author) Corpus parallèles et corpus ciblés au secours du dictionnaire de traduction 2009 In: Cahiers de lexicologie. - : Classiques Garnier. - 9782812400858 ; , s. 199-219 Book chapter (other academic/artistic)
7.	Bjerva, Johannes, et al. (author) What Do Language Representations Really Represent? 2019 In: Computational linguistics - Association for Computational Linguistics (Print). - : MIT Press - Journals. - 0891-2017 .- 1530-9312. ; 45:2, s. 381-389 Journal article (other academic/artistic)abstract A neural language model trained on a text corpus can be used to induce distributed representations of words, such that similar words end up with similar representations. If the corpus is multilingual, the same model can be used to learn distributed representations of languages, such that similar languages end up with similar representations. We show that this holds even when the multilingual corpus has been translated into English, by picking up the faint signal left by the source languages. However, just as it is a thorny problem to separate semantic from syntactic similarity in word representations, it is not obvious what type of similarity is captured by language representations. We investigate correlations and causal relationships between language representations learned from translations on one hand, and genetic, geographical, and several levels of structural similarity between languages on the other. Of these, structural similarity is found to correlate most strongly with language representation similarity, whereas genetic relationships—a convenient benchmark used for evaluation in previous work—appears to be a confounding factor. Apart from implications about translation effects, we see this more generally as a case where NLP and linguistic typology can interact and benefit one another.
8.	Bouma, Gosse, et al. (author) Linguistic Knowledge and Question Answering 2005 In: Traitement Automatique des Langues (TAL). - 1248-9433. ; 46, s. 15-39 Journal article (peer-reviewed)
9.	Bouma, Gosse, et al. (author) Question Answering for Dutch using Dependency Relations 2005 In: Working Notes of the 6th Workshop of the Cross-Language Evaluation Forum (CLEF 2005). Conference paper (other academic/artistic)
10.	Bouma, Gosse, et al. (author) Question Answering for Dutch using Dependency Relations 2006 In: Accessing Multilingual Information Repositories. - : Springer Berlin/Heidelberg. ; , s. 370-379 Book chapter (peer-reviewed)
11.	Bouma, Gosse, et al. (author) Question Answering with Joost at CLEF 2007 2007 In: Working Notes of the 8th Workshop of the Cross-Language Evaluation Forum (CLEF 2007). Conference paper (other academic/artistic)
12.	Bouma, Gosse, et al. (author) Question Answering with Joost at CLEF 2007 2008 In: Lecture Notes in Computer Science. - : Springer Berlin/Heidelberg. - 9783540857594 ; , s. 257-260 Book chapter (other academic/artistic)
13.	Bouma, Gosse, et al. (author) Question Answering with Joost at CLEF 2008 2008 In: Working Notes of the 9th Workshop of the Cross-Language Evaluation Forum (CLEF 2008). Conference paper (other academic/artistic)
14.	Bouma, Gosse, et al. (author) The University of Groningen at QA@CLEF 2006 : Using Syntactic Knowledge for QA 2006 In: Working Notes of the 7th Workshop of the Cross-Language Evaluation Forum (CLEF 2006). Conference paper (other academic/artistic)
15.	Bouma, Gosse, et al. (author) Using Syntactic Knowledge for QA 2007 In: Evaluation of Multilingual and Multi-modal Information Retrieval. - : Springer Berlin/Heidelberg. ; , s. 318-327 Book chapter (peer-reviewed)
16.	Callin, Jimmy, et al. (author) Part-of-Speech Driven Cross-Lingual Pronoun Prediction with Feed-Forward Neural Networks 2015 In: Proceedings of the Second Workshop on Discourse in Machine Translation (DiscoMT). - Stroudsburg, PA : Association for Computational Linguistics. - 9781941643327 ; , s. 59-64 Conference paper (peer-reviewed)abstract For some language pairs, pronoun translation is a discourse-driven task which requires information that lies beyond its local context. This motivates the task of predicting the correct pronoun given a source sentence and a target translation, where the translated pronouns have been replaced with placeholders. For cross-lingual pronoun prediction, we suggest a neural network-based model using preceding nouns and determiners as features for suggesting antecedent candidates. Our model scores on par with similar models while having a simpler architecture.
17.	DL4NLP 2019. Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing 2019 Editorial collection (peer-reviewed)
18.	Ehrentraut, Claudia, et al. (author) Detecting hospital-acquired infections : A document classification approach using support vector machines and gradient tree boosting 2018 In: Health Informatics Journal. - : SAGE Publications. - 1460-4582 .- 1741-2811. ; 24:1, s. 24-42 Journal article (peer-reviewed)abstract Hospital-acquired infections pose a significant risk to patient health, while their surveillance is an additional workload for hospital staff. Our overall aim is to build a surveillance system that reliably detects all patient records that potentially include hospital-acquired infections. This is to reduce the burden of having the hospital staff manually check patient records. This study focuses on the application of text classification using support vector machines and gradient tree boosting to the problem. Support vector machines and gradient tree boosting have never been applied to the problem of detecting hospital-acquired infections in Swedish patient records, and according to our experiments, they lead to encouraging results. The best result is yielded by gradient tree boosting, at 93.7percent recall, 79.7percent precision and 85.7percent F1 score when using stemming. We can show that simple preprocessing techniques and parameter tuning can lead to high recall (which we aim for in screening patient records) with appropriate precision for this task.
19.	Ehrentraut, Claudia, et al. (author) Detection of Hospital Acquired Infections in Sparse and Noisy Swedish Patient Records 2012 In: Proceedings of the Sixth Workshop on Analytics for Noisy Unstructured Text Data (AND 2012). Conference paper (peer-reviewed)
20.	Guillou, Liane, et al. (author) Findings of the 2016 WMT Shared Taskon Cross-lingual Pronoun Prediction 2016 In: Proceedings of the First Conference on Machine Translation. ; , s. 525-542 Conference paper (other academic/artistic)abstract We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction. This is a classification task in which participants are asked to provide predictions on what pronoun class label should replace a placeholder value in the target-language text, provided in lemmatised and PoS-tagged form. We provided four subtasks, for the English–French and English–German language pairs, in both directions. Eleven teams participated in the shared task; nine for the English–French subtask, five for French–English, nine for English–German, and six for German–English. Most of the submissions outperformed two strong language-model-based baseline systems, with systems using deep recurrent neural networks outperforming those using other architectures for most language pairs.
21.	Guillou, Liane, et al. (author) ParCor 1.0 : A Parallel Pronoun-Coreference Corpus to Support Statistical MT 2014 In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14). - Paris : European Language Resources Association. - 9782951740884 ; , s. 3191-3198 Conference paper (peer-reviewed)abstract We present ParCor, a parallel corpus of texts in which pronoun coreference – reduced coreference in which pronouns are used as referringexpressions – has been annotated. The corpus is intended to be used both as a resource from which to learn systematic differences inpronoun use between languages and ultimately for developing and testing informed Statistical Machine Translation systems aimed ataddressing the problem of pronoun coreference in translation. At present, the corpus consists of a collection of parallel English-Germandocuments from two different text genres: TED Talks (transcribed planned speech), and EU Bookshop publications (written text). Alldocuments in the corpus have been manually annotated with respect to the type and location of each pronoun and, where relevant, itsantecedent. We provide details of the texts that we selected, the guidelines and tools used to support annotation and some corpus statistics.The texts in the corpus have already been translated into many languages, and we plan to expand the corpus into these other languages, aswell as other genres, in the future.
22.	Hardmeier, Christian, et al. (author) Anaphora Models and Reordering for Phrase-Based SMT 2014 In: Proceedings of the Ninth Workshop on Statistical Machine Translation. - : Association for Computational Linguistics. - 9781941643174 ; , s. 122-129 Conference paper (peer-reviewed)abstract We describe the Uppsala University systems for WMT14. We look at the integration of a model for translating pronominal anaphora and a syntactic dependency projection model for English–French. Furthermore, we investigate post-ordering and tunable POS distortion models for English–German.
23.	Hardmeier, Christian (author) Discourse in Statistical Machine Translation 2014 Doctoral thesis (other academic/artistic)abstract This thesis addresses the technical and linguistic aspects of discourse-level processing in phrase-based statistical machine translation (SMT). Connected texts can have complex text-level linguistic dependencies across sentences that must be preserved in translation. However, the models and algorithms of SMT are pervaded by locality assumptions. In a standard SMT setup, no model has more complex dependencies than an n-gram model. The popular stack decoding algorithm exploits this fact to implement efficient search with a dynamic programming technique. This is a serious technical obstacle to discourse-level modelling in SMT.From a technical viewpoint, the main contribution of our work is the development of a document-level decoder based on stochastic local search that translates a complete document as a single unit. The decoder starts with an initial translation of the document, created randomly or by running a stack decoder, and refines it with a sequence of elementary operations. After each step, the current translation is scored by a set of feature models with access to the full document context and its translation. We demonstrate the viability of this decoding approach for different document-level models.From a linguistic viewpoint, we focus on the problem of translating pronominal anaphora. After investigating the properties and challenges of the pronoun translation task both theoretically and by studying corpus data, a neural network model for cross-lingual pronoun prediction is presented. This network jointly performs anaphora resolution and pronoun prediction and is trained on bilingual corpus data only, with no need for manual coreference annotations. The network is then integrated as a feature model in the document-level SMT decoder and tested in an English–French SMT system. We show that the pronoun prediction network model more adequately represents discourse-level dependencies for less frequent pronouns than a simpler maximum entropy baseline with separate coreference resolution.By creating a framework for experimenting with discourse-level features in SMT, this work contributes to a long-term perspective that strives for more thorough modelling of complex linguistic phenomena in translation. Our results on pronoun translation shed new light on a challenging, but essential problem in machine translation that is as yet unsolved.
24.	Hardmeier, Christian, et al. (author) Docent : A Document-Level Decoder for Phrase-Based Statistical Machine Translation 2013 In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. - : Association for Computational Linguistics. ; , s. 193-198 Conference paper (peer-reviewed)abstract We describe Docent, an open-source decoder for statistical machine translation that breaks with the usual sentence-by-sentence paradigm and translates complete documents as units. By taking translation to the document level, our decoder can handle feature models with arbitrary discourse-wide dependencies and constitutes an essential infrastructure component in the quest for discourse-aware SMT models.
25.	Hardmeier, Christian, et al. (author) Document-Wide Decoding for Phrase-Based Statistical Machine Translation 2012 In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. - : Association for Computational Linguistics. ; , s. 1179-1190 Conference paper (peer-reviewed)abstract Independence between sentences is an assumption deeply entrenched in the models and algorithms used for statistical machine translation (SMT), particularly in the popular dynamic programming beam search decoding algorithm. This restriction is an obstacle to research on more sophisticated discourse-level models for SMT. We propose a stochastic local search decoding method for phrase-based SMT, which permits free document-wide dependencies in the models. We explore the stability and the search parameters of this method and demonstrate that it can be successfully used to optimise a document-level semantic language model.
26.	Hardmeier, Christian, et al. (author) Latent Anaphora Resolution for Cross-Lingual Pronoun Prediction 2013 In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. - : Association for Computational Linguistics. ; , s. 380-391 Conference paper (peer-reviewed)abstract This paper addresses the task of predicting the correct French translations of third-person subject pronouns in English discourse, a problem that is relevant as a prerequisite for machine translation and that requires anaphora resolution. We present an approach based on neural networks that models anaphoric links as latent variables and show that its performance is competitive with that of a system with separate anaphora resolution while not requiring any coreference-annotated training data. This demonstrates that the information contained in parallel bitexts can successfully be used to acquire knowledge about pronominal anaphora in an unsupervised way.
27.	Hardmeier, Christian, et al. (author) Pronoun-Focused MT and Cross-Lingual Pronoun Prediction: Findings of the 2015 DiscoMT Shared Task on Pronoun Translation 2015 In: Proceedings of the Second Workshop on Discourse in Machine Translation (DiscoMT). - Stroudsburg, PA : Association for Computational Linguistics. - 9781941643327 ; , s. 1-16 Conference paper (other academic/artistic)abstract We describe the design, the evaluation setup, and the results of the DiscoMT 2015 shared task, which included two subtasks, relevant to both the machine translation (MT) and the discourse communities: (i) pronoun-focused translation, a practical MT task, and (ii) cross-lingual pronoun prediction, a classification task that requires no specific MT expertise and is interesting as a machine learning task in its own right. We focused on the English–French language pair, for which MT output is generally of high quality, but has visible issues with pronoun translation due to differences in the pronoun systems of the two languages. Six groups participated in the pronoun-focused translation task and eight groups in the cross-lingual pronoun prediction task.
28.	Hardmeier, Christian, et al. (author) The Uppsala-FBK systems at WMT 2011 2011 In: Proceedings of the Sixth (6th) Workshop on Statistical Machine Translation. - : Association for Computational Linguistics. - 9781937284121 ; , s. 372-378 Conference paper (peer-reviewed)abstract This paper presents our submissions to the shared translation task at WMT 2011. We created two largely independent systems for English-to-French and Haitian Creole-to-English translation to evaluate different features and components from our ongoing research on these language pairs. Key features of our systems include anaphora resolution, hierarchical lexical reordering, data selection for language modelling, linear transduction grammars for word alignment and syntax-based decoding with monolingual dependency information.
29.	Hardmeier, Christian, et al. (author) Translating Pronouns with Latent Anaphora Resolution 2014 Conference paper (other academic/artistic)abstract We discuss the translation of anaphoric pronouns in statistical machine translation from English into French. Pronoun translation requires resolving the antecedents of the pronouns in the input, a classic discourse processing problem that is usually approached through supervised learning from manually annotated data. We cast cross-lingual pronoun prediction as a classification task and present a neural network architecture that incorporates the links between anaphors and potential antecedents as latent variables, allowing us to train the classifier on parallel text without explicit supervision for the anaphora resolver. We demonstrate that our approach works just as well for classification as using an external coreference resolver whereas its impact in a practical translation experiment is more limited.
30.	Hardmeier, Christian, et al. (author) Tree Kernels for Machine Translation Quality Estimation 2012 In: Proceedings of the 7th Workshop on Statistical Machine Translation. - : Association for Computational Linguistics. - 9781937284206 - 1937284204 ; , s. 109-113 Conference paper (peer-reviewed)abstract This paper describes Uppsala University’s submissions to the Quality Estimation (QE) shared task at WMT 2012. We present a QE system based on Support Vector Machine regression, using a number of explicitly defined features extracted from the Machine Translation input, output and models in combination with tree kernels over constituency and dependency parse trees for the input and output sentences. We confirm earlier results suggesting that tree kernels can be a useful tool for QE system construction especially in the early stages of system design.
31.	Holmqvist, Maria, 1979- (author) Word Alignment by Re-using Parallel Phrases 2008 Licentiate thesis (other academic/artistic)abstract In this thesis we present the idea of using parallel phrases for word alignment. Each parallel phrase is extracted from a set of manual word alignments and contains a number of source and target words and their corresponding alignments. If a parallel phrase matches a new sentence pair, its word alignments can be applied to the new sentence. There are several advantages of using phrases for word alignment. First, longer text segments include more context and will be more likely to produce correct word alignments than shorter segments or single words. More importantly, the use of longer phrases makesit possible to generalize words in the phrase by replacing words by parts-of-speech or other grammatical information. In this way, the number of words covered by the extracted phrases can go beyond the words and phrases that were present in the original set of manually aligned sentences. We present experiments with phrase-based word alignment on three types of English–Swedish parallel corpora: a software manual, a novel and proceedings of the European Parliament. In order to find a balance between improved coverage and high alignment accuracy we investigated different properties of generalised phrases to identify which types of phrases are likely to produce accurate alignments on new data. Finally, we have compared phrase-based word alignments to state-of-the-art statistical alignment with encouraging results. We show that phrase-based word alignments can be used to enhance statistical word alignment. To evaluate word alignments an English–Swedish reference set for the Europarl corpus was constructed. The guidelines for producing this reference alignment are presented in the thesis.
32.	Islam, Zahurul, et al. (author) English to Bangla Phrase-Based Machine Translation 2010 In: Proceedings of 14th Annual Conference of the European Association for Machine Translation (EAMT’10). - : European Association for Machine Translation (EAMT). Conference paper (peer-reviewed)
33.	Kotzé, Gideon, et al. (author) Large aligned treebanks for syntax-based machine translation 2012 In: Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12). - 9782951740877 ; , s. 467-473 Conference paper (peer-reviewed)
34.	Loáiciga, Sharid, et al. (author) Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction 2017 In: Proceedings of the Third Workshop on Discourse in Machine Translation. Conference paper (other academic/artistic)abstract We describe the design, the setup, and the evaluation results of the DiscoMT 2017 shared task on cross-lingual pronoun prediction. The task asked participants to predict a target-language pronoun given a source-language pronoun in the context of a sentence. We further provided a lemmatized target-language human-authored translation of the source sentence, and automatic word alignments between the source sentence words and the target-language lemmata. The aim of the task was to predict, for each target-language pronoun placeholder, the word that should replace it from a small, closed set of classes, using any type of information that can be extracted from the entire document.We offered four subtasks, each for a different language pair and translation direction: English-to-French, English-to-German, German-to-English, and Spanish-to-English. Five teams participated in the shared task, making submissions for all language pairs. The evaluation results show that all participating teams outperformed two strong n-gram-based language model-based baseline systems by a sizable margin.
35.	Lonneke, van der Plas, et al. (author) Automatic acquisition of synonyms for French using parallel corpora 2010 In: Proceedings of the 4th International Workshop on Distributed Agent-Based Retrieval Tools. Conference paper (peer-reviewed)abstract In this paper we describe an approach to acquire synonyms for French automatically that is easy to port across domains and across languages. The approach relies on automatic word alignments in parallel texts and uses distributional methods to compute the semantic similarity of words based on these word alignments. As a result the system outputs ranked lists of candidate synonyms for a given word. We compare the performance of the system with a system that uses syntactic contexts to acquire synonyms automatically. Evaluations are done on a large-scale French synonym dictionary. We show that the alignment-based method outperforms the syntactic method by a large margin. In addition we show that the method can easily be ported to a different language and to a different domain.
36.	Manguin, Jean-Luc, et al. (author) Le traitement automatique : un moteur pour l’évolution des dictionnaires de synonymes 2008 In: Actes du colloque ’Lexicographie et informatique: bilan et perspectives’. Conference paper (peer-reviewed)
37.	Martinez Garcia, Eva, et al. (author) Word’s Vector Representations meet Machine Translation 2014 In: Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation. ; , s. 132-134 Conference paper (peer-reviewed)
38.	Nabende, Peter, et al. (author) Pair Hidden Markov Model for Named Entity Matching 2010 In: Innovations and Advances in Computer Science and Engineering. - : Springer Berlin/Heidelberg. - 9789048136575 ; , s. 497-502 Book chapter (peer-reviewed)
39.	Nakov, Preslav, et al. (author) Combining Word-Level and Character-Level Models for Machine Translation Between Closely-Related Languages 2012 In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2. ; , s. 301-305 Conference paper (peer-reviewed)
40.	Pettersson, Eva, 1978-, et al. (author) An SMT Approach to Automatic Annotation of Historical Texts 2013 In: <em>Workshop on Computational Historical Linguistics, Nodalida</em> 2013.. Conference paper (peer-reviewed)abstract In this paper we propose an approach to tagging and parsing of historical text, using characterbasedSMT methods for translating the historical spelling to a modern spelling before applyingthe NLP tools. This way, existing modern taggers and parsers may be used to analyse historicaltext instead of training new tools specialised in historical language, which might be hardconsidering the lack of linguistically annotated historical corpora. We show that our approachto spelling normalisation is successful even with small amounts of training data, and thatit is generalisable to several languages. For the two languages presented in this paper, theproportion of tokens with a spelling identical to the modern gold standard spelling increasesfrom 64.8% to 83.9%, and from 64.6% to 92.3% respectively, which has a positive impact onsubsequent tagging and parsing using modern tools.
41.	Plas, Lonneke van der, et al. (author) Automatic extraction of medical term variants from multilingual parallel translations 2011 In: Interactive Multi-modal Question Answering. - Berlin : Springer Berlin/Heidelberg. - 9783642175244 ; , s. 149-169 Book chapter (peer-reviewed)
42.	Plas, Lonneke van der, et al. (author) Extraction de synonymes à partir d'un corpus multilingue aligné 2007 In: Actes des 5èmes journées de linguistique de corpus. Conference paper (peer-reviewed)
43.	Plas, Lonneke van der, et al. (author) Finding Synonyms Automatically in Multilingual Parallel Corpora 2008 In: Proceedings of the HERA Conference. Conference paper (peer-reviewed)
44.	Plas, Lonneke van der, et al. (author) Finding Synonyms Using Automatic Word Alignment and Measures of Distributional Similarity 2006 In: Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions. ; , s. 866-873 Conference paper (peer-reviewed)
45.	Plas, Lonneke van der, et al. (author) Synonym acquisition across domains and languages 2011 In: Advances in Distributed Agent-based Retrieval Tools. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642213830 Book chapter (peer-reviewed)
46.	Plas, Lonneke van der, et al. (author) Using lexico-semantic information for query expansion in passage retrieval for question answering 2008 In: Coling 2008. ; , s. 50-57 Conference paper (peer-reviewed)
47.	Plas, van der, Lonneke, et al. (author) Finding Medical Term Variations using Parallel Corpora and Distributional Similarity 2010 In: Proceedings of the 6thWorkshop on Ontologies and Lexical Resources (OntoLex 2010). Conference paper (peer-reviewed)abstract We describe a method for the identification of medical term variation using parallel corpora and measures of distributional similarity. Our approach is based on automatic word alignment and standard phrase extraction techniques commonly used in statistical machine translation. Combined with pattern-based filters we obtain encouraging results compared to related approaches using similar data-driven techniques.
48.	Proceedings of the Workshop on Annotation and Exploitation of Parallel Corpora, Tartu, December 2, 2010. NEALT Proceedings Series, Vol. 10. 2010 Editorial proceedings (other academic/artistic)
49.	Proceedings of the Workshop on Discourse in Machine Translation 2013 Editorial proceedings (other academic/artistic)
50.	Scherrer, Yves, et al. (author) Analysing concatenation approaches to document-level NMT in two different domains 2019 In: Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019), November 3, 2019, Hong Kong, China / Andrei Popescu-Belis, Sharid Loáiciga, Christian Hardmeier, Deyi Xiong (Editors). - Stroudsburg, PA : Association for Computational Linguistics. - 9781950737741 Conference paper (peer-reviewed)

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