Search Results for author: Ekaterina Lapshinova-Koltunski

Found 33 papers, 1 papers with code

EPIC UdS - Creation and Applications of a Simultaneous Interpreting Corpus

no code implementations LREC 2022 Heike Przybyl, Ekaterina Lapshinova-Koltunski, Katrin Menzel, Stefan Fischer, Elke Teich

In this paper, we describe the creation and annotation of EPIC UdS, a multilingual corpus of simultaneous interpreting for English, German and Spanish.

ParCorFull2.0: a Parallel Corpus Annotated with Full Coreference

no code implementations LREC 2022 Ekaterina Lapshinova-Koltunski, Pedro Augusto Ferreira, Elina Lartaud, Christian Hardmeier

Similar to the previous version, this corpus has been created to address translation of coreference across languages, a phenomenon still challenging for machine translation (MT) and other multilingual natural language processing (NLP) applications.

coreference-resolution Coreference Resolution +2

DiHuTra: a Parallel Corpus to Analyse Differences between Human Translations

1 code implementation LREC 2022 Ekaterina Lapshinova-Koltunski, Maja Popović, Maarit Koponen

The resulting corpus consists of English news and reviews source texts, their translations into Russian and Croatian, and translations of the reviews into Finnish.

Machine Translation Translation

Translationese in Russian Literary Texts

no code implementations EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 Maria Kunilovskaya, Ekaterina Lapshinova-Koltunski, Ruslan Mitkov

We expect that literary translations from typologically distant languages should exhibit more translationese, and the fingerprints of individual source languages (and their families) are traceable in translations.


Coreference Strategies in English-German Translation

no code implementations COLING (CRAC) 2020 Ekaterina Lapshinova-Koltunski, Marie-Pauline Krielke, Christian Hardmeier

We present a study focusing on variation of coreferential devices in English original TED talks and news texts and their German translations.

Machine Translation Multilingual NLP +1

Measuring Translationese across Levels of Expertise: Are Professionals more Surprising than Students?

no code implementations NoDaLiDa 2021 Yuri Bizzoni, Ekaterina Lapshinova-Koltunski

The present paper deals with a computational analysis of translationese in professional and student English-to-German translations belonging to different registers.

Language Modelling Machine Translation +2

Lexicogrammatic translationese across two targets and competence levels

no code implementations LREC 2020 Maria Kunilovskaya, Ekaterina Lapshinova-Koltunski

This research employs genre-comparable data from a number of parallel and comparable corpora to explore the specificity of translations from English into German and Russian produced by students and professional translators.

Specificity Translation

Analysing Coreference in Transformer Outputs

no code implementations WS 2019 Ekaterina Lapshinova-Koltunski, Cristina España-Bonet, Josef van Genabith

We analyse coreference phenomena in three neural machine translation systems trained with different data settings with or without access to explicit intra- and cross-sentential anaphoric information.

Machine Translation Translation

Translationese Features as Indicators of Quality in English-Russian Human Translation

no code implementations RANLP 2019 Maria Kunilovskaya, Ekaterina Lapshinova-Koltunski

We use a range of morpho-syntactic features inspired by research in register studies (e. g. Biber, 1995; Neumann, 2013) and translation studies (e. g. Ilisei et al., 2010; Zanettin, 2013; Kunilovskaya and Kutuzov, 2018) to reveal the association between translationese and human translation quality.

Association Translation

Cross-lingual Incongruences in the Annotation of Coreference

no code implementations WS 2019 Ekaterina Lapshinova-Koltunski, Sharid Lo{\'a}iciga, Christian Hardmeier, Pauline Krielke

In the present paper, we deal with incongruences in English-German multilingual coreference annotation and present automated methods to discover them.

coreference-resolution Coreference Resolution +1

A Pronoun Test Suite Evaluation of the English--German MT Systems at WMT 2018

no code implementations WS 2018 Liane Guillou, Christian Hardmeier, Ekaterina Lapshinova-Koltunski, Sharid Lo{\'a}iciga

We evaluate the output of 16 English-to-German MT systems with respect to the translation of pronouns in the context of the WMT 2018 competition.

Machine Translation NMT +1

Discovery of Discourse-Related Language Contrasts through Alignment Discrepancies in English-German Translation

no code implementations WS 2017 Ekaterina Lapshinova-Koltunski, Christian Hardmeier

In this paper, we analyse alignment discrepancies for discourse structures in English-German parallel data {--} sentence pairs, in which discourse structures in target or source texts have no alignment in the corresponding parallel sentences.

Machine Translation Translation +1

From Interoperable Annotations towards Interoperable Resources: A Multilingual Approach to the Analysis of Discourse

no code implementations LREC 2016 Ekaterina Lapshinova-Koltunski, Kerstin Anna Kunz, Anna Nedoluzhko

We use an interoperable scheme unifying discourse phenomena in both frameworks into more abstract categories and considering only those phenomena that have a direct match in German and Czech.

Machine Translation Semantic Similarity +2

Data Mining with Shallow vs. Linguistic Features to Study Diversification of Scientific Registers

no code implementations LREC 2014 Stefania Degaetano-Ortlieb, Peter Fankhauser, Hannah Kermes, Ekaterina Lapshinova-Koltunski, Noam Ordan, Elke Teich

We present a methodology to analyze the linguistic evolution of scientific registers with data mining techniques, comparing the insights gained from shallow vs. linguistic features.

Text Categorization

Feature Discovery for Diachronic Register Analysis: a Semi-Automatic Approach

no code implementations LREC 2012 Stefania Degaetano-Ortlieb, Ekaterina Lapshinova-Koltunski, Elke Teich

In this paper, we present corpus-based procedures to semi-automatically discover features relevant for the study of recent language change in scientific registers.

Text Classification

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