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.
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.
1 code implementation • 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.
no code implementations • EMNLP (CODI) 2020 • Ekaterina Lapshinova-Koltunski, Kerstin Kunz
The present paper focuses on variation phenomena in coreference chains.
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.
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.
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.
no code implementations • COLING (CODI, CRAC) 2022 • Ekaterina Lapshinova-Koltunski, Michael Carl
We look into English-German translation process data to analyse explicitation and implicitation phenomena of discourse connectives.
no code implementations • CODI 2021 • Ekaterina Lapshinova-Koltunski, Heike Przybyl, Yuri Bizzoni
In the present paper, we explore lexical contexts of discourse markers in translation and interpreting on the basis of word embeddings.
no code implementations • RANLP 2021 • Maria Kunilovskaya, Ekaterina Lapshinova-Koltunski, Ruslan Mitkov
The texts are represented with frequency-based features that capture structural and lexical properties of language.
1 code implementation • 22 Aug 2023 • Silvana Deilen, Sergio Hernández Garrido, Ekaterina Lapshinova-Koltunski, Christiane Maaß
This study sets out to investigate the feasibility of using ChatGPT to translate citizen-oriented administrative texts into German Easy Language, a simplified, controlled language variety that is adapted to the needs of people with reading impairments.
no code implementations • 10 Jul 2020 • Anita Ramm, Ekaterina Lapshinova-Koltunski, Alexander Fraser
Grammatical tense and mood are important linguistic phenomena to consider in natural language processing (NLP) research.
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.
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.
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.
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.
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.
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.
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.
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.
no code implementations • LREC 2012 • Marilisa Amoia, Kerstin Kunz, Ekaterina Lapshinova-Koltunski
This paper describes an empirical study of coreference in spoken vs. written text.
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.