Optimising the Europarl corpus for translation studies with the EuroparlExtract toolkit

Ustaszewski, Michael

doi:10.1080/0907676x.2018.1485716

Cited by 11 publications

(7 citation statements)

References 18 publications

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“…EuroParl is a parallel corpus with translations provided by professional human translators and is extracted from the European Parliament website by Koehn ( 2005 ). This corpus was chosen in light of its “(…) free availability, size, linguistic diversity, data authenticity, and sentence-aligned architecture as well as homogeneity in terms of register, text type, and subject domain (…)” (Ustaszewski, 2019 : 107), all of which make it ideal for translation-oriented corpus-based inquiries, moreover, if applied as a data-driven approach that serves to characterize mental or sociocultural aspects of interlingual phenomena.…”

Section: Methodsmentioning

confidence: 99%

Embodied Metarepresentations

et al. 2022

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Meaning has been established pervasively as a central concept throughout disciplines that were involved in cognitive revolution. Its metaphoric usage comes to be, first and foremost, through the interpreter's constraint: representational relationships and contents are considered to be in the “eye” or mind of the observer and shared properties among observers themselves are knowable through interlinguistic phenomena, such as translation. Despite the instability of meaning in relation to its underdetermination by reference, it can be a tertium comparationis or “third comparator” for extended human cognition if gauged through invariants that exist in transfer processes such as translation, as all languages and cultures are rooted in pan-human experience and, thus, share and express species-specific ontology. Meaning, seen as a cognitive competence, does not stop outside of the body but extends, depends, and partners with other agents and the environment. A novel approach for exploring the transfer properties of some constituent items of the original natural semantic metalanguage in English, that is, semantic primitives, is presented: FrameNet's semantic frames, evoked by the primes SEE and FEEL, were extracted from EuroParl, a parallel corpus that allows for the automatic word alignment of items with their synonyms. Large Ontology Multilingual Extraction was used. Afterward, following the Semantic Mirrors Method, a procedure that consists back-translating into source language, a translatological examination of translated and original versions of items was performed. A fully automated pipeline was designed and tested, with the purpose of exploring associated frame shifts and, thus, beginning a research agenda on their alleged universality as linguistic features of translation, which will be complemented with and contrasted against further massive feedback through a citizen science approach, as well as cognitive and neurophysiological examinations. Additionally, an embodied account of frame semantics is proposed.

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Section: Methodsmentioning

confidence: 99%

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et al. 2022

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“…The corpus for each language is a Wikipedia dump from 27 July 2020, cleaned using tools from Bojanowski et al (2017), and tokenized using Eu-ropalExtract (Ustaszewski, 2019), except for Bengali and Hindi, which are tokenized using NLTK (Bird et al, 2009). Because DUONG2016 and HAKIMI2020 can learn high quality cross-lingual embeddings from monolingual corpora of only 5M sentences each, we down-sample the English corpus for these two methods to 5M sentences.…”

Section: Training Corpora and Dictionariesmentioning

confidence: 99%

Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages

Parizi¹,

Cook²

2021

Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

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Cross-lingual word embeddings provide a way for information to be transferred between languages. In this paper we evaluate an extension of a joint training approach to learning cross-lingual embeddings that incorporates sub-word information during training. This method could be particularly well-suited to lower-resource and morphologically-rich languages because it can be trained on modest size monolingual corpora, and is able to represent out-of-vocabulary words (OOVs). We consider bilingual lexicon induction, including an evaluation focused on OOVs. We find that this method achieves improvements over previous approaches, particularly for OOVs.

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“…The corpus for each language is a Wikipedia dump from 27 July 2020, cleaned using tools from , and tokenized using Eu-ropalExtract (Ustaszewski, 2019), except for Bengali and Hindi, which are tokenized using NLTK . Because DUONG2016 and HAKIMI2020 can learn high quality cross-lingual embeddings from monolingual corpora of only 5M sentences each, we down-sample the English corpus for these two methods to 5M sentences.…”

Section: Training Corpora and Dictionariesmentioning

confidence: 99%

Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

2021

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Recently, natural language processing (NLP) has had increasing success and produced extensive industrial applications. Despite being sufficient to enable these applications, current NLP systems often ignore the social part of language, e.g., who says it, in what context, for what goals. In this talk, we take a closer look at social factors in language via a new theory taxonomy, and its interplay with computational methods via two lines of work. The first one studies what makes language persuasive by introducing a semi-supervised method to leverage hierarchical structures in text to recognize persuasion strategies in good-faith requests. The second part demonstrates how various structures in conversations can be utilized to generate better summaries for everyday interaction. We conclude by discussing several open-ended questions towards how to build socially aware language technologies, with the hope of getting closer to the goal of human-like language understanding.

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Optimising the Europarl corpus for translation studies with the EuroparlExtract toolkit

Cited by 11 publications

References 18 publications

Embodied Metarepresentations

Embodied Metarepresentations

Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages

Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

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