The Role of Expectedness in the Implicitation and Explicitation of Discourse Relations

Hoek, Jet; Evers-Vermeul, Jacqueline; Sanders, Ted

doi:10.18653/v1/w15-2505

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…Conversely, the types of relations that are hardly ever explicitated will also be the ones that are hardly ever implicitated in translation. The results of a small-scale parallel corpus study reported in Hoek et al (2015) are in line with these predictions.…”

Section: Implicitation and Implicitnesssupporting

confidence: 79%

Cognitive complexity and the linguistic marking of coherence relations: A parallel corpus study

Hoek

Zufferey

Evers-Vermeul

et al. 2017

Journal of Pragmatics

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Coherence relations can be made linguistically explicit by means of connectives (e.g., but, because) or cue phrases (e.g., on the other hand, which is why), but can also be left implicit and conveyed through the juxtaposition of two clauses or sentences. However, it seems that not all relations are equally easy to reconstruct when they are implicit. In this paper, we explore which features of coherence relations make them more, or less, likely to be conveyed implicitly. We adopt the assumption that expected relations are more often implicit than relations that are not expected, and propose to determine a relation's expectedness using the notion of cognitive complexity. We test our hypotheses by means of a parallel corpus study, in which we analyze the translations of explicit English coherence relations from the Europarl Direct corpus into four target languages: Dutch, German, French, and Spanish. We find that cognitive complexity indeed influences the linguistic marking of coherence relations, and that this does not vary between the languages in our corpus. In addition, we find that a relation's relational and syntactic dependency also influences its linguistic marking, but that these measures are not completely independent of relation type.

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Section: Implicitation and Implicitnesssupporting

confidence: 79%

Cognitive complexity and the linguistic marking of coherence relations: A parallel corpus study

Hoek

Zufferey

Evers-Vermeul

et al. 2017

Journal of Pragmatics

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“…Despite its utility, research on explicitation is limited: 1) current automatic metrics of translation prefer translations that precisely convey the exact literal meaning of the original source (thus penalizing the addition of "extraneous information"); 2) the lack of labeled explicitation data hampers the study of automatic generation. Existing research including Hoek et al (2015) and Lapshinova-Koltunski and Hardmeier (2017) is confined to the explicitation of connectives or relational coreferences in discourse translation and lacks systematic strategies to automatically detect or generate the explicitation.…”

Section: The Best Translations Go Beyond Literal Meaningmentioning

confidence: 99%

“…Explicitation in Contemporary Works. Many of contemporary works with the term "explicitation" focus on discourse MT (Hoek et al, 2015;Webber et al, 2015) and mainly developed by Lapshinova-Koltunski et al (2019, 2020. However, despite the broad coverage of the term, the explicitation in these studies are limited to insertion of connectives or annotations of coreference in the target side.…”

Section: Related Workmentioning

confidence: 99%

Bridging Background Knowledge Gaps in Translation with Automatic Explicitation

Han,

Boyd-Graber,

Carpuat

2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Translations help people understand content written in another language. However, even correct literal translations do not fulfill that goal when people lack the necessary background to understand them. Professional translators incorporate explicitations to explain the missing context by considering cultural differences between source and target audiences. Despite its potential to help users, NLP research on explicitation is limited because of the dearth of adequate evaluation methods. This work introduces techniques for automatically generating explicitations, motivated by WIKIEXPL 1 : a dataset that we collect from Wikipedia and annotate with human translators. The resulting explicitations are useful as they help answer questions more accurately in a multilingual question answering framework.

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“…This is similar to work by Steele (2015) who use placeholder tokens for the implicit items in the source side of the training data, and trains a binary classifier to predict whether or not to insert a marker in the TT. This notion of explicitation, and the opposite implicitation, is the subject of research by Hoek et al (2015), who find that implicitation and explicitation of discourse relations occurs frequently in human translations. There seems to be a degree to which the implicitation and explicitation of discourse relations depends on the discourse relation they signal, and the language pair in question.…”

Section: Discourse In Mtmentioning

confidence: 99%

Proceedings of the Third Workshop on Discourse in Machine Translation

2017

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We hope that workshops such as this one will continue to stimulate work on Discourse and Machine Translation, in a wide range of discourse phenomena and MT architectures.We would like to thank all the authors who submitted papers to the workshop, as well as all the members of the Program Committee who reviewed the submissions and delivered thoughtful, informative reviews. AbstractWe 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 targetlanguage 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, Englishto-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. IntroductionPronoun translation poses a problem for machine translation (MT) as pronoun systems do not map well across languages, e.g., due to differences in gender, number, case, formality, or humanness, as well as because of language-specific restrictions about where pronouns may be used. For example, when translating the English it into French an MT system needs to choose between il, elle, and cela, while translating the same pronoun into German would require a choice between er, sie, and es. This is hard as selecting the correct pronoun may need discourse analysis as well as linguistic and world knowledge. Null subjects in pro-drop languages pose additional challenges as they express person and number within the verb's morphology, rendering a subject pronoun or noun phrase redundant. Thus, translating from such languages requires generating a pronoun in the target language for which there is no pronoun in the source.Pronoun translation is known to be challenging not only for MT in general, but also for Statistical Machine Translation (SMT) in particular (Le Nagard and Koehn, 2010;Hardmeier and Federico, 2010; Novák, 2011;. Phrase-based SMT (Koehn et al., 2013) was state of the art until recently, but it is gradually being replaced by Neural Machine Translation, or NMT, (Cho et al., 2014; Sutskever et al., 2014; Bahdanau et al., 2015;Luong et al., 2015). 1 NMT yields generally higher-quality translation, but is harder to analyze, and thus little is known about how well it handles pronoun translation. Yet, it is clear that it has access to larger context compa...

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The Role of Expectedness in the Implicitation and Explicitation of Discourse Relations

Cited by 9 publications

References 13 publications

Cognitive complexity and the linguistic marking of coherence relations: A parallel corpus study

Cognitive complexity and the linguistic marking of coherence relations: A parallel corpus study

Bridging Background Knowledge Gaps in Translation with Automatic Explicitation

Proceedings of the Third Workshop on Discourse in Machine Translation

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