Tracing variation in discourse connectives in translation and interpreting through neural semantic spaces (2021)

Abstract: In the present paper, we explore lexical contexts of discourse markers in translation and interpreting on the basis of word embeddings. Our special interest is on contextual variation of the same discourse markers in (written) translation vs. (simultaneous) interpreting. To explore this variation at the lexical level, we use a data-driven approach: we compare bilingual neural word embeddings trained on source-to-translation and source-to-interpreting aligned corpora. Our results show more variation of semantically related items in translation spaces vs. interpreting ones and a more consistent use of fewer connectives in interpreting. We also observe different trends with regard to the discourse relation types.

## introduction this paper presents an explorative study of discourse connectives in cross-linguistically mediated communication. we compare written translation with spoken simultaneous interpreting in the domain of european parliament discourse. we start from the perspective of translationese, an observation that translations share specific linguistic features distinguishing them from non-translated language. although interpreting has recently received increased attention in computational approaches, there are not so many studies of interpretese. in this work, we are aiming to understand the differences between translated and interpreted texts in terms of discourse connectives. @xcite show that translations and interpreting transcripts differ significantly in the use of these linguistic units. for instance, there is a difference in the preference for the choice of connectives triggering the same relation. specifically, the relation of contrast/concession is preferably expressed with the connective however in translation, whereas the use of but is characteristic of interpreting. this indicates that in marking logical relations, interpreters tend to prefer more general items over more specific ones. this is in line with the existing observations about differences between speech and writing @xcite : there are fewer but more polyfunctional discourse markers in speech than in writing. to explore the differences between translated and interpreted texts, we follow the data-driven approach as in @xcite and use neural word embeddings (word2vec) to compare the bilingual semantic spaces from bilingual word embeddings built on aligned corpora. the resulting semantic spaces model the lexical choices of a specific translation. we train two bilingual distributional models on two comparable, aligned corpora (translation and interpreting) and compare the resulting semantic spaces to detect differences in the lexical patterns of discourse connectives impacted by translation mode (written vs. spoken). as stated by @xcite the existing constraints of interpreting (high cognitive load, time pressure) have impact on lexical choices, which is reflected in interpreting if compared to translation. ## related work translated texts share linguistic characteristics which distinguish them from non-translated textsthe phenomenon of translationese (see @xcite @xcite . these differences can be traced in the distribution of various language patterns, i.e. linguistic features mostly organised in terms of more abstract categories (sometimes called translation universals or translation features) such as normalisation and shining-through (teich, 2003), simplification @xcite , convergence @xcite and explicitation @xcite . the latter is often related to discourse connectives. for instance, @xcite stated that explicitation in interpreting is related to adding discourse markers among other means of cohesive explicitness. at the same time, @xcite observed a reduction of cohe-sive ties in interpreting if compared to the source language input (implicitation). @xcite showed that there are differences between translation and interpreting in the usage of linking adverbials (with translation being more explicit). the phenomena of explicitation and implicitation may also depend on the type of relations discourse connectives trigger: cognitively simple relations are more often left implicit than relations that are cognitively more complex (see @xcite . this is also confirmed in a recent study by blumenthal-dramé (2021) who showed that causal links are more expected than concessive ones and the processing of concessive sentences benefits more from the explicit marking than the processing of causal sentences (also pointing to cross-lingual differences between english and german). although we are not pursuing creation of a multilingual lexicon, our work is related to those dealing with mapping of discourse connectives @xcite @xcite @xcite . numerous studies analysed discourse connectives from a cross-lingual point of view using aligned texts (see @xcite @xcite @xcite . in our exploratory study of the differences between translation and interpreting, we do not focus on the direct transfer of specific discourse connectives, but look into their bilingual lexical context. in this sense, our work relates to the study by @xcite , who used cross-lingual embeddings and discourse connectives to analyse semantically related words in several languages. we rely on the methodology proposed by @xcite , which is related to other studies that attempt to use word embeddings for linguistic analysis, such as @xcite ; @xcite ; @xcite . whereas @xcite analyse general variation in lexical choices in interpreting and translation, our focus is on discourse-related phenomena. besides that, our work is related to those with a focus on domain-specific word embeddings @xcite , those dealing with multilingual word embeddings for lexicon induction and mapping @xcite @xcite and those focusing on creating consistent spaces @xcite in cross-lingual word analogy tasks @xcite . ## data and methods our data includes officially published original speeches, as well as transcripts of the speeches delivered at the european parliament (ep) aligned with their translations or interpretations, correspondingly. both parallel subsets are parts of the europarl-uds @xcite and epic-uds @xcite corpora, with english as source and german as target. both corpora are strictly comparable in terms of register, as they contain european parliament speeches. the spoken part (epic-uds) are true transcripts of the spoken utterances by members of the ep and interpeters, including spoken language features such as false starts, filled pauses and unfinished sentences. the basis of written dataset (europarl-uds) is also a spoken event in the european parliament, however modified to respect written language characteristics. overall, we have 130,000 sentence pairs in the translated data and 3,397 sentence pairs for the interpreted data. refer to this paper uses the approach described by bizzoni and @xcite : neural word embeddings (word2vec) are used to compare sourceto-translation and source-to-interpreting lexical choices. the sentence aligned corpus data is used to create reshuffled bilingual pseudo-sentences. subsequently, a standard skipgram word2vec model is trained on these sentences to create translation and interpreting spaces. the main idea of this method is that words with a consistent translation in an aligned corpus will share similar contexts with their translation, and will result in close proximity in distributional spaces. this is a mechanism similar to that used in standard distributional semantics, where words having similar contexts are closer in space, but it is applied to bilingual aligned contexts instead of standard monolingual texts. for example, if a source word a is always translated with a target word b, and b is only used to translate a, such words will appear only together and their bilingual context will be identical. as an effect, their distributional vectors will be extremely similar and the two words will be very close neighbours in a semantic space. the more a translation deviates from this type of systematicity, the less two words will result close in the semantic space. for the words without consistent translation, different configurations in translation space exist. if a word is ambiguous, it can be close to the variants of its translations in the space, but the similarity would be lower (reflected in the cosine similarity score). if a word is very hard or even impossible to translate with one term, there will be no translations close to the word, which does not show a high similarity to its neighbours. while this method, being based on word2vec, is sensitive to frequency effects, @xcite show that it is robust enough to obtain meaningful results from corpora few thousands sentences long (see @xcite , for more details and examples). our list of connectives is restricted to 14 items that appear to be most frequent in both written and spoken data at hand. 1 the connectives can be grouped according to their senses defined in pdtb-3 @xcite as temporal (finally, first, firstly, secondly), contingency (as, because, if, so, why), comparison (but, however, yet) and expansion (also, that). some of them are ambiguous and may trigger different relations, e.g. the connective as is ambiguous and may express all the four meaning relations, although preference is given for temporal and contingency @xcite . the connective finally is ambiguous between temporal meaning and the meaning of expansion, whereas the connective if is conditional, but may also express comparison. the resulting semantic spaces of connectives are analyse in the following way: 10 nearest neighbours of the 14 discourse connectives are analysed for semantic relation to the connective -if an item expresses the same discourse function, i.e. source or target language synonym (e.g. however -butzwar), can be used for paraphrasing the function (e.g. why -for this reason, finally -to sum up) or items that express other logical relations via discourse connective (e.g. that -ob), we consider them as semantically related. we have a number of assumptions about the resulting semantic spaces. first of all, following the findings by @xcite , we expect that the connectives translated consistently with the same target language equivalent will be very close to each other in the resulting semantic space. besides that, we know that interpreters tend to use fewer connectives than translators. so we expect less variation in interpreting than translation, and consequently, fewer but closer semantic neighbours in interpreting than in translation. at the same time, as there should be more implicitation in interpreting than in translation, we expect more target language equivalents in the semantic spaces of translation than interpreting. furthermore, we expect variation in the resulting semantic spaces with regard to the discourse relation a connective expresses. ## analysis and observations we qualitatively analyse the differences between resulting translation and interpreting spaces (tr and si spaces in the following). for the sake of space, we will report on the most remarkable cases only. the whole list of spaces for both translation and interpreting is given in the appendix. ## conclusion and future work we used neural semantic spaces to observe differences between discourse connectives in translation and interpreting. generally, we observe similar trends reported by @xcite for the lexical differences between interpreting and translation. our results are in line with implication and explicitation trends in translation. they confirm the assumption that interpreting shows more implicitation than translation. it was also interesting to see that cognitive complexity of relations also has impact on the resulting semantic spaces in translation, but has a different effect on the interpreting spaces. in future, we plan to further investigate these differences using a wider range of connectives expressing different discourse relations. we would also like to systematically compare our results with the original aligned corpus, to provide a convincing qualitative test of the trends we have observed through our semantic spaces. also, word2vec represents one of the most efficient methods to produce a word's compact distributional profile, but it is not the only one. it could be interesting to compare the results of word2vec with other state of the art, non-contextualized foot_1 word embeddings when applied to the same corpus. moreover, our results also confirm the tendency of interpreting to show less variation in terms of the range of discourse connectives. here, we would like to extend our work and include more discourse connectives. finally, we plan to experiment with disambiguated data (include the cases of discourse relations only).