Simultaneous Interpreting as a Noisy Channel: How Much Information Gets Through (2023)

Abstract: We explore the relationship between information density/surprisal of source and target texts in translation and interpreting in the language pair English-German, looking at the specific properties of translation (“translationese”). Our data comes from two bidirectional English-German subcorpora representing written and spoken mediation modes collected from European Parliament proceedings. Within each language, we (a) compare original speeches to their translated or interpreted counterparts, and (b) explore the association between segment-aligned sources and targets in each translation direction. As additional variables, we consider source delivery mode (read-out, impromptu) and source speech rate in interpreting. We use language modelling to measure the information rendered by words in a segment and to characterise the cross-lingual transfer of information under various conditions. Our approach is based on statistical analyses of surprisal values, extracted from n-gram models of our dataset. The analysis reveals that while there is a considerable positive correlation between the average surprisal of source and target segments in both modes, information output in interpreting is lower than in translation, given the same amount of input. Significantly lower information density in spoken mediated production compared to non-mediated speech in the same language can indicate a possible simplification effect in interpreting.

## introduction in this study, we describe and explain linguistic choice in translation and interpreting from the point of view of rational communication, according to which language users strive to encode their messages effectively and efficiently, i.e. they attempt to ensure that their messages are transmitted successfully while at the same time, their cognitive effort stays at a reasonable level (see e.g. @xcite . our approach stipulates that the behaviour of translators, while guided by effectiveness and efficiency, is severely constrained by the specific conditions of mediated communication, especially in interpreting (see studies on the cognitive effort in interpreting, e.g. @xcite . simultaneous interpreters have to balance allocating cognitive resources to overlapping comprehension and production processes in a way that allows them to complete the task and communication is not put at risk. from empirical translatology we know that the coping mechanisms involved in translation/interpreting have an impact on the linguistic properties of the output, widely known as translationese (e.g. @xcite @xcite . section 2). while there is a rich literature on trends in translational behaviour (e.g. simplification, explicitation, normalisation), a unifying explanation for the diverse linguistic phenomena is still lacking. this study is an attempt to fill this gap by adopting an information-theoretic approach. our analysis is based on measuring information density (id) aka surprisal of translation/interpreting outputs and contrasting them with non-mediated (i.e. original) speeches and between each other, as well as looking at the association between surprisal values of aligned source and target segments. we interpret surprisal as the amount of information conveyed by a given linguistic event from the point of view of a given language model. in mediated communication, interpreters' and translators' output is expected to reflect the amount of information contained in the source. however, it may be expected that interpreters will not manage to encode the target to the same level of average surprisal (short: avs) as observed in the source. apart from mediation mode (translation, inter-preting) and translation direction, further factors may have an impact on encoding. in simultaneous interpreting, where comprehension of the source text (st) and production of the target text (tt) claim cognitive resources at the same time, the amount of information transmitted from st to tt may vary according to source delivery mode (impromptu vs. read-out) and source speech rate (words per minute). with regard to the various factors at play in crosslingual mediation discussed above, we formulate the following hypotheses. to address these hypotheses, we analyse surprisal in a bidirectional english-german corpus of european parliament proceedings containing both mediation modes. the remainder of the paper is organised as follows. section 2 provides an overview of related work and theoretical background. section 3 describes our methodology and experimental setup. in sections 4 and 5, we present the results and their interpretation. section 6 gives a summary and conclusion. ## discussion we have established that the information density of the target is strongly and positively correlated with the information density of the source in both mediation modes, spoken and written. however, the information output in interpreting is lower than in translation given the same input: the intercept of the regression lines for interpreting is lower in both translation directions (see the legends in figure 1 ). to demonstrate the differences between translation and interpreting, we looked at the top and bottom segment pairs by target surprisal in epic-uds and their translated alternatives from europarl-uds. example (1) demonstrates that translation follows the german source more faithfully than the interpreted version, where the last coordination is omitted, making the output less informative. translation: europe must learn to speak with one voice and to take united action. interpreting: europe must learn to speak with one voice. (@xmath0.52) in example (2), the explicit description of an issue, given in the source and faithfully retained in translation, is replaced with a generic anaphoric phrase (this sort of thing), and the more specific word bürger (citizens) is replaced with a general noun, people. translation: the burden represented by the constantly growing number of illegal immigrants -i would like to emphasise the word 'illegal' hereis becoming unbearable for the citizens of the eu. interpreting: and this sort of thing is an unsustainable situation in the eu and for people of the eu. (@xmath1.24). the surprisal values for each token in the interpreted segment from example (2) are shown in figure 5 . the lineplot demonstrates how simpler structural and lexical content in interpreting (as compared to translation) keeps the avs low. the powerful simplification trend, which is reinforced by the spoken features of interpreting and which pulls the avs in interpreting down, is counteracted by the tendency to follow source segment patterns, which generates a shining-through effect. it can be manifested in the use of cognates, unusual verb constructions, or as in example (3) unexpected noun phrases. translation: ein viertel aller europäer leidet mindestens einmal in dem leben unter psychischen problemen. interpreting: jeder vierte europäer leidet zumindest ein mal in seinem leben unter einer geistigen krankheit. (@xmath2.76) similarly, the interpreted segment from example (4) has a surprisal peak at the end of the sentence. it is generated by the word complaints in an unusual context, which was most likely an erroneous word choice. (4) source: wollen wir den chinesen mit wto klagen drohen. translation: do we want to threaten the chinese with world trade organisation (wto) sanctions? interpreting: you know are we going to threaten the chinese with wto complaints (@xmath3.57). based on our results, rejection of h2b might be explained by the intensity of the two opposite trends that increase the spread of the surprisal values in interpreting. on the one hand, interpreters have a strong tendency to select simpler, more frequent vocabulary and fill pauses with highly expected phrases, which decreases mean segment surprisal. on the other hand, interpreting can demonstrate more noticeable forms of interference and lack of fluency that would generate increased segment surprisal. finally, to ascertain that avs values are aligned with intuition, we looked at the results for segments that were either omitted or added in interpreting. typical segments that are skipped in our sample are the politeness formula and discourse organisation markers. for example, the interpreter omitted segments like the following: sehr geehrter herr präsident. (en translation: mister president.) (@xmath4.23), ich komme dann zu dem ende. (en translation: i am coming to the end.) (@xmath5.99), finally just to sum up very briefly an old saying. (@xmath6.42), let us be very clear. (@xmath7.17). a more curious case are additions, i.e. segments that were not aligned to any content on the source language side. these segments typically reiterated the speaker's emphasis and included short segments like aber was sollte man jetzt tun. (en translation: but what should be done now.) (@xmath8.10), aber so ist es. (en translation: but that's how it is) (@xmath9.14), that is the thing. (@xmath10.88), so here we have to speak out. (@xmath11.00). the avs for omitted and added segments was lower than the average across all segments in both language directions in epic-uds (6.31 and 5.69 for interpreted german and english, respectively). this means that the attempted modelling setup supports some theoretical expectations if not others. overall, a manual analysis of token surprisal values in various subsets of data demonstrated that an n-gram model trained on limited data might be too constrained by the amount of available corpus evidence to rely on its output for a fine-grained analysis of translational phenomena. however, surprisal contours are a good source for qualitative checks of statistical results. all else being equal, the german model returned higher surprisal values and perplexities, either suggesting a lower quality than that of the english model or simply a language-specific feature. overall, the proposed modelling approach might be biased toward producing middle-range surprisal values (evidenced by a sharp-peak distribution with thin tails), partly because it assigns the same probability to all hapax legomena and uses a simple back-off to a lower-order n-gram to resolve the out-of-vocabulary issue. ## summary and conclusion the study demonstrated that mean segment surprisal values capture the distinction between nonmediated and mediated language for three out of four parallel subcorpora: mediated language has lower surprisal. importantly, this difference can be interpreted as an indicator of simplification: mediated language is characterised by a lower information density than comparable non-mediated segments. it is particularly true for interpreting, as seen from our analysis of the association between sources and targets. this, however, does not affect the strong positive correlation between the information density of sources and targets, seen in this study for all parallel subcorpora. contrary to our expectations, transcripts of interpreted documents had a higher variability of segment surprisal values than in translation, making their information density less predictable from that of the source segment. the choice of the research method in this study was largely determined by the small size of the data available for modelling if we wanted to train on a balanced corpus (12 k segments, ca. 250 k tokens in each language). the parameters of the surprisal distributions suggest that the current modelling approach might be sub-optimal. in future work, we plan to explore other modelling approaches compatible with small-size datasets to obtain a more faithful representation of information density in a segment and across the segments. the ultimate goal of modelling surprisal is to apply information theory to the explanation of linguistic choice in mediated communication linking it to the availability of cognitive resources that can be more or less engaged depending on the properties of the source segment, context, mediation mode, and extralinguistic conditions of the information transfer. this goal calls for multilingual models, on the one hand, and for more fine-grained qualitative analysis, on the other. we believe that the interpreting data -represented by accurate transcripts of spoken sources and their targets, including disfluencies -is particularly suited for these purposes and for understanding the mechanisms of human speech generation, in general.