Towards the evaluation of automatic simultaneous speech translation from a communicative perspective (2021)

Abstract: In recent years, automatic speech-to-speech and speech-to-text translation has gained momentum thanks to advances in artificial intelligence, especially in the domains of speech recognition and machine translation. The quality of such applications is commonly tested with automatic metrics, such as BLEU, primarily with the goal of assessing improvements of releases or in the context of evaluation campaigns. However, little is known about how the output of such systems is perceived by end users or how they compare to human performances in similar communicative tasks. In this paper, we present the results of an experiment aimed at evaluating the quality of a real-time speech translation engine by comparing it to the performance of professional simultaneous interpreters. To do so, we adopt a framework developed for the assessment of human interpreters and use it to perform a manual evaluation on both human and machine performances. In our sample, we found better performance for the human interpreters in terms of intelligibility, while the machine performs slightly better in terms of informativeness. The limitations of the study and the possible enhancements of the chosen framework are discussed. Despite its intrinsic limitations, the use of this framework represents a first step towards a user-centric and communication-oriented methodology for evaluating real-time automatic speech translation.

## introduction real-time or simultaneous speech translation (st) aims at translating a continuous speech input from one language to another with the lowest latency foot_0 and highest quality possible. in recent years, automatic speech translation systems have been devel-oped at scale, and their quality has improved significantly @xcite . at present, research is increasingly focusing on end-to-end trainable encoder-decoder models, i.e. speech-tospeech (sts) or speech-to-text (stt) translation systems that directly match source and target language @xcite @xcite . nonetheless, the cascading approach is de facto still the mainstream solution for speech translation (st). the main reason is that this approach benefits from the remarkable improvements in automatic speech recognition (asr) @xcite and machine translation (mt) @xcite obtained thanks to the wealth of task-specific data available. in cascading systems, the process of translating from speech to text or from speech to speech is performed by a series of concatenated modules. in most cases, these systems apply asr to the speech input, and then pass the results on to an mt engine. since a short latency is an important characteristic of such systems, the translation is rendered while the source is unfolding, on the basis of different approaches ranging from simple time delay to complex agents that establish when the context is sufficient to perform the translation. several additional components can be integrated into this pipeline, such as text normalization @xcite , suppression of speech disfluencies @xcite , prosody transfer @xcite , and so forth. real-time st systems have the potential to be used in communicative settings, such as institutional events, lectures, conferences, etc. in order to make multilingual content accessible in realtime, thus increasing inclusion and participation when human services for language accessibility are not available, such as live interlingual subtitling @xcite or conference interpreting @xcite . so far, the evaluation of st in general, and real-time st in par-ticular, has been framed in the domain of computer science (cs). in cs, automatic metrics are applied in order to compare systems and monitor progress over time foot_1 . however, little is known about how such systems, that for the sake of this paper we will define as machine interpreting (mi) systems foot_2 , perform in real communication settings and whether they are able to meet the needs of end users. to the best of our knowledge, no evaluation framework has been developed and deployed in the past to assess the performance of such systems from a communicative perspective. to address this shortcoming, in the present contribution we apply a user-centric evaluation framework derived from interpreting studies (is) to the task of assessing an automatic system for st. moving towards an evaluation framework that takes into consideration the authentic communicative setting, we compare the performances of the automated system with the performances of professional simultaneous interpreters. we do so in order to assess the level of usability of such framework and to benchmark the performances of the machine, inferring the suitability of the st system for the proposed communication task from its comparison with the human performance. the rest of the paper is organised as follows. in section 2, we present an overview of research areas in the field of automatic speech translation and human interpreting evaluation. in section 3, we illustrate our research methodology and the experimental design. section 3.1 describes the dataset created for this task, while section 3.2 introduces the framework used to evaluate the performance of the machine and of the human interpreters. section 4 presents the results of the evaluation and section 5 discusses and puts the results into perspective. section 6 concludes the paper with final remarks. ## related work the evaluation of simultaneous speech translation, independently of whether the process is performed by a human or a machine, is a topic central both to the domain of computer science and of interpreting studies. in cs, st is typically evaluated in terms of quality and latency. similar to mt, the approach used consists in the application of automatic metrics in order to allow for a fast and objective evaluation of the systems @xcite . however, due to its novelty, the st research community currently lacks a universally adopted evaluation methodology. quality is generally measured by bleu @xcite , ter @xcite and meteor @xcite . the approach to compare system outputs against source texts, gold standard translations, and other system outputs represents, despite the limitations of such metrics @xcite , a widely accepted evaluation methodology. the measurement of latency, which broadly corresponds to the ear-voice span of human interpreting (e.g. @xcite , represents a more challenging task that still lacks sufficient clarity and consistency. in this context, several metrics have been introduced, such as average proportion (ap) @xcite , continues wait length (cw) @xcite , average lagging (al) @xcite , differentiable average lagging (dal) (cherry and foster, 2019). generally speaking, the evaluation approach used in cs is product-oriented. the concept of quality is limited to measuring proximity in the linguistic surface between translation and ground truth. it does not take into consideration the user perception, the pragmatic aspect of communication, and, intrinsically, cannot consider the translation process as embedded in a communicative event (e.g. @xcite . this is different to is. since human interpretation always occurs in a specific communicative setting, the need to evaluate it accordingly has always been in focus. here, the pursuit of conceptual and methodological tools for the empirical study and assessment of quality has a long tradition, particularly in the conference domain and simultaneous modality (e.g. @xcite @xcite . despite the different perspectives that have been adopted to define and evaluate quality, there is considerable agreement among scholars on a number of criteria which are considered fundamental when evaluating human interpretation. most criteria of quality are associated with the product-oriented perspective and can be subsumed in two main areas, the first one focusing primarily on the interpretation or target-text as "a 'faithful' image" @xcite or "exact and faithful reproduction" @xcite of the original speech, the second one on the notion of intelligibility, also called clarity, target-text comprehensibility, linguistic acceptability, stylistic correctness, etc. such evaluation is centred on the view of interpreting as a language processing task. at an even higher level, quality can also be seen under the paradigm of a holistic idea of successful communication. from this perspective, interpreting is assessed on the basis of whether it successfully allows the parties involved in a particular context of interaction to achieve their communicative goal, as judged from the various perspectives in and on the communicative event @xcite . the focus of this perspective is no longer on the product (the rendition), but rather on the communicative action performed to achieve a certain purpose and effect, and therefore on the holistic function of facilitating communicative interaction @xcite . from a methodological perspective, quality in interpretation has been evaluated through surveys @xcite , measures of performance through experimentation @xcite , or corpusbased analysis @xcite . different to the cs approach, which is based on automatic metrics, the analysis of data in is is performed on the basis of a manual evaluation of the corpus data. while such evaluation frameworks have been designed and used regularly in the domain of machine translation, very few attempts have been made so far to evaluate the performance of automatic speech translation system both in the context of the product-based and of the holistic/communicative approach. a few pilot studies on the usability of st systems have only been performed in the context of dialogue interpreting @xcite , while only one has been attempted in the area of real-time st @xcite . we believe that such approaches, if appropriately adapted to the research desideratum at hand, could contribute to a better understanding and evaluation of machine speech translation systems. ## data and methodology as discussed in the previous section, st systems are typically evaluated by means of automatic metrics using reference datasets. although such evaluations are useful to compare systems among each other, one of their main limitations is that they do not take into consideration the communicative setting nor the perception of their usefulness by final users. to overcome this limitation, we select and apply to the assessment of st a user-centric framework commonly used for the evaluation of human interpretation. in order to understand the potential usefulness of the automatically generated translation, we compare the machine performance with a gold standard: the interpretation delivered by professional human interpreters in the real context of the event. simultaneous interpretation (si) is the modality most commonly used to provide multilingual access in real-time foot_3 . since we assume that the service provided by professional interpreters allows communication among the parties in the event, we consider it to be our "communicative" ground truth. this gold standard is not an ideal rendition of the original, but it comes with all the benefits and limitations of the real simultaneous translation used at a specific event to overcome language barriers. by means of this comparison we can infer, at least to some extent, the communicative performance of the machine in the context of a real communicative event. the overall question driving our research is therefore "how does the performance of a speech-to-text translation system compare with human si?". to answer this question we compile a corpus of speeches in english delivered in real-life contexts and align them with their human interpreted versions into italian as well as with the output of a simultaneous stt translation system chosen for this task. the dataset is described in section 3.1. we manually assess the quality of the human and automatic renditions (transcriptions) on the basis of the evaluation framework described in section 3.2. this evaluation represents an attempt to apply a more user-centric approach to the assessment of the automatic service provided by stt translation systems. ## results the scores for each of the two parameters (intelligibility and informativeness) are summed for each speech, output and rater and then averaged. the relative percentage score is calculated on the maximum amount of points obtainable for each text. the figures below illustrate the results of data analysis. as shown by figure 1 , human interpreters obtain better scores for intelligibility (84.84 % to 73.49 %), while the machine output is rated slightly better than the human interpretations in terms of informativeness (74.63 % to 72.82 %). when combining the scores for the two rating criteria (figure 2 ), the human output surpasses machine output by 4.77 percentage points. it can be argued that the two parameters do not have the same weight in terms of their impact on the success of the communicative event. at this stage of evaluation, however, we decide to combine them without any weight and to leave this more in-depth analysis to a later phase of development of our evaluation framework. figure 3 illustrates the standard deviation (sd) for the two evaluation parameters for all 5 speeches. both for the criterion intelligibility and for the criterion informativeness, the sd for the human output is larger than for the machine output, for which the scores are very close to each other in our sample. this result suggests that variables such as topic, density, speed of the original speech, accents, etc. affect less the machine than the human interpreter. on the one hand, this is quite surprising if one considers that aspects such as performance of asr with foreign accents, to name but one example, are considered detrimental in automatic language processing @xcite . on the other hand, the larger sd for the human output may point to the fact that humans tend to have a high degree of variance in performances due to different background knowledge, skills, etc. because of the small size of the corpus, the trends observed in the present study cannot be generalized and the analysis should be conducted on a larger sample. in order to verify the adequacy of the evaluation methodology, and in particular of the rating scales used to assess intelligibility and informativeness achieved by the human and the machine interpretation, krippendorff's α is calculated for the two evaluation criteria and the two types of output. this measure is chosen as it allows to overcome the problems presented by fleiss's κ @xcite , another common measure of intercoder reliability used when multiple raters are involved (see @xcite . the statistic is interpreted like other measures of reliability, with higher scores indicating higher intercoder reliability. overall, α values are below the lowest value (.667) defined as acceptable by @xcite for tentative conclusions, and well below the recommended value of .800 (ibid.). the α is considerably lower than these values for both intelligibility and informativeness in human interpreting (α = .442 and .607 respectively). the α for mi intelligibility is .658, while the value of interrater reliability for the category of informativeness in mi is barely acceptable (α = .676). overall, these results suggest that applying the evaluation scale derived from is as is for the comparison of hi and mi output presents limitations that need to be addressed in future work, for instance in terms of the optimisation of the scoring rubric and the inclusion of further dimensions in the evaluation scale. ## discussion the differences in the raters' evaluation of the human and machine output can be better understood by analysing several phenomena retrieved from the corpus. the complexity of the evaluation and, inherently, of the comparison between human and machine interpretation is strictly linked with the pragmatic nature of hi, which often calls for interventions on the part of the interpreter. such interventions, emerging on the linguistic surface of the interpreted text, may be evidence of underlying strategic behaviour exercised, for instance, to favour comprehension, or may be the result of emergency coping tactics aimed at preventing a disruption of the rendition in adverse conditions, for instance in the case of particularly informationdense or fast speeches. phenomena such as generalisation, addition and (intentional) omission (see for instance @xcite seem to occur more often in human si than in written translation, and are entirely absent in the automatic translation of speech. the mt engine lacks any linguistic phenomena that may index intentional interventions, not only because it lacks deliberateness, but also because it has been trained on written (and not interpreted) texts. this fundamental difference may limit the ability of a classic evaluation framework (both manual and automatic) to provide an assessment of quality which reflects the communicative success of an event mediated by human or by machine interpretation. in order to illustrate our argument, we report several example passages from the corpus complete of their rendition by the human interpreters and by the st engine. in the following example ( in the following example ( the two examples discussed above illustrate an inherent conundrum in the evaluation, i.e. how to evaluate pragmatic interventions by the interpreter. whether such interventions should be considered as justifiable or not and which rendition, the human or the machine, is more appreciated by the end-user and more conducive to the same communicative goal pursued by the speaker cannot be reflected in an evaluation framework such as the one chosen for this initial exploration. furthermore, these phenomena substantiate our argument that a framework for the evaluation of st in comparison with hi requires a broader perspective. another key point of comparison between hi and st lies in the presence and evaluation of errors. it may be argued that blatant errors are more apparent in st than in professional hi, as exemplified by the following passage: the erroneous translation in the automatic output ("devoti ghiacciati", i.e. iced pious), clearly due to a speech recognition issue (example 4), is immediately recognisable as such by the (human) end-user. this type of blatant mistake seems to be a distinctive characteristic of mi and is more frequent than in neural machine translation because of the key features of oral speech. it would be interesting to explore the effects of this error type in a real-life communicative event. however, human interpreters may also commit severe mistakes. let us consider the following case: at first sight, the hi may appear more elegant and fluent than the automatic output (the median intelligibility score for this segment is 5). thus, the wrong rendition of "overestimating" with "sottova-lutazione" (en: underestimating), due to erroneous anticipation or to having misheard the speaker's words, may go unnoticed without a comparison with the source text. the examples discussed above emphasise on the one hand that the type of mistakes end-users are confronted with may be of very different nature. the effects of the various types of mistakes on communication and their evaluation by human raters may also vary, and should be explored within a framework that takes into account the communicative perspective. on the other hand, this comparison also stresses the need to compare st not with the ideal of hi but with the variability of human performances. ## conclusion and future work this paper reports on an experiment that compares the output of a real-time speech-to-text translation system with the performance of human interpreters. the main goal was to expand the methodology that is used nowadays to evaluate such systems from the purely computational approach based on automatic metrics to a more user-centric and communicationoriented one. to do so, we apply an evaluation framework derived from interpreting studies and let six evaluators assess the performance of humans and machines according to the criteria of intelligibility and informativeness. the results show a better performance by humans in terms of intelligibility and a slightly better performance by the machine in terms of accuracy. despite several drawbacks of the framework adopted, the path initiated with this study may bear fruits in terms of better understanding and evaluating the output of speech-to-text and speech-tospeech translation systems in the context of situated multilingual communication and its pragmatic context. the study also highlights several limitations of the approach chosen. they are mainly related to the difficulty of defining objective criteria in the evaluation of quality of interpreted texts, and to the intrinsic shortcomings of evaluating a communicative event only on the basis of the product of the translation process without the contextual embedding of the evaluation in the communicative setting. such shortcomings need to be addressed in future work.