Semantic Prosody in Machine Translation: the E nglish- C hinese Case of Passive Structures (2025)

Abstract: Semantic prosody is a collocational meaning formed through the co-occurrence of a linguistic unit and a consistent series of collocates, which should be treated separately from semantic meaning. Since words that are literal translation of each other may have different semantic prosody, more attention should be paid to this linguistic property in order to generate accurate translation. However, current machine translation models cannot handle this problem. To bridge the gap, we propose an approach to teach machine translation models about semantic prosody of a specific structure. We focus on Chinese BEI passives and create a dataset of English-Chinese sentence pairs with the purpose of demonstrating the negative semantic prosody of BEI passives. Then we fine-tune OPUS-MT, NLLB-600M and mBART50-mmt models with our dataset for the English-Chinese translation task. Our results show that fine-tuned MT models perform better on using BEI passives for translating unfavourable content and avoid using it for neutral and favourable content. Also, in NLLB-600M, which is a multilingual model, this knowledge of semantic prosody can be transferred from English-Chinese translation to other language pairs, such as Spanish-Chinese.

## introduction the notion of semantic prosody was first proposed in corpus linguistics in the 1990s. it was @xcite who first noticed the phenomenon that some words or phrases tend to co-occur with unpleasant events, such as happen and set in. this property was later named semantic prosody @xcite 's concept of phonological prosody) by @xcite and defined as "a consistent aura of meaning with which a form is imbued by its collocates". another well-known example is cause as a verb @xcite . as shown in figure 1 , entities "caused" are generally considered undesirable: rupture, death, pain, misery, damage, and diseases. @xcite considers semantic prosody to be on the "pragmatic side of the semantics/pragmatics continuum" and can reveal the speaker or writer's attitude. however, semantic prosody cannot be precisely perceived by individual native speakers through intuition @xcite . a statistical analysis of corpus data is needed to correctly interpret the semantic prosody of a linguistic unit (a node), since corpus data can provide multiple usage patterns across many speakers and reflect the general intuition of native speakers @xcite . the co-occurrence of a node and a predominantly positive/negative context can foster subtle associative meaning, and the node is then considered to have positive/negative semantic prosody. a word in english probably has a synonym in chinese with the same semantic meaning. however, the two words do not necessarily have the same semantic prosody due to language variation. for example, the semantic meaning of the english word because and the chinese word "由 于 youyu" are equivalent, but their semantic prosodies are not. because has a neutral semantic prosody, while youyu has a negative one @xcite . another example is insist on, which is often used to describe annoying stubbornness, while its literal translation in chinese "坚 持 jianchi" has positive semantic prosody. this divergence in semantic prosody has even caused english learners who are native mandarin chinese speakers to use insist on to mean encouragement. at the same time, this usage has never appeared in coca foot_0 @xcite . during the translation process, semantic prosody should be considered in order to achieve semantic/pragmatic equivalence and avoid missing information. since it is difficult for individual non-native speakers of a language to correctly perceive semantic prosody with intuition, even professional human translators may fail to convey it into the target text, as they usually translate from their second language (l2) into their mother tongue. in both translation pedagogy and l2 learning, semantic prosody awareness has been considered to be important and of value to learners @xcite . inequivalence of semantic prosody is also a problem for machine translation (mt), yet little attention has been paid to it. if we ask a model to translate "i was praised by my teacher" into chinese, google translate, chatgpt 4 and deepl would give the following translations: ## related work research on semantic prosody has developed in multiple directions, ranging from theoretical discussions to empirical studies in contrastive linguistics and translation. in this section, we first outline the key concepts related to semantic prosody, and then examine how it has been applied in crosslinguistic studies and translation research. ## dataset description we propose teaching machine translation models about the negative semantic prosody of chinese bei passives by fine-tuning them with a dataset focusing on this structure, (hereafter referred to as the bei dataset). detailed information on the dataset is presented in this section. ## experimental setup and analysis in this section, we describe our experiments teaching mt models about the negative semantic prosody of bei passives. we also present the experimental setup for fine-tuning and probing, a comprehensive analysis of the evaluation and our results. we experimented with three sequence to sequence (seq2seq) mt models: helsinki-nlp/opus-mt-en-zh @xcite , facebook/nllb-200-distilled-600m @xcite and facebook/mbart-large-50-many-to-many-mmt @xcite )-hereinafter referred to as opus-mt, nllb-600m and mbart50-mmt. all models reach state-of-the-art performance on english-chinese text translation. the evaluation was done in two parts. we tested the performance of the fine-tuned models for translating general text with the flores+ @xcite and tatoeba @xcite datasets. bleu @xcite , chrf2 @xcite and cometkiwi @xcite metrics were used to evaluate the translation. after that, we used the bei dataset test split to see whether the fine-tuned models had learned about the negative semantic prosody of bei passives and if they had a higher accuracy deciding when to use bei passives in translation. finally, a probing task was conducted to assess whether the pretrained and fine-tuned models possess information that aids in correctly using the active or passive voice when translating english passives into chinese. the task also aimed to identify which layers of the encoder or decoder store this information. ## discussion after fine-tuning the different models with the bei dataset, they all showed improvements in reserving chinese bei passives for negative contexts. here, we have a closer look at those cases with negative evidence that the model managed to translate correctly into the active voice, in order to find potential methods to further improve model performance. the cases that models corrected after finetuning can be categorized into two kinds according to the translation strategy. for some be passives, models switched the position of the subject and the object and removed the passive marker-such as in example (6)-in order to give a sentence in active voice. this is the kind of change we expected to see. the second translation strategy is to use a notional passive, which is also called a topic sentence, as in example ( 8): (8) source text: that they have been preserved so well. target text: 它们都保存得十分完整。 'they are in perfect preservation.' mbart50-mmt: 它们被保存得如此好。 'they have been preserved so well.' fine-tuned mbart50-mmt: 它们保存得很 好。 'they are in very good preservation.' in a topic sentence, the subject argument is the topic and the patient of the verb (they in example (8)), while the remaining constituent is the comment. topic sentences can express passive meaning because of the nature of their subjects, and sometimes it is grammatical to add a passive marker and turn it into a marked passive sentence, depending mainly on the verb. however, notional passives are only compatible with a very restricted number of verbs, and are not considered passive constructions in a strict sense @xcite . in modern mandarin chinese literature, notional passives mainly occur in neutral contexts (over 80% of the time) and are twice as frequent as bei passives @xcite , which means it is a plausible choice for translating be passives into chinese. moreover, in example (8) we can observe that translating a be passive into a notional passive does not require the model to change the order of subject and verb or to add back the omitted agent of the verb. the fine-tuned model only had to drop bei 被 between the patient they 它们 and verb preserve 保存 to obtain a topic sentence. in this case, they 它们 refers to non-human arguments, which means they cannot conduct the action of preserving something, but can only be the patient of the verb preserve. without the need to add the passive marker bei, the sentence already transfers the passive notion that they are pre-served. using topic sentences to translate be passives requires less movement and can preserve the passive meaning and neutral semantic prosody at the same time. however, while the bei passive is more frequent in translated chinese literature than in original chinese literature, the opposite is the case with the notional passive @xcite . both human translators and machine translation models should pay more attention to this structure to generate more accurate chinese translations for passive sentences. ## conclusion and future work in this work, we propose an approach for teaching sequence to sequence machine translation models about the semantic prosody of a specific structure, namely the chinese bei passive, and to improve model performance on translating english be passives into chinese. the primary focus of our approach is to fine-tune mt models with a dataset that explicitly demonstrates the negative semantic prosody of bei passives through the contrast with using bei passives to translate be passives with negative context, while for be passives with neutral and positive content, the translations are in the active voice. after fine-tuning opus-mt, nllb-600m and mbart50-mmt, all models showed improvements in using bei passives correctly in translation while maintaining their original performance on general text translation, showing that our approach is a valid one. moreover, for multilingual models such as nllb-600m, the knowledge of semantic prosody is transferable to translation tasks in other language pairs (e.g. from english-chinese translation to spanish-chinese translation). through probing experiments, we found that, for nllb-600m and mbart50-mmt, information that helps to decide whether the active or passive voice is more plausible in translation is concentrated in the deeper layers of the encoder and equally distributed in the decoder. fine-tuning improved the probing accuracy of decoders significantly, but did not have a great influence on the encoders. our work focused on the bei structure. a potential future research direction will be fine-tuning a model with combined data that can show the semantic prosody of multiple linguistic units and observe whether the model can learn different semantic prosodies simultaneously. having a more diverse dataset can also prevent the problem of overfitting. however, there is no comprehensive list of linguistic units from different languages with semantic prosody that may cause inequivalence in translation. it would be of great value to compile such a list and create datasets that demonstrate their semantic prosody. in english and spanish, passive structures can be categorized into two different kinds, namely adjectival and verbal passives. in this work, we did not distinguish between them, as they present the same problem when translated into chinese by mt models. however, we plan to investigate further to determine whether model performance varies on different kinds of passives. finally, another avenue for future work would be multilingual fine-tuning, which may yield better results on multilingual models. the difficulty here lies in the limited numbers of annotated multilingual parallel corpora, without which it would be difficult to collect data on a specific structure.