Semantic Prosody in Machine Translation: the English-Chinese Case of Passive Structures

• URL: http://arxiv.org/abs/2510.14662v1
• Date: Thu, 16 Oct 2025 13:16:59 GMT
• Title: Semantic Prosody in Machine Translation: the English-Chinese Case of Passive Structures
• Abstract summary: We propose an approach to teach machine translation models about semantic prosody of a specific structure.<n>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.<n>Then we fine-tune OPUS-MT, NLLB-600M and mBART50 models with our dataset for the English-Chinese translation task.
• Score: 3.3573302174193973
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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 translations of each other may have different semantic prosody, more attention should be paid to this linguistic property to generate accurate translations. 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 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.

Related papers

• Bidirectional Chinese and English Passive Sentences Dataset for Machine Translation [3.3573302174193973]
  This paper proposes a bidirectional multi-domain dataset of passive sentences, extracted from five Chinese-English parallel corpora.<n>The dataset consists of 73,965 parallel sentence pairs (2,358,731 English words, 3,498,229 Chinese characters)<n>We evaluate two state-of-the-art open-source MT systems with our dataset, and four commercial models with the test set.
  arXiv  Detail & Related papers  (2026-03-16T13:04:09Z)
• Beyond English: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs [41.53385639669034]
  Large language models have significantly advanced Multilingual Machine Translation (MMT), yet the broad language coverage, consistent translation quality, and English-centric bias remain open challenges.<n>We introduce textbfLMT, a suite of textbfLarge-scale textbfMultilingual textbfTranslation models centered on both Chinese and English.<n>LMT achieves SOTA performance among models of comparable language coverage, with our 4B model surpassing the much larger Aya-13B and NLLB-54B models by a substantial margin.
  arXiv  Detail & Related papers  (2025-11-10T11:54:53Z)
• The Role of Handling Attributive Nouns in Improving Chinese-To-English Machine Translation [5.64086253718739]
  We specifically target the translation challenges posed by attributive nouns in Chinese, which frequently cause ambiguities in English translation.<n>By manually inserting the omitted particle X ('DE'), we improve how this critical function word is handled.
  arXiv  Detail & Related papers  (2024-12-18T20:37:52Z)
• Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting [66.02718577386426]
  We provide a simple characterization of idiomatic translation and related issues. We conduct a synthetic experiment revealing a tipping point at which transformer-based machine translation models correctly default to idiomatic translations. To improve translation of natural idioms, we introduce two straightforward yet effective techniques.
  arXiv  Detail & Related papers  (2023-10-10T23:47:25Z)
• Towards Effective Disambiguation for Machine Translation with Large Language Models [65.80775710657672]
  We study the capabilities of large language models to translate "ambiguous sentences" Experiments show that our methods can match or outperform state-of-the-art systems such as DeepL and NLLB in four out of five language directions.
  arXiv  Detail & Related papers  (2023-09-20T22:22:52Z)
• Do GPTs Produce Less Literal Translations? [20.095646048167612]
  Large Language Models (LLMs) have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. We find that translations out of English (E-X) from GPTs tend to be less literal, while exhibiting similar or better scores on Machine Translation quality metrics.
  arXiv  Detail & Related papers  (2023-05-26T10:38:31Z)
• Decomposed Prompting for Machine Translation Between Related Languages using Large Language Models [55.35106713257871]
  We introduce DecoMT, a novel approach of few-shot prompting that decomposes the translation process into a sequence of word chunk translations. We show that DecoMT outperforms the strong few-shot prompting BLOOM model with an average improvement of 8 chrF++ scores across the examined languages.
  arXiv  Detail & Related papers  (2023-05-22T14:52:47Z)
• Multilingual Neural Machine Translation:Can Linguistic Hierarchies Help? [29.01386302441015]
  Multilingual Neural Machine Translation (MNMT) trains a single NMT model that supports translation between multiple languages. The performance of an MNMT model is highly dependent on the type of languages used in training, as transferring knowledge from a diverse set of languages degrades the translation performance due to negative transfer. We propose a Hierarchical Knowledge Distillation (HKD) approach for MNMT which capitalises on language groups generated according to typological features and phylogeny of languages to overcome the issue of negative transfer.
  arXiv  Detail & Related papers  (2021-10-15T02:31:48Z)
• Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast [12.691501386854094]
  We propose to align sentence representations from different languages into a unified embedding space, where semantic similarities can be computed with a simple dot product. As the experimental results show, the sentence representations produced by our model achieve the new state-of-the-art on several tasks.
  arXiv  Detail & Related papers  (2021-09-01T08:48:34Z)
• ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback [70.5469946314539]
  ChrEnTranslate is an online machine translation demonstration system for translation between English and an endangered language Cherokee. It supports both statistical and neural translation models as well as provides quality estimation to inform users of reliability.
  arXiv  Detail & Related papers  (2021-07-30T17:58:54Z)
• Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation [81.7786241489002]
  Massively multilingual models for neural machine translation (NMT) are theoretically attractive, but often underperform bilingual models and deliver poor zero-shot translations. We argue that multilingual NMT requires stronger modeling capacity to support language pairs with varying typological characteristics. We propose random online backtranslation to enforce the translation of unseen training language pairs.
  arXiv  Detail & Related papers  (2020-04-24T17:21:32Z)
• Translation Artifacts in Cross-lingual Transfer Learning [51.66536640084888]
  We show that machine translation can introduce subtle artifacts that have a notable impact in existing cross-lingual models. In natural language inference, translating the premise and the hypothesis independently can reduce the lexical overlap between them. We also improve the state-of-the-art in XNLI for the translate-test and zero-shot approaches by 4.3 and 2.8 points, respectively.
  arXiv  Detail & Related papers  (2020-04-09T17:54:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.