Moral Semantics Survive Machine Translation: Cross-Lingual Evidence from Moral Foundations Corpora

Maciej Skorski

arxiv: 2605.22660 · v1 · pith:P3JYDH7Snew · submitted 2026-05-21 · 💻 cs.CL · cs.AI

Moral Semantics Survive Machine Translation: Cross-Lingual Evidence from Moral Foundations Corpora

Maciej Skorski This is my paper

Pith reviewed 2026-05-22 05:32 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords moral foundationsmachine translationcross-lingualsocial mediamoral values classificationPolishembedding similarityLLM translation

0 comments

The pith

Machine translation preserves subtle moral semantics in social media texts across languages.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates whether direct machine translation can move moral language annotations from English to Polish without losing the key cues that classifiers rely on. It applies a pipeline of embedding comparisons, alignment measures, judge evaluations, and classifier tests to roughly 50,000 annotated social media posts. Results show high similarity scores and only tiny drops in detection accuracy across moral foundations. This matters because most large moral annotation sets exist only in English, so a working translation step would let researchers study moral values in many more languages without starting from scratch each time. A sympathetic reader would view the work as a practical bridge for expanding moral language research beyond English-dominant data.

Core claim

Despite shortcomings in handling slang, vulgarity, and culturally-loaded expressions, direct translation preserves subtle moral cues well enough to be harvested by cross-lingual machine learning -- with mean cosine similarity of 0.86 and AUC gaps of 0.01--0.02 across all foundations closing further under fine-tuning of language models.

What carries the argument

Four-method validation pipeline of LaBSE cross-lingual embedding similarity, Centered Kernel Alignment (CKA), LLM-as-judge evaluation, and deep learning classifier parity tests.

If this is right

Moral values classification extends to Polish and related Slavic languages using translated English data.
Fine-tuning language models reduces the remaining performance gaps between languages.
Machine translation offers a practical, low-cost route to moral research in languages without native annotated corpora.
The same translation-plus-validation approach supports generalization to other under-resourced languages.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The pipeline could transfer to other value-laden domains such as political or ethical discourse analysis.
Small native-language validation sets could be added to translated data to handle rare cultural moral expressions.
The method invites tests on more distant language pairs to check how far the preservation holds.
Real-time monitoring of moral language across multilingual social media becomes more feasible without new annotations in every language.

Load-bearing premise

That embedding similarity, kernel alignment, judge scores, and classifier parity together sufficiently confirm preservation of subtle moral meanings even for culturally specific or low-frequency expressions.

What would settle it

Native Polish speakers rating paired original English and translated Polish posts as carrying substantially different moral foundations, or classifiers trained on translated data showing large performance drops on held-out native Polish text.

Figures

Figures reproduced from arXiv: 2605.22660 by Maciej Skorski.

read the original abstract

Moral language is subtle and culturally variable, making it difficult to translate faithfully across languages. Idiomatic expressions, slang, and cultural references introduce hard-to-avoid translation artifacts. Yet automated moral values classification depends on language-specific annotated corpora that exist almost exclusively in English. We investigate whether LLM-based translation can bridge this gap, taking Polish as a test case. Using $\sim$50k morally-annotated social media posts from a diverse range of topics, we apply a principled four-method validation pipeline: LaBSE cross-lingual embedding similarity, Centered Kernel Alignment (CKA), LLM-as-judge evaluation, and deep learning classifier parity tests. We show that despite shortcomings in handling slang, vulgarity, and culturally-loaded expressions, direct translation preserves subtle moral cues well enough to be harvested by cross-lingual machine learning -- with mean cosine similarity of 0.86 and AUC gaps of 0.01--0.02 across all foundations closing further under fine-tuning of language models. These results demonstrate that machine translation is a practical and cost-effective path to moral values research in languages currently under-resourced in this domain. We demonstrate this for Polish as a representative Slavic language, with expected generalisation to related languages.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Machine translation keeps moral signals mostly intact in Polish social media posts per the numbers, though the proxies might overlook some cultural drift.

read the letter

Machine translation preserves moral semantics in Polish social media posts well enough for practical use, according to this paper's numbers. They took around 50,000 morally annotated posts, translated them, and ran four checks: LaBSE similarity, centered kernel alignment, LLM as judge, and how well classifiers perform on the translated version versus original. The results show 0.86 average cosine similarity and AUC differences of only 0.01 to 0.02 across moral foundations, with fine-tuning making it even tighter. This suggests direct translation is a workable shortcut for languages without their own moral corpora. The work does a good job laying out these complementary tests and giving specific figures instead of vague claims. It positions the result as useful for under-resourced languages like Polish and similar ones. The potential weak point is whether these metrics fully capture subtle changes in culturally loaded expressions or slang. Embeddings and classifiers might align on broad patterns even if some nuanced moral framing drifts in translation. The paper acknowledges shortcomings with vulgarity and such, but the stress-test concern about missing low-frequency specifics seems worth a closer look in the details. This paper is for applied researchers in moral NLP who need to bootstrap data for new languages. It offers a cost-effective path rather than a deep theoretical advance. I would recommend sending it to peer review so the sampling, translation choices, and statistical tests can be examined in full.

Referee Report

2 major / 2 minor

Summary. The paper claims that LLM-based direct translation of Polish social media posts preserves subtle moral semantics sufficiently for cross-lingual machine learning, despite shortcomings with slang, vulgarity, and cultural expressions. Using ~50k morally annotated posts and a four-method validation pipeline (LaBSE cosine similarity, CKA, LLM-as-judge, and classifier parity), it reports a mean similarity of 0.86 and AUC gaps of 0.01-0.02 across foundations that narrow further with fine-tuning, positioning translation as a practical route for under-resourced languages such as Polish.

Significance. If the central results hold, the work supplies a concrete, low-cost method for extending Moral Foundations research beyond English by leveraging existing annotations and models. The provision of specific quantitative benchmarks (0.86 similarity, small AUC gaps) together with four complementary checks is a positive feature that supports potential generalization to related Slavic languages.

major comments (2)

[Methods] Methods section: the manuscript provides no details on sampling of the ~50k posts, the specific translation model or LLM used, or the exact exclusion rules applied to posts containing slang or vulgarity. These omissions are load-bearing because they prevent assessment of whether post-hoc decisions inflate the reported LaBSE similarity and AUC parity.
[Validation pipeline] Validation pipeline and results: the four proxies (LaBSE, CKA, LLM-as-judge, classifier AUC) could remain high even if translation alters nuanced moral framing in low-frequency or culturally specific Polish expressions, since embeddings and classifiers may capture topical overlap or surface patterns rather than original moral semantics. A targeted test isolating culturally loaded items is needed to support the claim that subtle cues survive.

minor comments (2)

[Abstract] Abstract: state explicitly whether translation is Polish-to-English, English-to-Polish, or bidirectional, and list the specific Moral Foundations examined.
[Results] Results: report statistical significance, confidence intervals, or p-values for the AUC gaps and similarity scores to allow readers to judge the practical importance of the 0.01-0.02 differences.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which highlights important areas for improving the clarity and robustness of our work. We address each major comment below and outline the revisions we will make to the manuscript.

read point-by-point responses

Referee: [Methods] Methods section: the manuscript provides no details on sampling of the ~50k posts, the specific translation model or LLM used, or the exact exclusion rules applied to posts containing slang or vulgarity. These omissions are load-bearing because they prevent assessment of whether post-hoc decisions inflate the reported LaBSE similarity and AUC parity.

Authors: We agree that greater methodological transparency is essential for reproducibility and for allowing readers to evaluate potential selection effects. In the revised manuscript we will add a dedicated subsection to the Methods that specifies: (1) the sampling procedure and source of the ~50k morally annotated posts, including any topic diversity criteria; (2) the exact translation model and LLM version used for direct translation; and (3) the precise exclusion or handling rules applied to posts containing slang, vulgarity, or culturally specific expressions. These details will demonstrate that decisions were made prior to analysis rather than post hoc. revision: yes
Referee: [Validation pipeline] Validation pipeline and results: the four proxies (LaBSE, CKA, LLM-as-judge, classifier AUC) could remain high even if translation alters nuanced moral framing in low-frequency or culturally specific Polish expressions, since embeddings and classifiers may capture topical overlap or surface patterns rather than original moral semantics. A targeted test isolating culturally loaded items is needed to support the claim that subtle cues survive.

Authors: We acknowledge that aggregate metrics across the full corpus could mask translation issues specific to low-frequency or culturally loaded expressions. Although the LLM-as-judge component was intended to probe semantic fidelity beyond surface patterns, we agree that an explicit targeted analysis would provide stronger support for our claims. In the revision we will add a new analysis subsection that isolates a subset of posts containing Polish idioms, slang, and culturally specific references, reporting LaBSE similarity and LLM-as-judge scores for this subset alone to directly test preservation of moral semantics in these challenging cases. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper presents an empirical study using a four-method validation pipeline consisting of LaBSE cross-lingual embedding similarity, Centered Kernel Alignment (CKA), LLM-as-judge evaluation, and deep learning classifier parity tests on held-out data. The reported metrics (mean cosine similarity of 0.86 and AUC gaps of 0.01-0.02) are computed directly from these external benchmarks and standard classifiers rather than being defined in terms of the authors' own choices or prior results. No equations, self-citations, or fitted parameters are shown that reduce the central claim to a self-referential construction. The derivation is self-contained against independent external benchmarks, with no load-bearing steps that qualify as self-definitional, fitted-input predictions, or ansatz smuggling.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the representativeness of the 50k social media posts for moral language and on the assumption that the chosen validation metrics detect preservation of subtle moral cues even when slang or cultural references are lost.

axioms (1)

domain assumption Moral foundations categories transfer across languages in a way that can be measured by embedding similarity and classifier parity
Invoked when interpreting small AUC gaps as evidence that moral semantics survive translation.

pith-pipeline@v0.9.0 · 5741 in / 1400 out tokens · 57956 ms · 2026-05-22T05:32:53.638482+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

mean cosine similarity of 0.86 and AUC gaps of 0.01–0.02 across all foundations

What do these tags mean?

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages

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Tuan Dung Nguyen, Georgina Lyall, Alasdair Tran, Minkyoung Shin, Nicholas G Carroll, Colin Klein, and Lexing Xie. 2022. Mapping Topics in 100,000 Real-Life Moral Dilemmas. InProceedings of the International AAAI Conference on Web and Social Media, Vol. 16. 699–710

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Gabriel Nicholas and Aliya Bhatia. 2023. Lost in Translation: Large Language Models in Non-English Content Analysis.arXiv e-prints(2023), arXiv–2306

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Flor Miriam Plaza-del Arco, Amanda Cercas Curry, Alba Curry, Gavin Aber- crombie, and Dirk Hovy. 2024. Angry men, sad women: Large language models reflect gendered stereotypes in emotion attribution. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 7682–7696

work page 2024
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Vjosa Preniqi, Iacopo Ghinassi, Julia Ive, Charalampos Saitis, and Kyriaki Kalimeri. 2024. MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social Discussions. InProceedings of the 2024 International Conference on Information Technology for Social Good. ACM, Bremen Germany, 433–442. doi:10.1145/3677525.3678694

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Shamik Roy and Dan Goldwasser. 2021. Analysis of Nuanced Stances and Senti- ment Towards Entities of US Politicians through the Lens of Moral Foundation Theory. InProceedings of the Ninth International Workshop on Natural Language Processing for Social Media. Association for Computational Linguistics, Online, 1–13. doi:10.18653/v1/2021.socialnlp-1.1

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Maciej Skorski and Alina Landowska. 2025. Beyond Human Judgment: A Bayesian Evaluation of LLMs’ Moral Values Understanding. InProceedings of the 2nd Workshop on Uncertainty-A ware NLP (UncertaiNLP 2025), Bryan Eikema, Raúl Vázquez, Jonathan Berant, Marie-Catherine de Marneffe, Barbara Plank, Artem Shelmanov, Swabha Swayamdipta, Jörg Tiedemann, Chrysoula Z...

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Jackson Trager, Alireza S. Ziabari, Aida Mostafazadeh Davani, Preni Golazizian, Farzan Karimi-Malekabadi, Ali Omrani, Zhihe Li, Brendan Kennedy, Nils Karl Reimer, Melissa Reyes, Kelsey Cheng, Mellow Wei, Christina Merrifield, Arta Khosravi, Evans Alvarez, and Morteza Dehghani. 2022. The Moral Foundations Reddit Corpus. doi:10.48550/ARXIV.2208.05545

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Greco, Davide Picca, and Andrea Tagarelli

Lorenzo Zangari, Candida M. Greco, Davide Picca, and Andrea Tagarelli. 2025. ME2-BERT: Are Events and Emotions What You Need for Moral Foundation Prediction?. InProceedings of the 31st International Conference on Computational Linguistics, Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara Di Eugenio, and Steven Schockaert (Eds.). Asso...

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Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guil- laume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. Unsupervised cross-lingual representation learning at scale. InProceedings of ACL. 8440–8451

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Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017. Automated hate speech detection and the problem of offensive language. In Proceedings of the 11th International AAAI Conference on Web and Social Media. 512–515

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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT. 4171–4186

work page 2019

[4] [4]

Matthew Feinberg and Robb Willer. 2013. The Moral Roots of Environmental Attitudes.Psychological Science24, 1 (2013), 56–62

work page 2013

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Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, and Wei Wang. 2020. Language-agnostic BERT sentence embedding.arXiv preprint arXiv:2007.01852(2020)

work page arXiv 2020

[6] [6]

Jesse Graham, Jonathan Haidt, Sena Koleva, Matt Motyl, Ravi Iyer, Sean Wojcik, and Peter H Ditto. 2013. Moral foundations theory: The pragmatic validity of moral pluralism.Advances in Experimental Social Psychology47 (2013), 55–130

work page 2013

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Jesse Graham, Jonathan Haidt, and Brian A Nosek. 2009. Liberals and conserva- tives rely on different sets of moral foundations.Journal of personality and social psychology96, 5 (2009), 1029–1046. doi:10.1037/a0015141

work page doi:10.1037/a0015141 2009

[8] [8]

Jonathan Haidt and Craig Joseph. 2004. Intuitive ethics: How innately prepared intuitions generate culturally variable virtues.Daedalus133, 4 (2004), 55–66

work page 2004

[9] [9]

Chang, Jenna Chin, Christian Leong, Jun Yen Leung, Arineh Mirinjian, and Morteza Dehghani

Joe Hoover, Gwenyth Portillo-Wightman, Leigh Yeh, Shreya Havaldar, Aida Mostafazadeh Davani, Ying Lin, Brendan Kennedy, Mohammad Atari, Zahra Kamel, Madelyn Mendlen, Gabriela Moreno, Christina Park, Tingyee E. Chang, Jenna Chin, Christian Leong, Jun Yen Leung, Arineh Mirinjian, and Morteza Dehghani. 2020. Moral Foundations Twitter Corpus: A Collection of ...

work page doi:10.1177/1948550619876629 2020

[10] [10]

Hopp, Jacob T

Frederic R. Hopp, Jacob T. Fisher, Devin Cornell, Richard Huskey, and René Weber

work page

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2021), 232–246

The Extended Moral Foundations Dictionary (eMFD): Development and Applications of a Crowd-Sourced Approach to Extracting Moral Intuitions from Text.Behavior Research Methods53, 1 (Feb. 2021), 232–246. doi:10.3758/s13428- 020-01433-0

work page doi:10.3758/s13428- 2021

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Katharina Kann, Ryan Cotterell, and Hinrich Schütze. 2017. One-Shot Neural Cross-Lingual Transfer for Paradigm Completion. InProceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Regina Barzilay and Min-Yen Kan (Eds.). Association for Computational Linguistics, Vancouver, Canada, 1993–2003. doi:10...

work page doi:10.18653/v1/p17-1182 2017

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Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton. 2019. Similarity of neural network representations revisited. InProceedings of the 36th International Conference on Machine Learning. 3519–3529

work page 2019

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Tuan Dung Nguyen, Ziyu Chen, Nicholas George Carroll, Alasdair Tran, Colin Klein, and Lexing Xie. 2024. Measuring Moral Dimensions in Social Media with Mformer.Proceedings of the International AAAI Conference on Web and Social Media18 (May 2024), 1134–1147. doi:10.1609/icwsm.v18i1.31378

work page doi:10.1609/icwsm.v18i1.31378 2024

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Tuan Dung Nguyen, Georgina Lyall, Alasdair Tran, Minkyoung Shin, Nicholas G Carroll, Colin Klein, and Lexing Xie. 2022. Mapping Topics in 100,000 Real-Life Moral Dilemmas. InProceedings of the International AAAI Conference on Web and Social Media, Vol. 16. 699–710

work page 2022

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Gabriel Nicholas and Aliya Bhatia. 2023. Lost in Translation: Large Language Models in Non-English Content Analysis.arXiv e-prints(2023), arXiv–2306

work page 2023

[17] [17]

Flor Miriam Plaza-del Arco, Amanda Cercas Curry, Alba Curry, Gavin Aber- crombie, and Dirk Hovy. 2024. Angry men, sad women: Large language models reflect gendered stereotypes in emotion attribution. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 7682–7696

work page 2024

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Vjosa Preniqi, Iacopo Ghinassi, Julia Ive, Charalampos Saitis, and Kyriaki Kalimeri. 2024. MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social Discussions. InProceedings of the 2024 International Conference on Information Technology for Social Good. ACM, Bremen Germany, 433–442. doi:10.1145/3677525.3678694

work page doi:10.1145/3677525.3678694 2024

[19] [19]

Shamik Roy and Dan Goldwasser. 2021. Analysis of Nuanced Stances and Senti- ment Towards Entities of US Politicians through the Lens of Moral Foundation Theory. InProceedings of the Ninth International Workshop on Natural Language Processing for Social Media. Association for Computational Linguistics, Online, 1–13. doi:10.18653/v1/2021.socialnlp-1.1

work page doi:10.18653/v1/2021.socialnlp-1.1 2021

[20] [20]

Maciej Skorski and Alina Landowska. 2025. Beyond Human Judgment: A Bayesian Evaluation of LLMs’ Moral Values Understanding. InProceedings of the 2nd Workshop on Uncertainty-A ware NLP (UncertaiNLP 2025), Bryan Eikema, Raúl Vázquez, Jonathan Berant, Marie-Catherine de Marneffe, Barbara Plank, Artem Shelmanov, Swabha Swayamdipta, Jörg Tiedemann, Chrysoula Z...

work page doi:10.18653/v1/2025.uncertainlp-main.3 2025

[21] [21]

Maciej Skorski and Alina Landowska. 2025. The Moral Gap of Large Language Models. (2025). arXiv:2507.18523 [cs] doi:10.13140/RG.2.2.26221.70880

work page doi:10.13140/rg.2.2.26221.70880 2025

[22] [22]

Jackson Trager, Alireza S. Ziabari, Aida Mostafazadeh Davani, Preni Golazizian, Farzan Karimi-Malekabadi, Ali Omrani, Zhihe Li, Brendan Kennedy, Nils Karl Reimer, Melissa Reyes, Kelsey Cheng, Mellow Wei, Christina Merrifield, Arta Khosravi, Evans Alvarez, and Morteza Dehghani. 2022. The Moral Foundations Reddit Corpus. doi:10.48550/ARXIV.2208.05545

work page doi:10.48550/arxiv.2208.05545 2022

[23] [23]

Greco, Davide Picca, and Andrea Tagarelli

Lorenzo Zangari, Candida M. Greco, Davide Picca, and Andrea Tagarelli. 2025. ME2-BERT: Are Events and Emotions What You Need for Moral Foundation Prediction?. InProceedings of the 31st International Conference on Computational Linguistics, Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara Di Eugenio, and Steven Schockaert (Eds.). Asso...

work page 2025