Venue: NAACL 2025

# Computational Discovery of Chiasmus in Ancient Religious Text

Hope McGovern\({}^{1}\) Hale Sirin\({}^{2}\) Tom Lippincott\({}^{2}\)

\({}^{1}\) Department of Computer Science & Technology, University of Cambridge, U.K.

\({}^{2}\) Center for Digital Humanities, Johns Hopkins University, Baltimore, U.S.A.

\({}^{1}\) hope.mcgovern@cl.cam.ac.uk  \({}^{2}\) {hsirin1, tom.lippincott}@jhu.edu

###### Abstract

Chiasmus, a debated literary device in Bibilical texts, has captivated mystics while sparking ongoing scholarly discussion. In this paper, we introduce the first computational approach to systematically detect chiasmus within Bibilical passages. Our method leverages neural embeddings to capture lexical and semantic patterns associated with chiasmus, applied at multiple levels of textual granularity (half-verses, verses). We also involve expert annotators to review a subset of the detected patterns. Despite its computational efficiency, our method achieves robust results, with high inter-annotator agreement and system precision@\(k\) of 0.80 at the verse level and 0.60 at the half-verse level. We further provide a qualitative analysis of the distribution of detected chiasm, along with selected examples that highlight the effectiveness of our approach.1

Footnote 1: All code and data available at [https://github.com/comp-int-hum/literary-translation](https://github.com/comp-int-hum/literary-translation)

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Associations for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160

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Footnote †: _Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)_, pages 154–160


rhetorical device detection (Schneider et al., 2021), and includes the repetition of words, phrases, grammatical structures, or (identical or antithetical) concepts as part of the chisatic parallels. In contrast with Schneider et al. (2021), our method is extensible to various sizes of chiasmus; that is, those of just four lines long or of 100 lines long, and is language-agnostic, whereas previous work has focused only on fine-grained, intra-line chiasm in English or German. In this study, we analyze both half-verses and verses as units so that a chiasm within the same verse can also be captured (i.e. "The Sabbath was made for man, not man for the Sabbath"). We formalize the notion of Biblical chiasmus thoroughly in SS 2.2.

Our main contributions are as follows:

1. We show that multilingual embedding spaces may be effectively used to detect rhetorical phenomena such as chiasmus in ancient manuscripts.
2. We provide, for the first time, a mathematical formalism of Biblical chiasmus and provide a computational algorithm for its detection.
3. Our method is computationally efficient and achieves robust results, with high inter-annotator agreement and system precision@\(k\) of 0.80 at the verse level and 0.60 at the half-verse level.
4. We contribute to Classics and Biblical Studies by providing a qualitative analysis of the distribution of detected chiasmi, along with selected examples that highlight the effectiveness of our approach.

## 2 Method

### Data

We use as our primary source the Translator's Amalgamated Hebrew Old Testament (TAHOT)2, which is based on the Leningrad Codex - the oldest complete extant version of the Hebrew Old Testament. Note that modern English translations follow a verification system that is at times slightly different to the Hebrew text due to a difference in textual traditions. We use the Hebrew verification system to better uncover chiasmi as they may be in the original text. N.B. We carry out all detection experiments using the Hebrew text, but for clarity and accessibility, report English translations3 in tables and figures.

Footnote 2: www.STEP Bible.org

We segment the text into two levels: verses and half-verses. In the Hebrew text, half-verses are naturally marked by the cancillation symbol, _atnach_, which typically separates the two halves of a verse. We consider up to and including the word with the _attach_ to be the first half, while the remainder is the second half. We then remove all vocalizations and canctillation symbols before embedding.

### Formalizing Chiasmus

The first step in our method involves constructing a cosine similarity matrix, denoted as \(S\), based on feature vectors extracted from the text via E5, a multilingual embedding model (Wang et al., 2024)4. Our method is similar to that of Burns et al. (2021), which uses pairwise cosine similarity of embedded representations to identify intertextual phrases in Latin.

Footnote 4: We use the ‘small’ variant of this model, with 118M parameters.

Each element \(S_{ij}\) represents the cosine similarity between the feature vectors of textual units \(i\) and \(j\). Next, we identify potential chisatic structures by focusing on matching groups of text pairs, such as \(A\) and \(A^{\prime}\), \(B\) and \(B^{\prime}\), and so forth. For each potential chisic structure, we compute the _chiasmus score_\(\mu_{\text{chiasmus}}\), which is the average cosine similarity of these matching pairs:

\[\mu_{\text{chiasmus}}=\frac{1}{k}\sum_{i=1}^{k}S_{\text{pair}(i)} \tag{1}\]

where pair\((i)\) refers to the indices of the matching pairs (e.g., \(A\) and \(A^{\prime}\), \(B\) and \(B^{\prime}\)). To assess the distinctiveness of this chisatic structure, we compute the average of all non-pair similarities, denoted \(\mu_{\text{non-pair}}\), which includes comparisons such as \(S_{A,B},S_{B,C^{\prime}}\), and others:

\[\mu_{\text{non-pair}}=\frac{1}{n}\sum_{i,j\in\text{non-pair}}S_{ij} \tag{2}\]

Our final score for each window is computed as the difference between these two averages:

\[\text{Final Score}=\mu_{\text{chiasmus}}-\mu_{\text{non-pair}} \tag{3}\]

To detect chiasmi across the text, we apply this method in a _sliding window_ fashion, where each 

starting position in the text serves as a potential beginning of a chiasmus detector. The length of the sliding window, \(N\), is fixed for each experiment, and we test several different \(N\) values, analyzing the aggregated results. We ensure that chiasmi do not cross book boundaries by disallowing matches across these divisions.

Finally, we standardize the chiasmus scores across the text by calculating their z-scores. The z-score \(z_{i}\) for each window \(i\) is determined by:

\[z_{i}=\frac{\mu_{\text{chiasmus},i}-\mu_{\text{chainsms},\text{mean}}}{\sigma _{\text{chiasmus}}} \tag{4}\]

where \(\mu_{\text{chiusus},\text{mean}}\) and \(\sigma_{\text{chiasmus}}\) are the mean and standard deviation of all chiasmus scores, respectively. We classify chisatic structures as significant if their z-scores exceed a threshold of three (3) standard deviations above the mean, thereby identifying statistically salient chiasm within the text.

### Why not use an LLM?

While large language models (LLMs) have enabled remarkable advances in a wide variety of NLP tasks, data contamination concerns and a lack of explainability limit their scope of usefulness for chiasmus detection in Biblical text.

Preliminary exploration revealed that some LLMs have a propensity to generate verbatim copyrighted English translations (e.g., the ESV) from Ancient Hebrew source passages. This behavior suggests that the extensive availability of online Biblical commentaries, which may reference chiastic structure, is likely included in web-based training corpora. Consequently, the outputs of LLMs risk being skewed by prior exposure to human annotation (Balloccu et al.).

Furthermore, the lack of transparency in LLM-generated responses poses a significant barrier for adoption in scholarly contexts. Biblical scholars, who are our primary target audience, require interpretable and verifiable methods rather than opaque, black-box solutions. Additionally, our aim extends beyond merely detecting chiasm; we seek to formalize the concept mathematically, thereby offering a rigorous and standardized framework for discussing what remains a somewhat ambiguous topic. Such a formalism could serve as a valuable tool for facilitating scholarly discourse and advancing the study of chiasmus.

## 3 Experiments

We run our model over the Hebrew Old Testament, considering every line or half-line as a potential starting position and length \((N)\) of chiasmus to be in the range of four to eight (\(N\in[4,8]\)). We take the top-50 highest-scoring outputs for both half-verse and verse grouping and evaluate them via human annotation. Annotation guidelines and results are found in SS 3.1. We use top-\(k\) precision as our evaluation metric as we are primarily interested in creating a tool for scholars to find the most-promising candidates for chiasmus to further examine.

Table 2 presents an overview of the system's output for chiastick structures at the half-verse and verse levels. A total of 1,896 chiastick structures were identified at the half-verse level, with an average length of 5.93 textual units (\(\pm\)1.34) and an average score of 0.32 (\(\pm\)0.1). For verse-level groupings, 879 chiastick structures were found, with an average length of 6.01 lines (\(\pm\)1.38) and an average score of 0.29 (\(\pm\)0.08). The book of Genesis contains the highest number of half-verse chiasmi, while Numbers contains the most verse-level chiasmi.

As shown in Figure 1, the number of detected chiastick structures varies across books of the Bible, with more instances found at the half-verse level than at the verse level for all books. Notably, certain books exhibit disproportionately higher numbers of half-verse chiasmi, particularly Genesis, 1 Samuel, Judges, 1 Chronicles, Psalms, Jeremiah, and Ezekiel. This trend is consistent with the literary nature of these texts: Psalms, Jeremiah, and Ezekiel include significant poetic sections, where half-verse chiastick structures are more prominent,

\begin{table}
\begin{tabular}{p{113.8pt} p{42.7pt} p{42.7pt} p{42.7pt}} \hline \hline  & & **Half-Verse** & **Versec** \\ \hline \multirow{4}{*}{Baselines} & Num. Found & 1896 & 879 \\  & Top Book & Genesis & Numbers \\  & Avg. Length & 5.93 \(\pm\) 1.34 & 6.01 \(\pm\) 1.38 \\  & Avg. Score & 0.32 \(\pm\) 0.1 & 0.29 \(\pm\) 0.08 \\ 

while Genesis features dense narrative and highly literary passages as well as many formulaic genealogies. The high counts in 1 Samuel, Judges, and 1 Chronicles, which are historical books, likely reflect the system's identification of formulaic narrative patterns, such as the recurring descriptions of the kingly line of Israel (e.g., "X became king, reigned for Y years, and did evil in the sight of the Lord").

### Human Annotation

The top-50 scoring half-verse and verse chiasm were manually reviewed by the first two authors, who both have graduate-level training in ancient languages and literature5. Given a three-class rubric, they were asked to determine whether the set of verses of half-verses identified by the model exhibited (1) _chiastic repetition_: a chisatic structure of repetition formed either through lexical or semantic textual units, (2) _non-chiastic repetition_: lexical or semantic repetition of textual units, but not in a discernibly chisatic way, or (3) _no repetition_: no discernible parallel or repeating content. Cohen's Kappa (\(\kappa\)), used to quantify inter-annotator agreement, is 0.76 and 0.89 for half-verses and verses, respectively, indicating strong agreement between the annotators.

Footnote 5: While the chiasmus identification is done entirely in Hebrew, the annotators use a literal English translation following Hebrew word order alongside the Hebrew text for easier inspection.

Two verse-level passages and four half-verse level passages were putative between chisatic repetition and non-chiatic repetition, while there were only two (both half-verse) passages that were disputed between no repetition and chisatic repetition. In other words, annotators were nearly always in agreement over which passages had elements of structural repetition, but discerning between chiastic and non-chiastic repetition poses a slightly more difficult challenge.

Considering "true" chiasm to be those marked as chisic by both annotators, we achieve a system precision@\(k\) of **0.60** for half-verses and **0.80** for verses. In both experiments, the majority of top-scoring chiasm are found in narrative sections of text.

Interestingly, passages classified as _non-chiastic repetition_ often involved formulaic or ritualistic language, which could be of interest to scholars seeking computational methods for identifying such patterns in texts. We find 29 examples of this across the top 100 collectively. Only 3 of the top 100, or 3%, of the top-scoring passages belonged to the _no repetition_ class.

## 4 Discussion

Several qualitatively interesting examples of chiasmus were identified by our method, highlighting the richness of the Biblical texts and the alignment with existing literary scholarship. One notable example is Genesis 1:19-23, as shown in Table 3. This five-line chiasmus, positively identified by both annotators, exhibits clear lexical parallels between its paired sections. The chiostic structure here emphasizes the order and the rhetorical intentionality in the Creation narrative, underscoring God's repeated affirmation that His creation is "good". This example aligns with scholarly interpretations that highlight the poetic nature of the Creation account.

Other significant examples include the story of Jacob stealing Esau's birthright, where the chisatic structure reflects the tension and reversal of fortune between the brothers. Similarly, the account of Isaac and Abraham and the sacrificial lamb contains a chiasmus that heightens the dramatic and rheological impact of the narrative, as God intervenes at the critical moment.

The method also uncovered a clear chiasmus in God's covenant with Noah after the flood, where the repetitive structure emphasizes God's promise of restoration and the symbolic importance of the

Figure 1: **Total number of chiasm per Book at verse and half-verse level. While some books tend to have more chiasm overall, this figure shows whether verse-level or half-verse-level chiasm are more prevalent in each book.**



'bow' in the clouds. Additionally, in Ezekiel's poetic description of the image of the glory of the LORD, chinastic elements serve to enhance the vividness and majesty of the vision, a hallmark of Ezekiel's prophetic style. Illustrations of these chiasmi may be seen in appendix A.

Notably, many instances of God's reported speech are presented in chisatic or poetic form, which may suggest an intentional literary quality meant to convey authority and solemnity. These findings further support the hypothesis that chiasmus is often employed for rhetorical and rheological purposes in Bibilical texts.

## 5 Conclusion

Our approach demonstrates the ability to uncover intricate literary patterns that might otherwise be overlooked, providing valuable insights for scholars of Biblical texts, politicaloratory, and literary studies. This example, along with our overall findings, underscores the importance of advanced computational techniques in literary analysis and supports the broader application of our method for discovering chiasm across various texts and translations. One future step is using a chiasmu detection method to create a labeled corpus of chiasmi within the Bible, particularly the Psalms, for scholarly exploration.

### Limitations

In this study, we only investigate chistuctures at the verse-level and half-verse-level. However, chiasmi can also be identified at the narrative level, where narrative segments _topically_ form a chisatic plot structure, such as the narrative of the flood in Genesis. We exclude this type since it exhibits many fewer lexical features and is overall less precisely defined in the scholarship.

## Acknowledgments

We would like to thank scholars at Tyndale House, especially Ellie Wiener and Caleb J. Howard, for useful discussions about Biblical Hebrew. Thanks to Andrew Canes for his helpful comments on previous drafts. Hope McGoverm's work is supported by the Woolf Institute for Interfaith Relations and the Cambridge Trust

## References

* Assis (2002) Elie Assis. 2002. Chiasmus in biblical narrative: Rhetoric of characterization. _Prooftexts_, 22(3):273-304.
* Balloccu et al. (2018) Simone Balloccu, Patricia Schmidtova, Mateusz Lango, and Ondr'ej Dusek. Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs.
* Bothwell et al. (2023) Stephen Bothwell, Justin DeBenedetto, Theresa Crnkovich, Hildegund Muller, and David Chiang. 2023. Introducing rhetorical parallelism detection: A new task with datasets, metrics, and baselines. In _Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing_, pages 5007-5039, Singapore. Association for Computational Linguistics.
* Burns et al. (2021) Patrick J Burns, James A Brofos, Kyle Li, Pramit Chaudhuri, and Joseph P Dexter. 2021. Profiling of intertextuality in lattin literature using word embeddings. In _Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_, pages 4900-4907.
* Dubremetz and Nivre (2015) Marie Dubremetz and Joakim Nivre. 2015. Rheterrical figure detection: the case of chiasmus. In _Proceedings of the Fourth Workshop on Computational Linguistics for Literature_, pages 23-31, Denver, Colorado, USA. Association for Computational Linguistics.
* Dubremetz and Schmitowski (2016)

\begin{table}
\begin{tabular}{c c} \hline \hline A & And there was evening and there was morning, the fourth day. \\ B & And God said, “Let the waters swarm with swarms of living creatures, and let birds fly above the earth across the expanse of the heavens.” \\  & So God created the great sea creatures and every living creature that moves, with which the waters swarm, according to their kinds, and every winged bird according to its kind. And God saw that it was good. \\ B’ & And God blessed them, saying, “Be fruitful and multiply and fill the waters in the seas, and let birds multiply on the earth.” \\ A’ & And there was evening and there was morning, the fifth day. \\ \hline \hline \end{tabular}
\end{table}
Table 3: **English translation of a positive example of a chiasmus automatically detected by our method.** Gen 1:19-23 (ESV) 

Marie Dubremetz and Joakim Nivre. 2017. Machine learning for rhetorical figure detection: More chiasm with less annotation. In _Proceedings of the 21st Nordic Conference on Computational Linguistics_, pages 37-45, Gothenburg, Sweden. Association for Computational Linguistics.
* Lowth (1839) Robert Lowth. 1839. _Lectures on the Sacred Poetry of the Hebrews_, 4th edition. Kessinger Publishing. Originally delivered in Latin as lectures at the University of Oxford in 1741 (ISBN 0-7661-8855-8).
* Martin (2018) Lee Roy Martin. 2018. The chisatic structure of psalm 106. _Old Testament Essays_, 31(3).
* McCoy (2003) Brad McCoy. 2003. Chiasmus: An Important Structural Device Commonly Found in Biblical Literature. _CTS Journal_, 9:18-34.
* Schneider et al. (2021) Felix Schneider, Bjorn Barz, Phillip Brandes, Sophie Marshall, and Joachim Denzler. 2021. Data-driven detection of general chiasmi using lexical and semantic features. In _Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature_, pages 96-100, Punta Cana, Dominican Republic (online). Association for Computational Linguistics.
* Wang et al. (2024) Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and Furu Wei. 2024. Multilingual e5 text embeddings: A technical report. _arXiv preprint arXiv:2402.05672_.
* Welch (1981) J.W. Welch. 1981. _Chiasmus in Antiquity: Structures, Analyses, Exegesis_. Gerstenberg.

 



## Appendix A Chiasms Referenced in the Discussion Section

\begin{tabular}{l l} \hline \hline A & And Esau said to Jacob, “Let me eat some of that red stew, for I am exhausted!” \\  & (Therefore his name was called Edom.) \\ B & Jacob said, “Sell me your birthright now.” \\ C & Esau said, “I am about to die; of what use is a birthright to me?” \\ B’ & Jacob said, “Swear to me now." So he swore to him and sold his birthright to \\  & Jacob. \\ A’ & Then Jacob gave Esau bread and lentil stew, and he ate and drank and rose and \\  & went his way. Thus Esau despised his birthright. \\ \hline \hline \end{tabular}

\begin{table}
\begin{tabular}{c p{142.3pt}} \hline \hline A & And God said, “This is the sign of the covenant that I make between me and you and \\  & every living creature that is with you, for all future generations: \\ B & I have set my bow in the cloud, and it shall be a sign of the covenant \\  & between me and the earth. \\ B’ & When I bring clouds over the earth and the bow is seen in the clouds, \\ A’ & I will remember my covenant that is between me and you and every living creature of \\  & all flesh. And the waters shall never again become a flood to destroy all flesh. \\ \hline \hline \end{tabular}
\end{table}
Table 5: **God’s covenant with Noah after the flood. English translation of a chiasmus automatically detected by our method. Gen 9:12-15 (ESV)**