Essential Readings

Multidisciplinary references:

• Philosophy of Language and Cognitive Metaphor theories and discussion:  

  • The Conceptual metaphor theory (CMT) 

    • George Lakoff and Mark Johnson. 1980. Metaphors we Live by. University of Chicago Press, Chicago.

    • Lakoff, G. (1993). The contemporary theory of metaphor. In Metaphor and Thought (pp. 202–251). Cambridge University Press.

    • Osaka University & Sugimoto, M. (2025). Master Metaphor List. https://www.lang.osaka-u.ac.jp/~sugimoto/MasterMetaphorList/MetaphorHome.html 

    • Zoltán Kövecses. 2020. Extended Conceptual Metaphor Theory. Cambridge University Press.

  • Structural Mapping Theory (SMT) and metaphors:

    • Brian Bowdle and Dedre Gentner. 2005. The career of metaphor. Psychological review, 112:193–216.

  • Neural Metaphor theory:

    • Jerome Feldman. 2006. From Molecule to Metaphor: A Neural Theory of Language. The MIT Press.

  • General theoretical discussions for early and more recent theories: 

    • Paul Ricœur. 1977. The rule of metaphor: Multi-disciplinary studies of the creation of meaning in language, volume 37 of University of Toronto romance series. University of Toronto Press, Toronto.

    • Raymond W. Gibbs, Jr. 2017. Metaphor Wars. Cambridge University Press.

    • Keith J. Holyoak and Dusan Stamenkovic. 2018. Metaphor comprehension: A critical review of theories and evidence. Psychological Bulletin, 144:641–671.

• Corpus linguistics, discourse and communication theories and studies: 

  • L. Cameron and A. Deignan. 2006. The emergence of metaphor in discourse. Applied linguistics, 27(4):671–690.

  • Deignan, A., Littlemore, J., & Semino, E. (2013). Figurative language, genre and register. (Cambridge Applied Linguistics). Cambridge University Press.

  • Finding Metaphor in Grammar and Usage / Steen, Gerard J. - Amsterdam : John Benjamins Publishing Company, 2007 - 446 p. - Converging Evidence in Language and Communication Research

  • Steen Gerard J.  (2023) Thinking by metaphor, fast and slow: Deliberate Metaphor Theory offers a new model for metaphor and its comprehension. Front. Psychol. 

  • Charteris-Black, J. (2004). Critical Metaphor Analysis. In: Corpus Approaches to Critical Metaphor Analysis. Palgrave Macmillan, London. 

  • Charteris-Black, J. (2011) Politicians and Rhetoric : The Persuasive Power of Metaphor. 2nd ed. 2011. London: Palgrave Macmillan UK. 

• MIPVU Annotation scheme: 

  • Gerard Steen. 2010. A method for linguistic metaphor identification: from MIP to MIPVU, volume v. 14 of Converging evidence in language and communication research. John Benjamins Pub. Co., Amsterdam.

  • Extended to more languages:  Susan Nacey, Aletta G. Dorst, Tina Krennmayr, and W. Gudrun Reijnierse, editors. 2019. Metaphor Identification in Multiple Languages: MIPVU around the world. John Benjamins.

• Multimodal Metaphor:

  • Forceville, C. (2024). Identifying and Interpreting Visual and Multimodal Metaphor in Commercials and Feature Films. Metaphor and Symbol, 39(1), 40–54. https://doi.org/10.1080/10926488.2023.2271544

  • Saakyan, A., Kulkarni, S., Chakrabarty, T., & Muresan, S. (2025). Understanding figurative meaning through explainable visual entailment. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 1–23). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.naacl-long.1

  • Li, B., Zhang, K., Zhang, H., Guo, D., Zhang, R., Li, F., Zhang, Y., Liu, Z., & Li, C. (2024). LLaVA-NeXT: Stronger LLMs supercharge multimodal capabilities in the wild. https://llava-vl.github.io/blog/2024-05-10-llava-next-stronger-llms/

  • Tschannen, M., Gritsenko, A., Wang, X., Naeem, M. F., Alabdulmohsin, I., Parthasarathy, N., Evans, T., Beyer, L., Xia, Y., Mustafa, B., Hénaff, O., Harmsen, J., Steiner, A., & Zhai, X. (2025). SigLIP 2: Multilingual vision-language encoders with improved semantic understanding, localization, and dense features. arXiv:2502.14786. https://arxiv.org/abs/2502.14786

  • Zhai, X., Mustafa, B., Kolesnikov, A., & Beyer, L. (2023). Sigmoid loss for language image pre-training. Proceedings of the IEEE/CVF International Conference on Computer Vision, 11975–11986.

  • Lin, Z., Pathak, D., Li, B., Li, J., Xia, X., Neubig, G., Zhang, P., & Ramanan, D. (2024). Evaluating text-to-visual generation with image-to-text generation. In Proceedings of the European Conference on Computer Vision (ECCV 2024). https://doi.org/10.1007/978-3-031-72673-6_20

  • Bai, S., Chen, K., Liu, X., Wang, J., Ge, W., Song, S., Dang, K., Wang, P., Wang, S., Tang, J., Zhong, H., Zhu, Y., Yang, M., Li, Z., Wan, J., Wang, P., Ding, W., Fu, Z., Xu, Y., … Lin, J. (2025). Qwen2.5-VL technical report. arXiv:2502.13923. https://arxiv.org/abs/2502.13923

References of NLP works on metaphors:

Metaphor Generation

• Jones, M. A. (1992). Generating metaphors from networks. In Proceedings of the 14th International Conference on Computational Linguistics (COLING 1992) (pp. 1066–1070).

• Abe, K., Sakamoto, K., & Nakagawa, M. (2006). A computational model of the metaphor generation process. In Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 937–942).

• Terai, A., & Nakagawa, M. (2010). A computational system of metaphor generation with evaluation mechanism. In Proceedings of the 20th International Conference on Artificial Neural Networks (ICANN 2010). Springer.

• Veale, T. (2016). Round up the usual suspects: Knowledge-based metaphor generation. In Proceedings of the Fourth Workshop on Metaphor in NLP (pp. 34–41).

• Stowe, K., Chakrabarty, T., Peng, N., Muresan, S., & Gurevych, I. (2021). Metaphor generation with conceptual mappings. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (pp. 6724–6736).

• Lai, H., & Nissim, M. (2022). Multi-figurative language generation. In Proceedings of the 29th International Conference on Computational Linguistics (pp. 5939–5954). 

• Chakrabarty, T., Choi, Y., & Shwartz, V. (2022). It's not Rocket Science: Interpreting figurative language in narratives. Transactions of the Association for Computational Linguistics, 10, 589–606.

• Reif, E., Ippolito, D., Yuan, A., Coenen, A., Callison-Burch, C., & Wei, J. (2022). A recipe for arbitrary text style transfer with large language models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Short Papers, pp. 837–848).

• Broad surveys:

  • Tony Veale, Ekaterina Shutova, and Beata Beigman Klebanov. 2016. Metaphor: A computational perspective. Morgan & Claypool Publishers.

  • Mengshi Ge, Rui Mao, and Erik Cambria. 2023. A survey on computational metaphor processing techniques: From identification, interpretation, generation to application. Artificial Intelligence Review, 56(02):1829–1895.

• Metaphor Detection: 

  • Wilks, Y. (1975). A preferential, pattern-seeking semantics for natural language inference. Artificial Intelligence, 6(1), 53–74.

  • Wilks, Y. (1983). Preference semantics, ill-formedness, and metaphor. American Journal of Computational Linguistics, 9(3–4), 178–187.

  • Fass, D. (1991). met*: A method for discriminating metonymy and metaphor by computer. Computational Linguistics, 17(1), 49–90.

  • Mason, Z. J. (2004). CorMet: A computational, corpus-based conventional metaphor extraction system. Computational Linguistics, 30(1), 23–44.

  • Krishnakumaran, S., & Zhu, X. (2007). Hunting elusive metaphors using lexical resources. In Proceedings of the Workshop on Computational Approaches to Figurative Language (pp. 13–20). 

  • Shutova, E., & Teufel, S. (2010). Metaphor corpus annotated for source and target domains. In Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC 2010).

  • Hovy, D., Sayeed, A., Coll Ardanuy, M., Bhatt, U., Bhatt, R., & Nastase, V. (2013). Identifying metaphorical word use with tree kernels. In Proceedings of the First Workshop on Metaphor in NLP (pp. 52–57).

  • Tsvetkov, Y., Boytsov, L., Gershman, A., Nyberg, E., & Dyer, C. (2014). Metaphor detection with cross-lingual model transfer. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (pp. 248–258).

  • Mao, R., Lin, C., & Guerin, F. (2018). Word embedding and WordNet based metaphor identification and interpretation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (pp. 1222–1231).

  • Li, Y., Wang, S., Lin, C., & Guerin, F. (2023). Metaphor detection via explicit basic meanings modelling. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Short Papers, pp. 91–100). 

  • Leong, C. W., Beigman Klebanov, B., & Shutova, E. (2018). A report on the 2018 VUA metaphor detection shared task. In Proceedings of the Workshop on Figurative Language Processing, NAACL 2018 (pp. 56–66). ACL.

  • Leong, C. W., Beigman Klebanov, B., Hamill, C., Stemle, E., Ubale, R., & Chen, X. (2020). A report on the 2020 VUA and TOEFL metaphor detection shared task. In Proceedings of the Second Workshop on Figurative Language Processing, ACL 2020 (pp. 18–29). ACL.

  • Swarnkar, K., & Singh, A. K. (2018). Di-LSTM contrast: A deep neural network for metaphor detection. In Proceedings of the Workshop on Figurative Language Processing, NAACL 2018 (pp. 115–120). ACL.

  • Gao, G., Choi, E., Choi, Y., & Zettlemoyer, L. (2018). Neural metaphor detection in context. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 607–613). ACL.

  • Zeng, W., & Bhat, R. S. (2021). Conceptual metaphor detection with a Chinese dataset and verbal metaphors. In Proceedings of the 3rd Workshop on Figurative Language Processing, EMNLP 2021 (pp. 9–20). ACL.

  • Gallese, V., & Lakoff, G. (2005). The brain's concepts: The role of the sensory-motor system in conceptual knowledge. Cognitive Neuropsychology, 22(3), 455–479.

  • Feldman, J. (2006). From Molecule to Metaphor: A Neural Theory of Language. MIT Press.

  • Desai, R. H. (2021). Are metaphors embodied? The neural evidence. In T. Oakley & E. W. Jensen (Eds.), Metaphor: Embodied Cognition and Discourse. Cambridge University Press.

  • Li, X., et al. (2024). EmbodiedBERT: Cognitively informed metaphor detection incorporating sensorimotor information. arXiv preprint, arXiv:2411.18260.

Metaphor Interpretation

• Barnden, J. A., & Lee, M. G. (2002). Reasoning in metaphor understanding: The ATT-Meta approach and system. In Proceedings of COLING 2002 (pp. 1–7).

• Agerri, R. (2008). Metaphor in textual entailment. In Proceedings of COLING 2008 (pp. 4–8).

• Shutova, E. (2010). Automatic metaphor interpretation as a paraphrasing task. In Proceedings of Human Language Technologies: NAACL 2010 (pp. 1029–1037). 

• Shutova, E., Teufel, S., & Korhonen, A. (2013). Statistical metaphor processing. Computational Linguistics, 39(2), 301–353.

• Mohler, M. (2013). Semantic and relational similarity for textual entailment. In Proceedings of the Second Joint Conference on Lexical and Computational Semantics (*SEM 2013) (pp. 160–168).

• Chakrabarty, T., Ghosh, S., & Muresan, S. (2021a). MERMAID: Metaphor generation with symbolism and discriminative decoding. In Proceedings of NAACL 2021 (pp. 4250–4261).

• Chakrabarty, T., Ghosh, S., Poliak, A., & Muresan, S. (2021b). Figurative language in recognizing textual entailment. In Findings of ACL 2021 (pp. 3354–3361). ACL.

• Stowe, K., Utama, P., & Gurevych, I. (2021). Exploring metaphoric paraphrase generation. In Proceedings of CoNLL 2021.

• Rakshit, G., & Flanigan, J. (2022). FigurativeQA: A test benchmark for figurativeness comprehension for question answering. In Proceedings of the 3rd Workshop on Figurative Language Processing (FLP), EMNLP 2022 (pp. 160–166). ACL.aclanthology

• Rakshit, G., & Flanigan, J. (2023). Does the "most sinfully decadent cake ever" taste good? Answering yes/no questions from figurative contexts. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing (RANLP 2023).aclanthology

• Comșa, I.-M., et al. (2022). Metaphor interpretation as commonsense reasoning. In Proceedings of ACL 2022 (pp. 1–11).

• Liu, J., et al. (2022). Fig-QA: Figurative language question answering. arXiv preprint, arXiv:2210.07993.

• Stowe, K., Utama, P., & Gurevych, I. (2021). Exploring metaphoric paraphrase generation. In Proceedings of CoNLL 2021 (pp. 277–291).

• Stowe, K., Zilka, R., & Gurevych, I. (2022). IMPLI: Investigating NLI models' performance on figurative language. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (pp. 5375–5388).

• Sanchez-Bayona, E., & Agerri, R. (2026). Meta4XNLI: A cross-lingual parallel corpus for metaphor detection and interpretation. Computational Linguistics, 52(1), 191–235.

• Prystawski, B., Thibodeau, P., Potts, C., & Goodman, N. D. (2022). Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models. arXiv preprint, arXiv:2209.08141.

• Tong, X., Choenni, R., Lewis, M., & Shutova, E. (2024). Metaphor understanding challenge dataset for LLMs (MUNCH). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (pp. 3517–3536). ACL.

• Sanchez-Bayona, E., & Agerri, R. (2025). Metaphor and large language models: When surface features matter more than deep understanding. In Findings of the Association for Computational Linguistics: ACL 2025 (pp. 17462–17477). ACL.

• Ye, F., Wang, S., Chao, L. S., & Wong, D. F. (2025). Unveiling LLMs' metaphorical understanding: Exploring conceptual irrelevance, context leveraging and syntactic influence. arXiv preprint, arXiv:2510.04120.

• Analogical Reasoning: 

  • Melanie Mitchell. 2021. Abstraction and analogy-making in artificial intelligence. Annals of the New York Academy of Sciences, 1505(1):79–101

  • Taylor Webb, Keith J. Holyoak, and Hongjing Lu. 2023. Emergent analogical reasoning in large language models.

• Metaphor identification online system

  • Rui Mao, Xiao Li, Kai He, Mengshi Ge, and Erik Cambria. 2023. MetaPro online: A computational metaphor processing online system. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 127–135, Toronto, Canada. Association for Computational Linguistics.

• Metaphor processing with LLMs: 

  • Lennart Wachowiak and Dagmar Gromann. 2023. Does GPT-3 Grasp Metaphors? Identifying Metaphor Mappings with Generative Language Models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1018–1032, Toronto, Canada. Association for Computational Linguistics.

  •  Antonio Lieto, Gian Luca Pozzato, and Stefano Zoia. 2025. The delta of thought: Channeling rivers of commonsense knowledge in the sea of metaphorical interpretations. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25, pages 10316–10324. International Joint Conferences on Artificial Intelligence Organization. Human-Centred AI.

• Multilinguality: 

  • Ekaterina Shutova, Lin Sun, Elkin Darío Gutiérrez, Patricia Lichtenstein, and Srini Narayanan. 2017. Multilingual metaphor processing: Experiments with semi-supervised and unsupervised learning. Computational Linguistics, 43(1):71–123.

  • Ehsan Aghazadeh, Mohsen Fayyaz, and Yadollah Yaghoobzadeh. 2022. Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2037–2050, Dublin, Ireland. Association for Computational Linguistics.

  • Wang et al. (2024) 

• Idiom translation:

  •  Verna Dankers, Christopher Lucas, and Ivan Titov. 2022. Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3608–3626, Dublin, Ireland. Association for Computational Linguistics.

  •  Emmy Liu, Aditi Chaudhary, and Graham Neubig. 2023. Crossing the threshold: Idiomatic machine translation through retrieval augmentation and loss weighting. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15095–15111, Singapore. Association for Computational Linguistics.

• Connections between NLP and neuroscience

  • Yuchen Zhou, Emmy Liu, Graham Neubig, Michael J. Tarr, and Leila Wehbe. 2024. Divergences between language models and human brains. In Conference on Neural Information Processing Systems (NeurIPS), Vancouver, BC.