Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-745bb68f8f-mzp66 Total loading time: 0 Render date: 2025-01-14T10:33:41.415Z Has data issue: false hasContentIssue false
Series:   Elements in Semantics

Semantics and Deep Learning

Published online by Cambridge University Press:  16 December 2024

Lasha Abzianidze ,
Lisa Bylinina  and
Denis Paperno
Lasha Abzianidze
Affiliation:
Utrecht University
Lisa Bylinina
Affiliation:
Utrecht University
Denis Paperno
Affiliation:
Utrecht University

Summary

This Element covers the interaction of two research areas: linguistic semantics and deep learning. It focuses on three phenomena central to natural language interpretation: reasoning and inference; compositionality; extralinguistic grounding. Representation of these phenomena in recent neural models is discussed, along with the quality of these representations and ways to evaluate them (datasets, tests, measures). The Element closes with suggestions on possible deeper interactions between theoretical semantics and language technology based on deep learning models.
Get access

Keywords

compositionalitygroundinginferencedeep learninglarge language models
Type
Element
Information
Series: Elements in Semantics
Online ISBN: 9781009542340
Publisher: Cambridge University Press
Print publication: 16 January 2025

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdou, M., Kulmizev, A., Hershcovich, D., et al. (2021). Can language models encode perceptual structure without grounding? A case study in color. arXiv:2109.06129.Google Scholar
Abusch, D. (2020). Possible-worlds semantics for pictures. In Gutzmann, D., Matthewson, L., Meier, C., et al., eds., The Wiley Blackwell companion to semantics (pp. 1–31). Wiley Blackwell.Google Scholar
Abzianidze, L. (2016). Natural solution to fracas entailment problems. In Proceedings of *SEM (pp. 64–74). ACL.Google Scholar
Abzianidze, L., Bjerva, J., Evang, K., et al. (2017). The Parallel Meaning Bank: Towards a multilingual corpus of translations annotated with compositional meaning representations. In Proceedings of EACL (pp. 242–247). ACL.Google Scholar
Abzianidze, L., Zwarts, J., & Winter, Y. (2023). SpaceNLI: Evaluating the consistency of predicting inferences in space. In Proceedings of NALOMA (pp. 12–24). ACL.Google Scholar
Agirre, E., Cer, D., Diab, M., & Gonzalez-Agirre, A. (2012). Semeval-2012 task 6: A pilot on semantic textual similarity. In Proceedings of Semeval (pp. 385–393).Google Scholar
Akyürek, E., & Andreas, J. (2022). Compositionality as lexical symmetry. arXiv:2201.12926.Google Scholar
Andreas, J. (2019a). Good-enough compositional data augmentation. arXiv:1904.09545.CrossRefGoogle Scholar
Andreas, J. (2019b). Measuring compositionality in representation learning. In International conference on learning representations. https://openreview.net/forum?id=HJz05o0qK7.Google Scholar
Antol, S., Agrawal, A., Lu, J., et al. (2015). Vqa: Visual question answering. In Proceedings of IEEE/CVFICCV (pp. 2425–2433).Google Scholar
Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv:1409.0473.Google Scholar
Banarescu, L., Bonial, C., Cai, S., et al. (2013). Abstract meaning representation for sembanking. In Proceedings of the 7th linguistic annotation workshop and interoperability with discourse (pp. 178–186). ACL.Google Scholar
Baroni, M. (2016). Grounding distributional semantics in the visual world. Language and Linguistics Compass, 10(1), 3–13.CrossRefGoogle Scholar
Baroni, M. (2022). On the proper role of linguistically-oriented deep net analysis in linguistic theorizing. In Lappin, S., ed., Algebraic systems and the representation of linguistic knowledge (pp. 5–22). Taylor and Francis.Google Scholar
Baroni, M., Dinu, G., & Kruszewski, G. (2014). Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. In Proceedings of ACL (pp. 238–247). ACL.Google Scholar
Baroni, M., & Zamparelli, R. (2010). Nouns are vectors, adjectives are matrices: Representing adjective–noun constructions in semantic space. In Proceedings of EMNLP (pp. 1183–1193).Google Scholar
Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59(1), 617–645.CrossRefGoogle ScholarPubMed
Bender, E. M., & Koller, A. (2020). Climbing towards NLU: On meaning, form, and understanding in the age of data. In Proceedings of ACL (pp. 5185–5198). ACL.Google Scholar
Bernardi, R., Dinu, G., Marelli, M., & Baroni, M. (2013). A relatedness benchmark to test the role of determiners in compositional distributional semantics. In Proceedings of ACL (pp. 53–57). ACL.Google Scholar
Bernardi, R., & Pezzelle, S. (2021). Linguistic issues behind visual question answering. Language and Linguistics Compass, 15(6), e12417.CrossRefGoogle ScholarPubMed
Bernardy, J.-P. (2018). Can recurrent neural networks learn nested recursion? Linguistic Issues in Language Technology, 16(1). https://aclanthology.org/2018.lilt-16.1.CrossRefGoogle Scholar
Bernardy, J.-P., & Chatzikyriakidis, S. (2021). Applied temporal analysis: A complete run of the FraCaS test suite. In Proceedings of IWCS (pp. 11–20). ACL.Google Scholar
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5, 135–146.CrossRefGoogle Scholar
Boleda, G., Baroni, M., McNally, L., et al. (2013). Intensionality was only alleged: On adjective-noun composition in distributional semantics. In Proceedings of IWCS.Google Scholar
Botha, J., & Blunsom, P. (2014). Compositional morphology for word representations and language modelling. In Xing, E. P. & Jebara, T., eds., Proceedings of the 31st international conference on machine learning (Vol. 32, no. 2) (pp. 1899–1907). https://proceedings.mlr.press/v32/botha14.html.Google Scholar
Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015). A large annotated corpus for learning natural language inference. In Proceedings of EMNLP (pp. 632–642).CrossRefGoogle Scholar
Bowman, S. R., & Dahl, G. (2021). What will it take to fix benchmarking in natural language understanding? In Proceedings of NAACL. ACL.Google Scholar
Brown, T., Mann, B., Ryder, N., et al. (2020). Language models are few-shot learners. In Larochelle, H., Ranzato, M., Hadsell, R., et al., eds., Advances in neural information processing systems (Vol. 33, pp. 1877–1901). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf.Google Scholar
Burgess, C., & Lund, K. (1995). Hyperspace analogue to language (HAL): A general model of semantic memory. In Annual meeting of the psychonomic society.Google Scholar
Bylinina, L., & Tikhonov, A. (2022). The driving forces of polarity-sensitivity: Experiments with multilingual pre-trained neural language models. In Proceedings of COGSCI (Vol. 44).Google Scholar
Cer, D., Yang, Y., Kong, S.-Y., et al. (2018). Universal sentence encoder. arXiv:1803.11175.Google Scholar
Chaabouni, R., Dessì, R., & Kharitonov, E. (2021). Can Transformers jump around right in natural language? Assessing performance transfer from scan. In Proceedings of blackboxnlp (pp. 136–148).Google Scholar
Chaabouni, R., Kharitonov, E., Bouchacourt, D., et al. (2020). Compositionality and generalization in emergent languages. arXiv:2004.09124.Google Scholar
Chan, S. C., Santoro, A., Lampinen, A. K., et al. (2022). Data distributional properties drive emergent few-shot learning in transformers. arXiv:2205.05055.Google Scholar
Chatzikyriakidis, S., Cooper, R., Dobnik, S., & Larsson, S. (2017). An overview of natural language inference data collection: The way forward? In Proceedings of the computing natural language inference workshop.Google Scholar
Chen, T., Jiang, Z., Poliak, A., et al. (2020). Uncertain natural language inference. In Proceedings of ACL. ACL.CrossRefGoogle Scholar
Chen, Z. (2021). Attentive tree-structured network for monotonicity reasoning. In Proceedings of NALOMA (pp. 12–21). ACL.Google Scholar
Chen, Z., Gao, Q., & Moss, L. S. (2021). NeuralLog: Natural language inference with joint neural and logical reasoning. In Proceedings of *SEM. ACL.Google Scholar
Cho, K., Van Merriënboer, B., Gulcehre, C., et al. (2014). Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv:1406.1078.Google Scholar
Chowdhery, A., Narang, S., Devlin, J., et al. (2022). Palm: Scaling language modeling with pathways. arXiv:2204.02311.Google Scholar
Clark, H. H. (1996). Using language. Cambridge University Press.CrossRefGoogle Scholar
Clark, P., Tafjord, O., & Richardson, K. (2021). Transformers as soft reasoners over language. In Proceedings of IJCAI.Google Scholar
Clark, S., Coecke, B., & Sadrzadeh, M. (2008). A compositional distributional model of meaning. In Proceedings of the second quantum interaction symposium (qi-2008) (pp. 133–140).Google Scholar
Condoravdi, C., Crouch, D., de Paiva, V., et al. (2003). Entailment, intensionality and text understanding. In Proceedings of the HLT-NAACL 2003 workshop on text meaning (pp. 38–45).CrossRefGoogle Scholar
Cooper, R., Crouch, D., Eijck, J. V., et al. (1996). Fracas: A framework for computational semantics. Deliverable D16.Google Scholar
Coppock, E., & Champollion, L. (2022). Invitation to formal semantics. Manuscript, Boston University and New York University.Google Scholar
Dagan, I., Glickman, O., & Magnini, B. (2006). The Pascal recognising textual entailment challenge. In Proceedings of the Pascal challenges workshop on recognising textual entailment (pp. 177–190). Springer.Google Scholar
Dagan, I., Roth, D., Sammons, M., & Zanzotto, F. M. (2013). Recognizing textual entailment: Models and applications. Morgan & Claypool.CrossRefGoogle Scholar
Dalvi, B., Jansen, P., Tafjord, O., et al. (2021). Explaining answers with entailment trees. In Proceedings of EMNLP (pp. 7358–7370). ACL.Google Scholar
Dankers, V., Bruni, E., & Hupkes, D. (2022). The paradox of the compositionality of natural language: A neural machine translation case study. In Proceedings of ACL (pp. 4154–4175). ACL.Google Scholar
Davis, F. (2022). On the limitations of data: Mismatches between neural models of language and humans (Unpublished doctoral dissertation). Cornell University.Google Scholar
de Deyne, S., Navarro, D. J., Collell, G., & Perfors, A. (2021). Visual and affective multimodal models of word meaning in language and mind. Cognitive Science, 45(1). 1–44. https://onlinelibrary.wiley.com/doi/epdf/10.1111/cogs.12922.CrossRefGoogle ScholarPubMed
de Marneffe, M.-C., Rafferty, A. N., & Manning, C. D. (2008). Finding contradictions in text. In Proceedings of ACL (pp. 1039–1047). ACL.Google Scholar
de Marneffe, M.-C., Simons, M., & Tonhauser, J. (2019). The commitmentbank: Investigating projection in naturally occurring discourse. Proceedings of Sinn und Bedeutung, 23(2), 107–124.Google Scholar
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Burstein, J., Doran, C., & Solorio, T., eds., Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: Human language technologies, volume 1 (long and short papers) (pp. 4171–4186). Association for Computational Linguistics. https://aclanthology.org/N19-1423. https://doi.org/10.18653/v1/N19-1423.Google Scholar
Dima, C., de Kok, D., Witte, N., & Hinrichs, E. (2019). No word is an island: A transformation weighting model for semantic composition. Transactions of the Association for Computational Linguistics, 7, 437–451.CrossRefGoogle Scholar
Drozdov, A., Schärli, N., Akyuürek, E., et al. (2022). Compositional semantic parsing with large language models. arXiv:2209.15003.Google Scholar
Du, L., Ding, X., Xiong, K., Liu, T., & Qin, B. (2022). Enhancing pretrained language models with structured commonsense knowledge for textual inference. Knowledge-Based Systems, 109488.CrossRefGoogle Scholar
Du, Y., Li, S., & Mordatch, I. (2020). Compositional visual generation with energy based models. In Neurips (Vol. 33, pp. 6637–6647). Curran Associates, Inc.Google Scholar
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.CrossRefGoogle Scholar
Ettinger, A. (2020). What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models. Transactions of the Association for Computational Linguistics, 8, 34–48.CrossRefGoogle Scholar
Ettinger, A., Elgohary, A., Phillips, C., & Resnik, P. (2018). Assessing composition in sentence vector representations. In Proceedings of Coling (pp. 1790–1801). ACL.Google Scholar
Fitch, F. B. (1973). Natural deduction rules for English. Philosophical Studies, 24(2), 89–104.CrossRefGoogle Scholar
Frank, S., Bugliarello, E., & Elliott, D. (2021). Vision-and-language or vision-for-language? On cross-modal influence in multimodal transformers. In Proceedings of EMNLP (pp. 9847–9857). Association for Computational Linguistics.Google Scholar
Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A. H., Chechik, G., & Cohen-Or, D. (2022). An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv.Google Scholar
Gamallo, P. (2021). Compositional distributional semantics with syntactic dependencies and selectional preferences. Applied Sciences, 11(12), 1–13.CrossRefGoogle Scholar
Gardenfors, P. (2004). Conceptual spaces as a framework for knowledge representation. Mind and Matter, 2(2), 9–27.Google Scholar
Gatti, D., Marelli, M., Vecchi, T., & Rinaldi, L. (2022). Spatial representations without spatial computations. Psychological Science, 33(11), 1947–1958. https://doi.org/10.1177/09567976221094863.CrossRefGoogle ScholarPubMed
Geiger, A., Cases, I., Karttunen, L., & Potts, C. (2018). Stress-testing neural models of natural language inference with multiply-quantified sentences. arXiv.Google Scholar
Geiger, A., Richardson, K., & Potts, C. (2020). Neural natural language inference models partially embed theories of lexical entailment and negation. In Proceedings of blackboxnlp (pp. 163–173).CrossRefGoogle Scholar
Giampiccolo, D., Magnini, B., Dagan, I., & Dolan, B. (2007). The third PASCAL recognizing textual entailment challenge. In Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing. ACL.Google Scholar
Gleitman, L. (1990). The structural sources of verb meanings. Language Acquisition, 1(1), 3–55.CrossRefGoogle Scholar
Gleitman, L. R., Cassidy, K., Nappa, R., Papafragou, A., & Trueswell, J. C. (2005). Hard words. Language Learning and Development, 1(1), 23–64.CrossRefGoogle Scholar
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., & Parikh, D. (2017). Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In Proceedings of IEEE/CVF CVPR (pp. 6904–6913).CrossRefGoogle Scholar
Greenberg, G. (2013). Beyond resemblance. Philosophical Review, 122(2).CrossRefGoogle Scholar
Greenberg, G. (2021). Semantics of pictorial space. Review of Philosophy and Psychology, 12(4), 847–887.CrossRefGoogle Scholar
Grefenstette, E., Dinu, G., Zhang, Y.- Z., et al. (2013). Multi-step regression learning for compositional distributional semantics. arXiv:1301.6939.Google Scholar
Guevara, E. R. (2011). Computing semantic compositionality in distributional semantics. In Proceedings of the ninth international conference on computational semantics (pp. 135–144).Google Scholar
Gururangan, S., Swayamdipta, S., Levy, O., et al. (2018). Annotation artifacts in natural language inference data. In Proceedings of NAACL (pp. 107–112). ACL.Google Scholar
Guu, K., Lee, K., Tung, Z., et al. (2020). Realm: Retrieval-augmented language model pre-training. arXiv.Google Scholar
Hacquard, V., & Lidz, J. (2022). On the acquisition of attitude verbs. Annual Review of Linguistics, 8, 193–212.CrossRefGoogle Scholar
Harnad, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1–3), 335–346.CrossRefGoogle Scholar
Harris, R. A. (1993). The linguistics wars. Oxford University Press on Demand.CrossRefGoogle Scholar
Hartmann, M., de Lhoneux, M., Hershcovich, D., et al. (2021). A multilingual benchmark for probing negation-awareness with minimal pairs. In Proceedings of CONLL (pp. 244–257). ACL.Google Scholar
Hawthorne, C., Jaegle, A., Cangea, C., et al. (2022). General-purpose, long-context autoregressive modeling with perceiver ar. arXiv:2202.07765.Google Scholar
He, Q., Wang, H., & Zhang, Y. (2020). Enhancing generalization in natural language inference by syntax. In Findings of EMNLP. ACL.Google Scholar
Hessel, J., & Lee, L. (2020). Does my multimodal model learn cross-modal interactions? It’s harder to tell than you might think! In Proceedings of EMNLP (pp. 861–877).Google Scholar
Hill, F., Cho, K., & Korhonen, A. (2016). Learning distributed representations of sentences from unlabelled data. In Proceedings of NAACL (pp. 1367–1377).CrossRefGoogle Scholar
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.CrossRefGoogle ScholarPubMed
Hofmann, V., Pierrehumbert, J. B., & Schütze, H. (2021). Superbizarre is not superb: Derivational morphology improves BERT’s interpretation of complex words. arXiv:2101.00403.Google Scholar
Hong, R., Liu, D., Mo, X., et al. (2019). Learning to compose and reason with language tree structures for visual grounding. IEEE transactions on pattern analysis and machine intelligence.Google Scholar
Hossain, M. M., Kovatchev, V., Dutta, P., et al. (2020). An analysis of natural language inference benchmarks through the lens of negation. In Proceedings of EMNLP (pp. 9106–9118). ACL.Google Scholar
Hu, H., Chen, Q., & Moss, L. (2019). Natural language inference with monotonicity. In Proceedings of IWCS (pp. 8–15). ACL.Google Scholar
Hudson, D. A., & Manning, C. D. (2018). Compositional attention networks for machine reasoning. In International Conference on Learning Representations.Google Scholar
Hudson, D. A., & Manning, C. D. (2019). Gqa: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of IEEE/CVF CVPR (pp. 6700–6709).CrossRefGoogle Scholar
Hupkes, D., Dankers, V., Mul, M., & Bruni, E. (2020). Compositionality decomposed: How do neural networks generalise? Journal of Artificial Intelligence Research, 67, 757–795.CrossRefGoogle Scholar
Hupkes, D., Giulianelli, M., Dankers, V., et al. (2022). State-of-the-art generalisation research in NLP: A taxonomy and review.Google Scholar
Hupkes, D., Veldhoen, S., & Zuidema, W. (2018). Visualisation and “diagnostic classifiers” reveal how recurrent and recursive neural networks process hierarchical structure. Journal of Artificial Intelligence Research, 61(1), 907–926.CrossRefGoogle Scholar
Icard, T. F. (2012). Inclusion and exclusion in natural language. Studia Logica, 100(4), 705–725.CrossRefGoogle Scholar
Icard, T. F., & Moss, L. S. (2014). Recent progress on monotonicity. LILT, 9.Google Scholar
Irsoy, O., & Cardie, C. (2014). Deep recursive neural networks for compositionality in language. NeurIPS, 27, 1–9.Google Scholar
Jeretic, P., Warstadt, A., Bhooshan, S., & Williams, A. (2020). Are natural language inference models IMPPRESsive? Learning IMPlicature and PRESupposition. In Proceedings of ACL (pp. 8690–8705). ACL.Google Scholar
Jiang, N., & de Marneffe, M.-C. (2019). Evaluating BERT for natural language inference: A case study on the CommitmentBank. In Proceedings of EMNLP–IJCNLP (pp. 6086–6091). ACL.Google Scholar
Jinman, Z., Zhong, S., Zhang, X., & Liang, Y. (2020). Pbos: Probabilistic bag-of-subwords for generalizing word embedding. arXiv:2010.10813.Google Scholar
Johnson, J., Hariharan, B., Van der Maaten, L., et al. (2017). Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In Proceedings of IEEE/CVF CVPR (pp. 2901–2910).CrossRefGoogle Scholar
Jumelet, J., Denic, M., Szymanik, J., et al. (2021). Language models use monotonicity to assess NPI licensing. In Findings of ACL–IJCNLP (pp. 4958–4969). ACL.Google Scholar
Kalouli, A.-L., Hu, H., Webb, A. F., et al. (2023). Curing the SICK and other NLI maladies. Computational Linguistics, 49(1), 199–243.CrossRefGoogle Scholar
Kalouli, A.-L., Real, L., & de Paiva, V. (2017). Textual inference: Getting logic from humans. In Proceedings of IWCS.Google Scholar
Kartsaklis, D., Sadrzadeh, M., & Pulman, S. (2013). Separating disambiguation from composition in distributional semantics. In Proceedings of CONLL (pp. 114–123). ACL.Google Scholar
Kassner, N., & Schütze, H. (2020). Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly. In Proceedings of ACL (pp. 7811–7818). ACL.Google Scholar
Khan, S., Naseer, M., Hayat, M., et al. (2021). Transformers in vision: A survey. ACM computing surveys (CSUR).Google Scholar
Kiela, D., Bulat, L., & Clark, S. (2015). Grounding semantics in olfactory perception. In Proceedings of ACL (pp. 231–236).CrossRefGoogle Scholar
Kim, N., & Linzen, T. (2020). Cogs: A compositional generalization challenge based on semantic interpretation. In Proceedings of EMNLP.CrossRefGoogle Scholar
Kim, N., & Schuster, S. (2023). Entity tracking in language models. In Proceedings of ACL (pp. 3835–3855). ACL.Google Scholar
Kirby, S., Cornish, H., & Smith, K. (2008). Cumulative cultural evolution in the laboratory: An experimental approach to the origins of structure in human language. Proceedings of the National Academy of Sciences, 105(31), 10681–10686.CrossRefGoogle Scholar
Kirby, S., Tamariz, M., Cornish, H., & Smith, K. (2015). Compression and communication in the cultural evolution of linguistic structure. Cognition, 141, 87–102.CrossRefGoogle ScholarPubMed
Kober, T., Bijl de Vroe, S., & Steedman, M. (2019). Temporal and aspectual entailment. In Proceedings of IWCS (pp. 103–119). ACL.Google Scholar
Kracht, M. (2011). Interpreted languages and compositionality (Vol. 89). Springer Science & Business Media.CrossRefGoogle Scholar
Kratzer, A., & Heim, I. (1998). Semantics in generative grammar (Vol. 1185). Blackwell Oxford.Google Scholar
Krishna, R., Zhu, Y., Groth, O., et al. (2017). Visual genome: Connecting language and vision using crowdsourced dense image annotations. International Journal of Computer Vision, 123(1), 32–73.CrossRefGoogle Scholar
Kudo, T., & Richardson, J. (2018). Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing.CrossRefGoogle Scholar
Lai, A., & Hockenmaier, J. (2014). Illinois-LH: A denotational and distributional approach to semantics. In Proceedings of SemEval (pp. 329–334). ACL.Google Scholar
Lake, B., & Baroni, M. (2018). Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks. In ICML.Google Scholar
Lakoff, G. (1970). Linguistics and natural logic. Synthese, 22(1), 151–271.CrossRefGoogle Scholar
Landau, B., & Gleitman, L. R. (1985). Language and experience: Evidence from the blind child. Harvard University Press.Google Scholar
Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104(2), 211–240.CrossRefGoogle Scholar
Lazaridou, A., Marelli, M., Zamparelli, R., & Baroni, M. (2013). Compositional-ly derived representations of morphologically complex words in distributional semantics. In Proceedings of ACL (pp. 1517–1526).Google Scholar
Le, P., & Zuidema, W. (2015). Compositional distributional semantics with long short term memory. arXiv:1503.02510.Google Scholar
Levy, O., & Goldberg, Y. (2014). Neural word embedding as implicit matrix factorization. NeurIPS, 27, 1–9.Google Scholar
Lewis, D. (1970). General semantics. Synthese, 22(1/2), 18–67.CrossRefGoogle Scholar
Li, B. Z., Nye, M., & Andreas, J. (2021). Implicit representations of meaning in neural language models. arXiv:2106.00737.Google Scholar
Li, F., Zhang, H., Zhang, Y.-F., et al. (2022). Vision-language intelligence: Tasks, representation learning, and large models. arXiv:2203.01922.Google Scholar
Li, L. H., Yatskar, M., Yin, D., et al. (2019). Visualbert: A simple and performant baseline for vision and language. arXiv:1908.03557.Google Scholar
Lin, T.-Y., Maire, M., Belongie, S., et al. (2014). Microsoft Coco: Common objects in context. In European conference on computer vision (pp. 740–755).Google Scholar
Lin, Z., Feng, M., dos Santos, C. N., et al. (2017). A structured self-attentive sentence embedding. In ICLR.Google Scholar
Linzen, T., & Baroni, M. (2021). Syntactic structure from deep learning. Annual Review of Linguistics, 7, 195–212.CrossRefGoogle Scholar
Liu, A., Wu, Z., Michael, J., et al. (2023). We’re afraid language models aren’t modeling ambiguity. In Bouamor, H., Pino, J., & Bali, K., eds., Proceedings of the 2023 conference on empirical methods in natural language processing (pp. 790–807). Association for Computational Linguistics. https://aclanthology.org/2023.emnlp-main.51. https://doi.org/10.18653/v1/2023.emnlp-main.51.CrossRefGoogle Scholar
Liu, Y., Ott, M., Goyal, N., et al. (2019). Roberta: A robustly optimized BERT pretraining approach. arXiv:1907.11692.Google Scholar
Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. NeurIPS.Google Scholar
Lu, J., Goswami, V., Rohrbach, M., et al. (2020). 12-in-1: Multi-task vision and language representation learning. In Proceedings of IEEE/CVF CVPR (pp. 10437–10446).Google Scholar
Luong, M.- T., Socher, R., & Manning, C. D. (2013). Better word representations with recursive neural networks for morphology. In Proceedings of CONLL.Google Scholar
MacCartney, B., & Manning, C. D. (2007). Natural logic for textual inference. In Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing (pp. 193–200). ACL.CrossRefGoogle Scholar
MacCartney, B., & Manning, C. D. (2009). An extended model of natural logic. In Proceedings of IWCS (pp. 140–156). ACL.Google Scholar
Mao, J., Huang, J., Toshev, A., et al. (2016). Generation and comprehension of unambiguous object descriptions. In Proceedings of IEEE/CVF CVPR (pp. 11–20).Google Scholar
Marelli, M., Menini, S., Baroni, M., et al. (2014). A sick cure for the evaluation of compositional distributional semantic models. In Proceedings of LREC (pp. 216–223).Google Scholar
Margolis, E. E., & Laurence, S. E. (1999). Concepts: Core readings. MIT Press.Google Scholar
McCoy, R. T., Linzen, T., Dunbar, E., & Smolensky, P. (2019). RNNs implicitly implement tensor-product representations. In ICLR.Google Scholar
McCoy, R. T., Pavlick, E., & Linzen, T. (2019). Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. In Proceedings of ACL (pp. 3428–3448). ACL.Google Scholar
Merrill, W., Warstadt, A., & Linzen, T. (2022). Entailment semantics can be extracted from an ideal language model. arXiv.CrossRefGoogle Scholar
Merullo, J., Eickhoff, C., & Pavlick, E. (2023). A mechanism for solving relational tasks in transformer language models.Google Scholar
Meteyard, L., Cuadrado, S. R., Bahrami, B., & Vigliocco, G. (2012). Coming of age: A review of embodiment and the neuroscience of semantics. Cortex, 48(7), 788–804.CrossRefGoogle Scholar
Mickus, T., Bernard, T., & Paperno, D. (2020). What meaning–form correlation has to compose with: A study of MFC on artificial and natural language. In Proceedings of COLING (pp. 3737–3749). International Committee on Computational Linguistics.Google Scholar
Mickus, T., Paperno, D., & Constant, M. (2022). How to dissect a Muppet: The structure of transformer embedding spaces. TACL, 10, 981–996.CrossRefGoogle Scholar
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv:1301.3781.Google Scholar
Mitchell, J., & Lapata, M. (2010). Composition in distributional models of semantics. Cognitive Science, 34(8), 1388–1429.CrossRefGoogle ScholarPubMed
Montague, R. (1970). English as a formal language. In Linguaggi nella societa e nella tecnica (pp. 188–221). Edizioni di Communita.Google Scholar
Montague, R. (1973). The proper treatment of quantification in ordinary English. In Approaches to natural language (pp. 221–242). Springer.CrossRefGoogle Scholar
Moss, L. S. (2010). Natural logic and semantics. In Logic, language and meaning (pp. 84–93). Springer.CrossRefGoogle Scholar
Moss, L. S. (2015). Natural logic. The handbook of contemporary semantic theory (pp. 559–592).CrossRefGoogle Scholar
Murzi, J., & Steinberger, F. (2017). Inferentialism. A Companion to the Philosophy of Language, 1, 197–224.CrossRefGoogle Scholar
Naik, A., Ravichander, A., Sadeh, N., et al. (2018). Stress test evaluation for natural language inference. In Proceedings of COLING (pp. 2340–2353). ACL.Google Scholar
Nangia, N., & Bowman, S. (2018). ListOps: A diagnostic dataset for latent tree learning. In Proceedings of NAACL: Student research workshop. ACL.Google Scholar
Nie, Y., Zhou, X., & Bansal, M. (2020). What can we learn from collective human opinions on natural language inference data? In Proceedings of EMNLP (pp. 9131–9143). ACL.Google Scholar
Nivre, J., de Marneffe, M.-C., Ginter, F., et al. (2020). Universal Dependencies v2: An evergrowing multilingual treebank collection. In Proceedings of LREC. ELRA.Google Scholar
Nye, M., Solar-Lezama, A., Tenenbaum, J., & Lake, B. M. (2020). Learning compositional rules via neural program synthesis. NeurIPS, 33, 10832–10842.Google Scholar
Olsson, C., Elhage, N., Nanda, N., et al. (2022). In-context learning and induction heads. arXiv:2209.11895.Google Scholar
Ouyang, L., Wu, J., Jiang, X., et al. (2022). Training language models to follow instructions with human feedback. NeurIPS, 35, 27730–27744.Google Scholar
Paperno, D. (2022). On learning interpreted languages wit–h recurrent models. Computational Linguistics, 48(2), 471–482.CrossRefGoogle Scholar
Paperno, D., & Baroni, M. (2016). When the whole is less than the sum of its parts: How composition affects pmi values in distributional semantic vectors. Computational Linguistics, 42(2), 345–350.CrossRefGoogle Scholar
Paperno, D., Kruszewski, G., Lazaridou, A., et al. (2016). The LAMBADA dataset: Word prediction requiring a broad discourse context. In Proceedings of ACL.Google Scholar
Paperno, D., Pham, N. T., & Baroni, M. (2014). A practical and linguistically-motivated approach to compositional distributional semantics. In Proceedings of ACL (pp. 90–99). ACL.Google Scholar
Parcalabescu, L., Cafagna, M., Muradjan, L., et al. (2022). Valse: A task-independent benchmark for vision and language models centered on linguistic phenomena. In Proceedings of ACL.CrossRefGoogle Scholar
Parcalabescu, L., & Frank, A. (2022). Mm-shap: A performance-agnostic metric for measuring multimodal contributions in vision and language models & tasks. arXiv:2212.08158.Google Scholar
Parikh, P. (2001). The use of language. CSLI Publications.Google Scholar
Parrish, A., Schuster, S., Warstadt, A., et al. (2021). NOPE: A corpus of naturally-occurring presuppositions in English. In Proceedings of CONLL (pp. 349–366). ACL.Google Scholar
Patel, A., Li, B., Rasooli, M. S., et al. (2022). Bidirectional language models are also few-shot learners. arXiv:2209.14500.Google Scholar
Pavlick, E., & Kwiatkowski, T. (2019). Inherent disagreements in human textual inferences. TACL, 7, 677–694.CrossRefGoogle Scholar
Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global vectors for word representation. In Proceedings of EMNLP (pp. 1532–1543).Google Scholar
Pérez, J., Barceló, P., & Marinkovic, J. (2021). Attention is Turing complete. Journal of Machine Learning Research, 22(1), 3463–3497.Google Scholar
Pezzelle, S. (2023). Dealing with semantic underspecification in multimodal NLP. In Proceedings of ACL (pp. 12098–12112). ACL.Google Scholar
Pezzelle, S., Takmaz, E., & Fernández, R. (2021). Word representation learning in multimodal pre-trained transformers: An intrinsic evaluation. TACL, 9, 1563–1579.CrossRefGoogle Scholar
Piantadosi, S. T., & Hill, F. (2022). Meaning without reference in large language models. arXiv.Google Scholar
Pinter, Y., Guthrie, R., & Eisenstein, J. (2017). Mimicking word embeddings using subword rnns. arXiv:1707.06961.Google Scholar
Poliak, A. (2020). A survey on recognizing textual entailment as an NLP evaluation. In Proceedings of the first workshop on evaluation and comparison of NLP systems (pp. 92–109). ACL.CrossRefGoogle Scholar
Poliak, A., Haldar, A., Rudinger, R., et al. (2018). Collecting diverse natural language inference problems for sentence representation evaluation. In Proceedings of EMNLP (pp. 67–81). ACL.Google Scholar
Poliak, A., Naradowsky, J., Haldar, A., et al. (2018). Hypothesis only baselines in natural language inference. In Proceedings of *SEM (pp. 180–191). ACL.Google Scholar
Potts, C. (2020). Is it possible for language models to achieve language understanding? (Medium post).Google Scholar
Prokhorov, V., Pilehvar, M. T., Kartsaklis, D., et al. (2019). Unseen word representation by aligning heterogeneous lexical semantic spaces. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, pp. 6900–6907).CrossRefGoogle Scholar
Pullum, G. K., & Huddleston, R. (2002). Negation. In The Cambridge grammar of the English language (pp. 785–850). Cambridge University Press.CrossRefGoogle Scholar
Radford, A., Kim, J. W., Hallacy, C., et al. (2021). Learning transferable visual models from natural language supervision. In ICML (pp. 8748–8763).Google Scholar
Radford, A., Wu, J., Child, R., et al. (2019). Language models are unsupervised multitask learners.Google Scholar
Rajaee, S., Yaghoobzadeh, Y., & Pilehvar, M. T. (2022). Looking at the overlooked: An analysis on the word-overlap bias in natural language inference. In Proceedings of EMNLP (pp. 10605–10616). ACL.Google Scholar
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016). Squad: 100,000+ questions for machine comprehension of text. arXiv:1606.05250.Google Scholar
Ramesh, A., Dhariwal, P., Nichol, A., et al. (2022). Hierarchical text-conditional image generation with clip latents. arXiv.Google Scholar
Rassin, R., Ravfogel, S., & Goldberg, Y. (2022). Dalle-2 is seeing double: Flaws in word-to-concept mapping in text2image models. arXiv:2210.10606.Google Scholar
Ravichander, A., Naik, A., Rose, C., & Hovy, E. (2019). EQUATE: A benchmark evaluation framework for quantitative reasoning in natural language inference. In Proceedings of CONLL (pp. 349–361). ACL.Google Scholar
Ribeiro, M. T., Wu, T., Guestrin, C., & Singh, S. (2020). Beyond accuracy: Behavioral testing of NLP models with CheckList. In Proceedings of ACL (pp. 4902–4912). ACL.Google Scholar
Richardson, K., Hu, H., Moss, L. S., & Sabharwal, A. (2020). Probing natural language inference models through semantic fragments. In AAAI.CrossRefGoogle Scholar
Ritter, S., Long, C., Paperno, D., et al. (2015). Leveraging preposition ambiguity to assess compositional distributional models of semantics. In Proceedings of *SEM.Google Scholar
Rogers, A., Kovaleva, O., & Rumshisky, A. (2020). A primer in BERTology: What we know about how BERT works. TACL, 8, 842–866.CrossRefGoogle Scholar
Rogers, A., & Rumshisky, A. (2020). A guide to the dataset explosion in QA, NLI, and commonsense reasoning. In Proceedings of COLING: Tutorial abstracts (pp. 27–32). International Committee for Computational Linguistics.Google Scholar
Rombach, R., Blattmann, A., Lorenz, D., et al. (2021). High-resolution image synthesis with latent diffusion models.CrossRefGoogle Scholar
Ross, A., & Pavlick, E. (2019). How well do NLI models capture verb veridicality? In Proceedings of EMNLP–IJCNLP (pp. 2230–2240). ACL.Google Scholar
Ruiz, N., Li, Y., Jampani, V., et al. (2022). Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. arXiv.Google Scholar
Ryzhova, D., Kyuseva, M., & Paperno, D. (2016). Typology of adjectives benchmark for compositional distributional models. In Proceedings of LREC (pp. 1253–1257).Google Scholar
Saha, S., Ghosh, S., Srivastava, S., & Bansal, M. (2020). PRover: Proof generation for interpretable reasoning over rules. In Proceedings of EMNLP (pp. 122–136). ACL.Google Scholar
Saha, S., Nie, Y., & Bansal, M. (2020). ConjNLI: Natural language inference over conjunctive sentences. In Proceedings of EMNLP. ACL.Google Scholar
Saharia, C., Chan, W., Saxena, S., et al. (2022). Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35, 36479–36494.Google Scholar
Schlag, I., Smolensky, P., Fernandez, R., et al. (2019). Enhancing the transformer with explicit relational encoding for math problem solving. arXiv:1910.06611.Google Scholar
Schlenker, P. (2018). What is super semantics? Philosophical Perspectives, 32(1), 365–453.CrossRefGoogle Scholar
Schroeder-Heister, P. (2018). Proof-theoretic semantics. In The Stanford encyclopedia of philosophy (Spring 2018 ed.). Metaphysics Research Lab, Stanford University.Google Scholar
Schuster, S., Chen, Y., & Degen, J. (2020). Harnessing the linguistic signal to predict scalar inferences. In Proceedings of ACL (pp. 5387–5403). ACL.Google Scholar
Sennrich, R., Haddow, B., & Birch, A. (2015). Neural machine translation of rare words with subword units. arXiv:1508.07909.Google Scholar
Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46(1– 2), 159–216.CrossRefGoogle Scholar
Socher, R., Huval, B., Manning, C. D., & Ng, A. Y. (2012). Semantic compositionality through recursive matrix-vector spaces. In Proceedings of EMNLP–CONLL (pp. 1201–1211).Google Scholar
Socher, R., Perelygin, A., Wu, J., et al. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of EMNLP (pp. 1631–1642).Google Scholar
Sommers, F. (1982). The logic of natural language. Oxford University Press.Google Scholar
Song, X., Salcianu, A., Song, Y., et al. (2021). Fast WordPiece tokenization. In Proceedings of EMNLP. ACL.Google Scholar
Soricut, R., & Och, F. J. (2015). Unsupervised morphology induction using word embeddings. In Proceedings of NAACL (pp. 1627–1637).Google Scholar
Soulos, P., McCoy, R. T., Linzen, T., & Smolensky, P. (2020). Discovering the compositional structure of vector representations with role learning networks. In Proceedings of blackboxnlp (pp. 238–254). ACL.Google Scholar
Srivastava, A., Rastogi, A., Rao, A., et al. (2022). Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv:2206.04615.Google Scholar
Storks, S., Gao, Q., & Chai, J. Y. (2019). Recent advances in natural language inference: A survey of benchmarks, resources, and approaches. arXiv.Google Scholar
Suhr, A., Lewis, M., Yeh, J., & Artzi, Y. (2017). A corpus of natural language for visual reasoning. In Proceedings of ACL (pp. 217–223).CrossRefGoogle Scholar
Tafjord, O., Dalvi, B., & Clark, P. (2021). ProofWriter: Generating implications, proofs, and abductive statements over natural language. In Findings of ACL–IJCNLP (pp. 3621–3634). ACL.Google Scholar
Tan, H., & Bansal, M. (2019). Lxmert: Learning cross-modality encoder representations from transformers. arXiv:1908.07490.Google Scholar
Tan, H., & Bansal, M. (2020). Vokenization: Improving language understanding with contextualized, visual-grounded supervision. arXiv:2010.06775.Google Scholar
Thrush, T., Jiang, R., Bartolo, M., et al. (2022). Winoground: Probing vision and language models for visio-linguistic compositionality. In Proceedings of IEEE/CVF CVPR.CrossRefGoogle Scholar
Tikhonov, A., Bylinina, L., & Paperno, D. (2023). Leverage points in modality shifts: Comparing language-only and multimodal word representations. In Proceedings of *SEM (pp. 11–17). ACL.Google Scholar
Tokmakov, P., Wang, Y.-X., & Hebert, M. (2019). Learning compositional representations for few-shot recognition. In Proceedings of IEEE/CVF ICCV (pp. 6372–6381).CrossRefGoogle Scholar
Touvron, H., Martin, L., Stone, K., et al. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv:2307.09288.Google Scholar
Truong, T., Baldwin, T., Cohn, T., & Verspoor, K. (2022). Improving negation detection with negation-focused pre-training. In Proceedings of NAACL (pp. 4188–4193). ACL.Google Scholar
Truong, T. H., Otmakhova, Y., Baldwin, T., et al. (2022). Not another negation benchmark: The NaN–NLI test suite for sub-clausal negation. In Proceedings of AACL–IJCNLP (pp. 883–894). ACL.Google Scholar
Tsuchiya, M. (2018). Performance impact caused by hidden bias of training data for recognizing textual entailment. In Proceedings of LREC. ELRA.Google Scholar
Turing, A. M. (2009). Computing machinery and intelligence. In Parsing the Turing test (pp. 23–65). Springer.CrossRefGoogle Scholar
Van Benthem, J. (1986). Natural logic. In Essays in logical semantics (pp. 109–119). Springer Netherlands.CrossRefGoogle Scholar
Van Benthem, J. (2008). A brief history of natural logic. In Logic, navya-nyaya and applications, homage to Bimal Krishna Matilal. College Publications.Google Scholar
Vashishtha, S., Poliak, A., Lal, Y. K., et al. (2020). Temporal reasoning in natural language inference. In Findings of EMNLP (pp. 4070–4078). ACL.Google Scholar
Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. In Neurips.Google Scholar
Vedantam, R., Bengio, S., Murphy, K., et al. (2017). Context-aware captions from context-agnostic supervision. In Proceedings of IEEE/CVF CVPR (pp. 251–260).CrossRefGoogle Scholar
Verga, P., Sun, H., Soares, L. B., & Cohen, W. W. (2020). Facts as experts: Adaptable and interpretable neural memory over symbolic knowledge. arXiv.Google Scholar
Vulić, I., Baker, S., Ponti, E. M., et al. (2020). Multi-simlex: A large-scale evaluation of multilingual and crosslingual lexical semantic similarity. Computational Linguistics, 46(4), 847–897.CrossRefGoogle Scholar
Wang, A., Pruksachatkun, Y., Nangia, N., et al. (2019). Superglue: A stickier benchmark for general-purpose language understanding systems. In Neurips (Vol. 32). Curran Associates, Inc.Google Scholar
Wang, A., Singh, A., Michael, J., et al. (2019). GLUE: A multi-task benchmark and analysis platform for natural language understanding. In ICLR.Google Scholar
Warstadt, A., & Bowman, S. R. (2022). What artificial neural networks can tell us about human language acquisition. arXiv:2208.07998.Google Scholar
Weiss, G., Goldberg, Y., & Yahav, E. (2018). On the practical computational power of finite precision rnns for language recognition. In Proceedings of ACL (pp. 740–745).Google Scholar
White, A. S., Rastogi, P., Duh, K., & Van Durme, B. (2017). Inference is everything: Recasting semantic resources into a unified evaluation framework. In Proceedings of IJCNLP (pp. 996–1005). Asian Federation of Natural Language Processing.Google Scholar
Williams, A., Nangia, N., & Bowman, S. (2018). A broad-coverage challenge corpus for sentence understanding through inference. In Proceedings of NAACL (pp. 1112–1122). ACL.Google Scholar
Yanaka, H., Mineshima, K., Bekki, D., & Inui, K. (2020). Do neural models learn systematicity of monotonicity inference in natural language? In Proceedings of ACL (pp. 6105–6117). ACL.Google Scholar
Yanaka, H., Mineshima, K., Bekki, D., et al. (2019a). Can neural networks understand monotonicity reasoning? In Proceedings of blackboxnlp (pp. 31–40).Google Scholar
Yanaka, H., Mineshima, K., Bekki, D., et al. (2019b). HELP: A dataset for identifying shortcomings of neural models in monotonicity reasoning. In Proceedings of *SEM.Google Scholar
Yang, Z., Dai, Z., Yang, Y., et al. (2019). Xlnet: Generalized autoregressive pretraining for language understanding. In Neurips (Vol. 32). Curran Associates, Inc.Google Scholar
Yi, K., Gan, C., Li, Y., et al. (2019). Clevrer: Collision events for video representation and reasoning. arXiv:1910.01442.Google Scholar
Yuksekgonul, M., Bianchi, F., Kalluri, P., et al. (2022). When and why vision-language models behave like bags-of-words, and what to do about it? arXiv.Google Scholar
Yun, T., Bhalla, U., Pavlick, E., & Sun, C. (2022). Do vision-language pretrained models learn primitive concepts? arXiv:2203.17271.Google Scholar
Zaenen, A., Karttunen, L., & Crouch, R. (2005). Local textual inference: Can it be defined or circumscribed? In Proceedings of the ACL workshop on empirical modeling of semantic equivalence and entailment. ACL.Google Scholar
Zhang, C., Van Durme, B., Li, Z., & Stengel-Eskin, E. (2022). Visual commonsense in pretrained unimodal and multimodal models. arXiv:2205.01850.Google Scholar
Zhang, C., Yang, Z., He, X., & Deng, L. (2020). Multimodal intelligence: Representation learning, information fusion, and applications. IEEE Journal of Selected Topics in Signal Processing, 14(3), 478–493.CrossRefGoogle Scholar
Zhou, D., Schärli, N., Hou, L., et al. (2022). Least-to-most prompting enables complex reasoning in large language models. arXiv:2205.10625.Google Scholar
Zhou, Y., Liu, C., & Pan, Y. (2016). Modelling sentence pairs with tree-structured attentive encoder. In Proceedings of COLING (pp. 2912–2922). The COLING 2016 Organizing Committee.Google Scholar

Save element to Kindle

To save this element to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Semantics and Deep Learning
Available formats
×

Save element to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Semantics and Deep Learning
Available formats
×

Save element to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Semantics and Deep Learning
Available formats
×