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Learning Functional Distributional Semantics with Visual Data ...
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IAPUCP at SemEval-2021 task 1: Stacking fine-tuned transformers is almost all you need for lexical complexity prediction
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Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model ...
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Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics ...
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Abstract:
Functional Distributional Semantics provides a linguistically interpretable framework for distributional semantics, by representing the meaning of a word as a function (a binary classifier), instead of a vector. However, the large number of latent variables means that inference is computationally expensive, and training a model is therefore slow to converge. In this paper, I introduce the Pixie Autoencoder, which augments the generative model of Functional Distributional Semantics with a graph-convolutional neural network to perform amortised variational inference. This allows the model to be trained more effectively, achieving better results on two tasks (semantic similarity in context and semantic composition), and outperforming BERT, a large pre-trained language model. ... : To be published in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL); added acknowledgements ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2005.02991
https://arxiv.org/abs/2005.02991
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Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics ...
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Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model ...
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Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Words are vectors, dependencies are matrices: Learning word embeddings from dependency graphs
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Copestake, Ann; Czarnowska, P; Emerson, Guy. - : Association for Computational Linguistics, 2019. : https://aclanthology.org/volumes/W19-04/, 2019. : IWCS 2019 - Proceedings of the 13th International Conference on Computational Semantics - Long Papers, 2019
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Functional Distributional Semantics: Learning Linguistically Informed Representations from a Precisely Annotated Corpus ...
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Emerson, Guy. - : Apollo - University of Cambridge Repository, 2018
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Functional Distributional Semantics: Learning Linguistically Informed Representations from a Precisely Annotated Corpus
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Emerson, Guy. - : University of Cambridge, 2018. : Department of Computer Science and Technology, 2018. : Trinity College, 2018
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Functional Distributional Semantics
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Emerson, Guy; Copestake, Ann. - : The Association for Computational Linguistics, 2016. : Proceedings of the 1st Workshop on Representation Learning for NLP, 2016
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Lacking integrity: HPSG as a morphosyntactic theory
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Emerson, Guy; Copestake, Ann. - : University Library J. C. Senckenberg, 2015. : http://web.stanford.edu/group/cslipublications/cslipublications/HPSG/2015/emerson-copestake.pdf, 2015. : Proceedings of the International Conference on Head-Driven Phrase Structure Grammar, 2015
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Leveraging a semantically annotated corpus to disambiguate prepositional phrase attachment
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Emerson, Guy; Copestake, Ann. - : The Association for Computer Linguistics, 2015. : https://aclanthology.org/volumes/W15-01/, 2015. : IWCS 2015 - Proceedings of the 11th International Conference on Computational Semantics, 2015
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