Luana Bulat


2020

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Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models
Vesna G. Djokic | Jean Maillard | Luana Bulat | Ekaterina Shutova
Transactions of the Association for Computational Linguistics, Volume 8

Recent years have seen a growing interest within the natural language processing (NLP) community in evaluating the ability of semantic models to capture human meaning representation in the brain. Existing research has mainly focused on applying semantic models to decode brain activity patterns associated with the meaning of individual words, and, more recently, this approach has been extended to sentences and larger text fragments. Our work is the first to investigate metaphor processing in the brain in this context. We evaluate a range of semantic models (word embeddings, compositional, and visual models) in their ability to decode brain activity associated with reading of both literal and metaphoric sentences. Our results suggest that compositional models and word embeddings are able to capture differences in the processing of literal and metaphoric sentences, providing support for the idea that the literal meaning is not fully accessible during familiar metaphor comprehension.

2019

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Deconstructing multimodality: visual properties and visual context in human semantic processing
Christopher Davis | Luana Bulat | Anita Lilla Vero | Ekaterina Shutova
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

Multimodal semantic models that extend linguistic representations with additional perceptual input have proved successful in a range of natural language processing (NLP) tasks. Recent research has successfully used neural methods to automatically create visual representations for words. However, these works have extracted visual features from complete images, and have not examined how different kinds of visual information impact performance. In contrast, we construct multimodal models that differentiate between internal visual properties of the objects and their external visual context. We evaluate the models on the task of decoding brain activity associated with the meanings of nouns, demonstrating their advantage over those based on complete images.

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Modeling Affirmative and Negated Action Processing in the Brain with Lexical and Compositional Semantic Models
Vesna Djokic | Jean Maillard | Luana Bulat | Ekaterina Shutova
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Recent work shows that distributional semantic models can be used to decode patterns of brain activity associated with individual words and sentence meanings. However, it is yet unclear to what extent such models can be used to study and decode fMRI patterns associated with specific aspects of semantic composition such as the negation function. In this paper, we apply lexical and compositional semantic models to decode fMRI patterns associated with negated and affirmative sentences containing hand-action verbs. Our results show reduced decoding (correlation) of sentences where the verb is in the negated context, as compared to the affirmative one, within brain regions implicated in action-semantic processing. This supports behavioral and brain imaging studies, suggesting that negation involves reduced access to aspects of the affirmative mental representation. The results pave the way for testing alternate semantic models of negation against human semantic processing in the brain.

2017

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Speaking, Seeing, Understanding: Correlating semantic models with conceptual representation in the brain
Luana Bulat | Stephen Clark | Ekaterina Shutova
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Research in computational semantics is increasingly guided by our understanding of human semantic processing. However, semantic models are typically studied in the context of natural language processing system performance. In this paper, we present a systematic evaluation and comparison of a range of widely-used, state-of-the-art semantic models in their ability to predict patterns of conceptual representation in the human brain. Our results provide new insights both for the design of computational semantic models and for further research in cognitive neuroscience.

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Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection
Marek Rei | Luana Bulat | Douwe Kiela | Ekaterina Shutova
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

The ubiquity of metaphor in our everyday communication makes it an important problem for natural language understanding. Yet, the majority of metaphor processing systems to date rely on hand-engineered features and there is still no consensus in the field as to which features are optimal for this task. In this paper, we present the first deep learning architecture designed to capture metaphorical composition. Our results demonstrate that it outperforms the existing approaches in the metaphor identification task.

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Learning to Negate Adjectives with Bilinear Models
Laura Rimell | Amandla Mabona | Luana Bulat | Douwe Kiela
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We learn a mapping that negates adjectives by predicting an adjective’s antonym in an arbitrary word embedding model. We show that both linear models and neural networks improve on this task when they have access to a vector representing the semantic domain of the input word, e.g. a centroid of temperature words when predicting the antonym of ‘cold’. We introduce a continuous class-conditional bilinear neural network which is able to negate adjectives with high precision.

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Modelling metaphor with attribute-based semantics
Luana Bulat | Stephen Clark | Ekaterina Shutova
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

One of the key problems in computational metaphor modelling is finding the optimal level of abstraction of semantic representations, such that these are able to capture and generalise metaphorical mechanisms. In this paper we present the first metaphor identification method that uses representations constructed from property norms. Such norms have been previously shown to provide a cognitively plausible representation of concepts in terms of semantic properties. Our results demonstrate that such property-based semantic representations provide a suitable model of cross-domain knowledge projection in metaphors, outperforming standard distributional models on a metaphor identification task.

2016

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Vision and Feature Norms: Improving automatic feature norm learning through cross-modal maps
Luana Bulat | Douwe Kiela | Stephen Clark
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Grounding Semantics in Olfactory Perception
Douwe Kiela | Luana Bulat | Stephen Clark
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)