Abstract

3 15 minute presentations on accepted EACL short papers:

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Learning to Negate Adjectives with Bilinear Models

Laura Rimell, Amandla Mabona, Luana Bulat, Douwe Kiela

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, Ekaterina Shutova, Stephen Clark

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 superior model of cross-domain knowledge projection in metaphors, outperforming standard distributional models on a metaphor identification task.

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Latent Variable Dialogue Models and their Diversity

Kris Cao and Stephen Clark

We present a dialogue generation model that directly captures the variability in possible responses to a given input, which reduces the `boring output’ issue of deterministic dialogue models. Experiments show that our model generates more diverse outputs than baseline models, and also generates more consistently acceptable output than sampling from a deterministic encoder-decoder model.