Papers by Martha Lewis
Modelling the interplay of metaphor and emotion through multitask learning (D19-1)
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| Challenge: | Existing research suggests that metaphorical phrases are more emotionally evocative than their literal counterparts. |
| Approach: | They propose a joint model of the relationship between metaphor and emotion within a computational framework by using hard and soft parameter sharing. |
| Outcome: | The proposed model advances the state of the art in both of these tasks. |
Behavioural vs. Representational Systematicity in End-to-End Models: An Opinionated Survey (2025.acl-long)
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| Challenge: | Existing benchmarks and models focus on systematicity of representations, but they focus on the systematicity in behaviour. |
| Approach: | They argue that systematicity is a desirable property in ML models as it enables strong generalization to novel contexts. |
| Outcome: | The proposed benchmarks and models focus on the systematicity of behaviour, while existing models focus primarily on language and vision. |
Recent advances in neural metaphor processing: A linguistic, cognitive and social perspective (2021.naacl-main)
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| Challenge: | Metaphor processing systems have benefited from recent studies on the role of metaphor in communication and deep learning for natural language processing. |
| Approach: | They present a review of automated metaphor processing and discuss their results from downstream NLP tasks. |
| Outcome: | The proposed system is based on the findings of a systematic and comprehensive survey of metaphor processing systems published five years ago. |
Quantifying Compositionality of Classic and State-of-the-Art Embeddings (2025.findings-emnlp)
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| Challenge: | Static word embeddings make strong claims about compositionality, but the SOTA generative models go too far in the other direction. |
| Approach: | a new study evaluates the compositionality of word embeddings by canonical correlation analysis . strong compositional signals are observed in later training stages across data modalities . |
| Outcome: | a new evaluation of compositional models shows that they exploit access meanings when justified . strong compositional signals are observed in later training stages and in deeper layers of the transformer-based model before a decline at the top layer. |
Metaphor Understanding Challenge Dataset for LLMs (2024.acl-long)
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| Challenge: | Metaphor understanding is an essential task for large language models (LLMs). |
| Approach: | They propose to evaluate the metaphor understanding capabilities of large language models (LLMs) the metaphor understanding challenge dataset provides over 10k paraphrases and 1.5k instances of inapt paraphrase. |
| Outcome: | The metaphor understanding challenge dataset evaluates the performance of large language models on a range of NLU tasks. |
Does CLIP Bind Concepts? Probing Compositionality in Large Image Models (2024.findings-eacl)
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| Challenge: | Large-scale neural network models combining text and images have made incredible progress in recent years, but to what extent they encode compositional representations of the concepts over which they operate remains an open question . |
| Approach: | They compare the performance of a large pretrained vision and language model (CLIP) to a set of three synthetic datasets designed to test concept binding. |
| Outcome: | The proposed model can encode compositional concepts and bind variables in a structure-sensitive way, e.g., differentiating ‘cube behind sphere’ from ‘cub behind cube’. |