Papers by Gitit Kehat
Improving Neural Metaphor Detection with Visual Datasets (2020.lrec-1)
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| Challenge: | a new method for metaphor detection uses text from visual datasets to identify words . a metaphor is a complex interaction between two terms, creating an "implicationcomplex" |
| Approach: | They propose a technique for sampling text from visual datasets to create a visibility word embedding. |
| Outcome: | The proposed method improves on previous approaches that use more complex neural networks and richer linguistic features for verb classification. |
Neural Metaphor Detection with Visibility Embeddings (2021.starsem-1)
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| Challenge: | Using Visibility Embeddings, sequence metaphor labeling is improved . many metaphors involve noticeable differences between the abstractness of words constructing them . |
| Approach: | They propose to concatenate sequence metaphor labeling with BiLSTM inputs to obtain improvements . they use visibility embeddings to provide a good estimation of a word's concreteness . |
| Outcome: | The proposed method improves the problem of sequence metaphor labeling with BERT . it allows for consistent and significant improvements at almost no cost . |
The economic trade-offs of large language models: A case study (2023.acl-industry)
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Kristen Howell, Gwen Christian, Pavel Fomitchov, Gitit Kehat, Julianne Marzulla, Leanne Rolston, Jadin Tredup, Ilana Zimmerman, Ethan Selfridge, Joseph Bradley
| Challenge: | Large Language Models (LLMs) are a natural fit for contact-based customer service, but their efficacy must be balanced with the cost of training and serving them. |
| Approach: | They propose a cost framework for evaluating an NLP model’s utility for the enterprise as a function of the usefulness of the responses that they generate. |
| Outcome: | The proposed model can be used to help human agents handle complex customer service calls and can be modified to improve their performance. |