Papers by Jesse Mu
Learning Outside the Box: Discourse-level Features Improve Metaphor Identification (N19-1)
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| Challenge: | Current approaches to metaphor identification use restricted linguistic contexts, e.g. by only considering a verb’s arguments or the sentence containing a phrase. |
| Approach: | They propose to train simple gradient boosting classifiers on representations of an utterance and its surrounding discourse learned with a variety of document embedding methods. |
| Outcome: | The proposed classifiers obtained state-of-the-art results on the 2018 VU Amsterdam metaphor identification task without complex metaphor-specific features or deep neural architectures employed by other systems. |
Calibrate your listeners! Robust communication-based training for pragmatic speakers (2021.findings-emnlp)
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| Challenge: | Prior work has investigated training NLP systems with communication-based objectives . prior work has focused on supervised learning, but is expensive to collect . |
| Approach: | They propose a method that uses a population of neural listeners to regularize speaker training. |
| Outcome: | The proposed method improves on ensemble- and dropout-based listening populations on reference games and generalizes to new games and listeners. |
Shaping Visual Representations with Language for Few-Shot Classification (2020.acl-main)
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| Challenge: | Existing models use natural language descriptions to classify images, but no model uses it for new tasks. |
| Approach: | They propose a model that regularizes visual representations to predict language in a few-shot setting . they propose to use language to improve few- shot visual classification . |
| Outcome: | The proposed model outperforms baseline models in two challenging few-shot domains. |