Papers by Eric Chu

2 papers
DAPPER: Learning Domain-Adapted Persona Representation Using Pretrained BERT and External Memory (2020.aacl-main)

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Challenge: Empirical evidence suggests that the learnt persona embeddings can be effective in downstream tasks like hate speech detection.
Approach: They propose a model that embeds personas from natural language into text . they evaluate the transferability of the model by simulating low-resource scenarios .
Outcome: The proposed model can learn to embed persona from natural language and alleviate task or domain-specific data sparsity issues related to personas.
Learning Personas from Dialogue with Attentive Memory Networks (D18-1)

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Challenge: Existing systems that can infer persona from dialogue can be used for computational narrative analysis and personalized dialogue generation.
Approach: They propose neural models to learn persona embeddings in a character trope classification task using IMDB dialogue snippets.
Outcome: The proposed methods could be applied to other domains, including personalized dialogue generation.

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