Abstract
Research in building intelligent agents have emphasized the need for understanding characteristic behavior of people. In order to reflect human-like behavior, agents require the capability to comprehend the context, infer individualized persona patterns and incrementally learn from experience. In this paper, we present a model called DAPPER that can learn to embed persona from natural language and alleviate task or domain-specific data sparsity issues related to personas. To this end, we implement a text encoding strategy that leverages a pretrained language model and an external memory to produce domain-adapted persona representations. Further, we evaluate the transferability of these embeddings by simulating low-resource scenarios. Our comparative study demonstrates the capability of our method over other approaches towards learning rich transferable persona embeddings. Empirical evidence suggests that the learnt persona embeddings can be effective in downstream tasks like hate speech detection.- Anthology ID:
- 2020.aacl-main.65
- Volume:
- Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
- Month:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Venue:
- AACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 643–652
- Language:
- URL:
- https://aclanthology.org/2020.aacl-main.65
- DOI:
- Cite (ACL):
- Prashanth Vijayaraghavan, Eric Chu, and Deb Roy. 2020. DAPPER: Learning Domain-Adapted Persona Representation Using Pretrained BERT and External Memory. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 643–652, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- DAPPER: Learning Domain-Adapted Persona Representation Using Pretrained BERT and External Memory (Vijayaraghavan et al., AACL 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.aacl-main.65.pdf
- Data
- Hate Speech