Alexander Miserlis Hoyle

According to our database1, Alexander Miserlis Hoyle authored at least 12 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2023
TopicGPT: A Prompt-based Topic Modeling Framework.
CoRR, 2023

Making the Implicit Explicit: Implicit Content as a First Class Citizen in NLP.
CoRR, 2023

Re-visiting Automated Topic Model Evaluation with Large Language Models.
CoRR, 2023

Revisiting Automated Topic Model Evaluation with Large Language Models.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Natural Language Decompositions of Implicit Content Enable Better Text Representations.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

2022
Are Neural Topic Models Broken?
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

2021
Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards?
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

Promoting Graph Awareness in Linearized Graph-to-Text Generation.
Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021

2020
Improving Neural Topic Models using Knowledge Distillation.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

2019
Combining Sentiment Lexica with a Multi-View Variational Autoencoder.
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019

Unsupervised Discovery of Gendered Language through Latent-Variable Modeling.
Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019


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