Hossein Rajaby Faghihi

Orcid: 0000-0003-4283-8816

According to our database1, Hossein Rajaby Faghihi authored at least 12 papers between 2020 and 2024.

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

Timeline

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Links

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Bibliography

2024
Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language.
Proceedings of the Neural-Symbolic Learning and Reasoning - 18th International Conference, 2024

Consistent Joint Decision-Making with Heterogeneous Learning Models.
Proceedings of the Findings of the Association for Computational Linguistics: EACL 2024, 2024

2023
The Role of Semantic Parsing in Understanding Procedural Text.
Proceedings of the Findings of the Association for Computational Linguistics: EACL 2023, 2023

GLUECons: A Generic Benchmark for Learning under Constraints.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Hybrid Learning Approach Toward Situation Recognition and Handling.
Comput. J., 2022

CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

2021
DomiKnowS: A Library for Integration of Symbolic Domain Knowledge in Deep Learning.
CoRR, 2021

Latent Alignment of Procedural Concepts in Multimodal Recipes.
CoRR, 2021

SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

Time-Stamped Language Model: Teaching Language Models to Understand The Flow of Events.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

DomiKnowS: A Library for Integration of Symbolic Domain Knowledge in Deep Learning.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2021

2020
Inference-Masked Loss for Deep Structured Output Learning.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020


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