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    <abstract>Recent research shows synthetic data as a source of supervision helps pretrained language models (PLM) transfer learning to new target tasks/domains. However, this idea is less explored for spatial language. We provide two new data resources on multiple spatial language processing tasks. The first dataset is synthesized for transfer learning on spatial question answering (SQA) and spatial role labeling (SpRL). Compared to previous SQA datasets, we include a larger variety of spatial relation types and spatial expressions. Our data generation process is easily extendable with new spatial expression lexicons. The second one is a real-world SQA dataset with human-generated questions built on an existing corpus with SPRL annotations. This dataset can be used to evaluate spatial language processing models in realistic situations. We show pretraining with automatically generated data significantly improves the SOTA results on several SQA and SPRL benchmarks, particularly when the training data in the target domain is small.</abstract>
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%0 Conference Proceedings
%T Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning
%A Mirzaee, Roshanak
%A Kordjamshidi, Parisa
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F mirzaee-kordjamshidi-2022-transfer
%X Recent research shows synthetic data as a source of supervision helps pretrained language models (PLM) transfer learning to new target tasks/domains. However, this idea is less explored for spatial language. We provide two new data resources on multiple spatial language processing tasks. The first dataset is synthesized for transfer learning on spatial question answering (SQA) and spatial role labeling (SpRL). Compared to previous SQA datasets, we include a larger variety of spatial relation types and spatial expressions. Our data generation process is easily extendable with new spatial expression lexicons. The second one is a real-world SQA dataset with human-generated questions built on an existing corpus with SPRL annotations. This dataset can be used to evaluate spatial language processing models in realistic situations. We show pretraining with automatically generated data significantly improves the SOTA results on several SQA and SPRL benchmarks, particularly when the training data in the target domain is small.
%R 10.18653/v1/2022.emnlp-main.413
%U https://aclanthology.org/2022.emnlp-main.413
%U https://doi.org/10.18653/v1/2022.emnlp-main.413
%P 6148-6165

Markdown (Informal)

[Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning](https://aclanthology.org/2022.emnlp-main.413) (Mirzaee & Kordjamshidi, EMNLP 2022)

• Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning (Mirzaee & Kordjamshidi, EMNLP 2022)

ACL

• Roshanak Mirzaee and Parisa Kordjamshidi. 2022. Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6148–6165, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.