2022/10/30 by Roshanak Mirzaee, Mirzaee, Roshanak, Parisa Kordjamshidi +1 · 7 citations
Computer Science · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Geographic Information Systems Studies #Multimodal Machine Learning Applications #Speech and dialogue systems #cs.CL
paper · pdf · doi:10.48550/arxiv.2210.16952
The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)
openalex publication_date 2022/10/30 · arxiv created 2022/11/03 · openalex created_date 2022/11/06 · arxiv updated 2022/11/07 · openalex updated_date 2026/07/28
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.