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Register a new userSPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning
سبارتا: سؤال نصي يجيب على المعيار للتفكير المكاني
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تقترح هذه الورقة معيارا للإجابة على الأسئلة (QA) للمنطق المكاني للنص اللغوي الطبيعي الذي يحتوي على ظواهر مكانية واقعية غير مغطاة بعمل مسبق وهو أمر صعب طرازات اللغة الحديثة (LM).نقترح طريقة الإشراف البعيدة لتحسين هذه المهمة.على وجه التحديد، نقوم بتصميم قواعد النحو والتفكير لإنشاء وصفا مكاني تلقائيا للمشاهد البصرية وأزواج ضمان الجودة المقابلة.تظهر التجارب أن محاور LMS بشكل أكبر على هذه البيانات التي تم إنشاؤها تلقائيا تعمل بشكل كبير على تحسين قدرة LMS على الفهم المكاني، والذي يساعد بدوره في حل مجموعات بيانات خارجية، و Babi، و Boolq.نأمل أن يعزز هذا العمل التحقيقات في نماذج أكثر تطورا للمناسبات المكانية على النص.
This paper proposes a question-answering (QA) benchmark for spatial reasoning on natural language text which contains more realistic spatial phenomena not covered by prior work and is challenging for state-of-the-art language models (LM). We propose a distant supervision method to improve on this task. Specifically, we design grammar and reasoning rules to automatically generate a spatial description of visual scenes and corresponding QA pairs. Experiments show that further pretraining LMs on these automatically generated data significantly improves LMs' capability on spatial understanding, which in turn helps to better solve two external datasets, bAbI, and boolQ. We hope that this work can foster investigations into more sophisticated models for spatial reasoning over text.
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