SlowGuess/ACL_2020_OCR huggingface dataset:
Course Name	Number of concepts included
Data and Structure 1	140
Data and Structure 2	117
Network Technology and Applications	125
The Basics of Programming in C++ Language	84
The Advanced Design of C++ Program	77
Introduction to Computer Science and Python Programming	116
Operating System	143
Java Programming Design	62
Artificial Intelligence	66
Artificial Intelligence for Beginner	54
5G and Artificial Intelligence	71
Big Data and Machine Learning	236
Table 3: Course name list.
Type	One-hop	Multu-hop	Total	All Types
MOOCQA Dataset	Query(Type A)	5,504	10,615	16,119	53,311
Judge(Type B)	16,324	14,301	30,625
Count(Type C)	3,384	3,183	6,567
Table 4: Statistics of questions.
Entity/Relationship	Table Name	Number of Rows
Entity	concept	700
course	12
paper	5,927
school	208
teacher	1,733
user	4,723
video	1,242
Relationship	concept_field	44
concept_paper	5,927
course_concept	10,346
course Video	1,591
school_course	705
school_teacher	2,130
teacher_course	2,349
user_course	24,933
video_concept	4,040
Table 5: Statistics of entities and relationships in MOOC Q&A.
understanding and multi-hop reasoning, to provide a comprehensive resource for more possible applications of MOOCs.
Here are the methods we followed to collect the QA dataset. We divide the dataset into one-hop questions and multi-hop questions. An one-hop question only involves a single head entity and a single predicate in the knowledge, while a multi-hop question may contain several entities and to
answer the question needs to reason over several facts in the knowledge graph.
We design 22 types of 1-hop question schema and 20 types of multi-hop question schema based on the meaningful real queries we collected from MOOC platform. Each schema is paraphrased into 4 different templates and questions are generated by random sampling from the text template pool. Triples related with twelve typical courses are used in case that the model wont run out of memory. The twelve courses are listed as Table 3.
The twelve courses are all from the computer science field. They cover different levels of courses in computer science and the internal prerequisite-successive relationships between the twelve courses typically represent the real relations between courses in MOOC platform. The model trained on our dataset is expected to provide MOOC users with information and further related knowledge they need. The type and number of entities and relationships are shown in Table 5.
Besides, to make our dataset closer to the actual scenario, three types of questions are contained in MOOCQA Dataset, which are Query, Judge and Count. When answering Query questions, model is expected to offer the correct entities in knowledge graph. As for Count questions, the count of the related entities is required. For Judge questions, the model should make a clear judgement of the
factoid description in the question.
In MOOCQA Dataset, each line is a question sample. In addition to the question and its corresponding answer, we provide more information including entity ids, question type, etc. Question, supporting fact and answer are separated by “\t”. If the answer consists of several entities, they will be separated by ‘|’.