On the rules given to lms programs using artificial intelligence with the help of natural language processing

Methods 
This work proposes a rule based approach to question generation from 
sentence. To be specific; dependency based, Named Entity Recognition (NER) 
based, and semantic role (SRL) labeling based templates/rules are used. In terms of 
rules, our contribution can be explained as follows:
1. For dependency based rules, the following patterns are newly added:
• S-V-oprd is an object predicate that defines the subject.
• S-V-xcomp is an open clausal complement without an internal subject.
• S-V-ccomp (clausal complement) is a clause with an internal subject. 
2. NER based rules, which are S-V-number, S-V-location, S-V-date, S-V-
person, are added.
3. More importantly, new semantic role labeling based templates/rules are 
constructed:
• S-V-ARGM-CAU: cause clause.
• S-V-ARGM-MNR: manner marker.
• S-V-ARGM-PNC: purpose clause.
• S-V-ARGM-LOC: locative.
• S-V-ARGM-TMP: temporal marker. 
This work mainly aims to generate more comprehensive questions by 
exploiting the semantic roles of words. Figure 1.1 shows the illustrative example of 
yes/no question generation. By using semantic role labeling, sentence is decomposed 
into its parts. Then, tense is detected and verb is converted to its base form. With 
construction stage, question generation becomes complete. 

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To test the effectiveness of the proposed approach, it is compared against two 
state-of-the-art systems: Du et al.’s (2017) learning-based system for question 
generation for reading comprehension and the best rule-based system by Heilman 
and Smith (2011). Our automatic evaluation through objective neural translation 
metrics show that our system has superior performance and outperforms H&S and 
Du’s systems in BLEU-2, METEOR, and ROUGE-L metrics. The superior 
performance of the proposed approach especially in METEOR metric can be 
attributed to its recall based nature. By diversifying and extending the rule sets, our 
approach produces an expanded set of questions out of those that can possibly be 
asked.
We performed human evaluation as well. In human evaluations, the designed 
system significantly outperforms H&S and Du’s systems and generated the most 
natural (human-like) questions. 

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