Question and Answer pair generation for T elugu short stories (2020)

Abstract: Question Answer pair generation is a task that has been worked upon by multiple researchers in many languages. It has been a topic of interest due to its extensive uses in different fields like self assessment, academics, business website FAQs etc. Many experiments were conducted on Question Answering pair generation in English, concentrating on basic Wh-questions with a rule-based approach. We have built the first hybrid machine learning and rule-based solution in Telugu which is efficient for short stories or short passages in children’s books. Our work covers the fundamental question forms with the question types: adjective, yes/no, adverb, verb, when, where, whose, quotative, and quantitative(how many/ how much). We constructed rules for question generation using POS tags and UD tags along with linguistic information of the surrounding context of the word.

## introduction question and answer pair generation is an open problem in linguistics which deals with natural language understanding (nlu) and natural lan guage generation (nlg). nlu and nlg are com monly used in interactive nlp applications such as aibased dialogue systems/voice assistants like siri, google assistant, alexa, and similar other personal assistants. numerous methods are intro duced for the q&a pair generation problem. for a lowresourced language like telugu, aibased so lutions can be nonviable. there are hardly any datasets available for the system to produce sig nificant accuracy. a completely rulebased sys tem might leave out principle parts of the abstract. there is a chance that all the questions cannot be captured inclusively by completely handwritten rules. hence, we wanted to introduce a mixed rule based and aibased solution to this problem. we attempted to produce questions, concentrat ing on the key points of a text that are generally asked in assessment tests. questions posed to an individual challenge their knowledge and under standing of specific topics, so we formed questions in each sentence in as many ways as possible. we based this paper on children's stories, so the ques tions we wanted to produce aim to be simpler and more objective. based on the observation of the data chosen and after analyzing all the possible cases, we devel oped a set of rules for each part of speech that could be formed into a question word in telugu. we max imized the possible number of questions in each sentence with all the keywords. ## related work previously, holy lovenia et al. @xcite experi mented on q&a pair generation @xcite in english where they suc ceeded in forming "what", "who", and "where" questions. rami @xcite worked on dialogue based question answering system in telugu for railway inquiries @xcite , which majorly concentrated on answer genera tion for a given query. shudipta sharma et al. worked on implementing automatic q&a pair gen eration for english and bengali texts @xcite using nlp tasks like verb decom position, subject auxiliary inversion for a ques tion tag. telugu dependency parsing using differ ent statistical parsers (seshukumari and rajesh wararao, 2017) explored dfferent statistical depen dency parsers for parsing telugu and analysed the performanced of each parser. we explored other foot_0 q&a state of art systems from different authors that suita our approach. ## summarization since telugu is a low resource language, we used statistical and unsupervised methods for this task foot_2 . summarization also ensures the portability of our system to other similar low resource languages. we have used a telugu stories dataset taken from a website called "kathalu wordpress". 3 this dataset was chosen because of the variety in the themes of the stories, wide vocabulary and sen tences of varying lengths. for summarization, we did the basic data preprocessing (spaces, special characters, etc.) in addition to rootword extrac tion using shiva reddy's pos tagger 4 . we implemented two types of existing summa rization techniques: ## answer phrase selection candidate answers are words/phrases that depict some vital information in a sentence. adjectives, adverbs, and the subject of a sentence are some ex amples of such candidates. the answer selection module utilizes two main nlp components pos tagging (parts of speech) and ud parsing (universal dependency), along with languagespecific rules to determine the an swer words in an input sentence. ## question formation questions are formed according to the chosen phrases chosen previously, and the question words are replaced using further conditions if required. ## results we obtained results that resemble commonly used questions covering nine parts of speech. the ques tions generated by this system are successful and are most similar to questions we see in textbooks. in most cases, it has given legible results that re semble humanmade questions, with few excep tions for complex sentences. out of 916 questions formed, only 34 were either completely erroneous or illegible, the rest of them were both grammati cally correct and significant for the context of the story. ## conclusions we have built a mixed rulebased and aibased question and answer generating system with 96.28% accuracy.we used two methods for summarization and two similarity measures. we constructed observationalbased rules for the data set in a particular domain. there is a chance of varying results if we test this system for data in a different domain, but it gives accuracies above 95% for any data in the domain we chose. we tested question generation in the news article domain, which gave grammatically correct ques tions. the error rate may increase if we use com plex words and phrases that need tags beyond the proposed set of rules. we plan to extend our work to be able to include: 1.anaphora resolution 2.extending to other domains 3.cover more types of questions 4.increase the accuracy of identifying subject for ud tags a appendix q: ఎటువంటి మోట తో వంగడం కష ట్ ంగా వుంది? a: అంత పె ద ద్ మోట తో వంగడం కష ట్ ంగా వుంది q: చె పుుప్ , బటు ల్ , గాులు , పళుళ్ , గినె న్లు బజారులో ఎలా కొని , ఊళోళ్ ఇంటింటికి వె ళి ల్ అముమ్కునే వాడు? a: చె లు , బట ట్ లు , గాజులు , పళుళ్ , గినె న్లు బజారులో చవకగా కొని , ఊళోళ్ ఇంటింటికి వె ళి ల్ అముమ్కునే వాడు q: సామాన ల్ నీన్ మోట కటి ట్ , గాడిద మీద వేసి , బజారు నుంచి ఊళోళ్ , ఊళో ల్ నుంచి తిరిగి ఎవరి ఇంటికి తిపేప్వాడు? a: సామాన ల్ నీన్ మోట కటి ట్ , గాడిద మీద వేసి , బజారు నుంచి ఊళోళ్ , ఊళో ల్ నుంచి తిరిగి అతని ఇంటికి తిపేప్వాడు q: అమాయక పిచుక ఎకక్డకి, ఎందుకు అని అడగకుండా, ఆ కాకులను గుడి డ్ గా నమిమ్ ఏమి చేసింది? a: అమాయక పిచుక ఎకక్డకి, ఎందుకు అని అడగకుండా, ఆ కాకులను గుడి డ్ గా నమిమ్ వాటితో వె ళిళ్ంది. q: పిచుక మాట నమమ్లేదు కదా , దాని వె ౖ పు అసహయ్ంగా చూసి మరో ఎనిన్ దె బబ్లు వేసారు? a: పిచుక మాట నమమ్లేదు కదా , దాని వె ౖ పు అసహయ్ంగా చూసి మరో రె ండు దె బబ్లు వేసారు q: ఆ కాకులతో పిచుకకి సేన్హం అయియ్ందా? a: అవును, ఆ కాకులతో పిచుకకి సేన్హం అయియ్ంది. q: ఒకానొక డు ఎకక్డ ఒక అమాయకపు పిచుక వుండేది? a:ఒకానొక డు ఒక ఊరిలో ఒక అమాయకపు పిచుక వుండేది. q: ఏమని పిచుక పా ర్ ధేయ పడింది? a:బాబోయ్! బాబోయ్! నా తపేప్మీ లేదు, నేను అమాయకురాలిని, నేనేమీ చేయలేదు, ననున్ వదిలేయండి! అని పిచుక పా ర్ ధేయ పడింది.