Samajh-Boojh: A Reading Comprehension system in H indi (2019)

Abstract: This paper presents a novel approach designed to answer questions on a reading comprehension passage. It is an end-to-end system which first focuses on comprehending the given passage wherein it converts unstructured passage into a structured data and later proceeds to answer the questions related to the passage using solely the aforementioned structured data. To the best of our knowledge, the proposed design is first of its kind which accounts for entire process of comprehending the passage and then answering the questions associated with the passage. The comprehension stage converts the passage into a Discourse Collection that comprises of the relation shared amongst logical sentences in given passage along with the key characteristics of each sentence. This design has its applications in academic domain , query comprehension in speech systems among others.

## introduction the samajh-boojh 1 system which we have built focuses on the basic principles behind utilization of rules in order to capture the semantics of the given passage which is in the devanagari script. the current trend is towards incorporating machine learning in the question answering models but they come with a downside of requiring huge quantity and variety of training data to achieve decent accuracy. whereas the proposed model is rule-based and hence eliminates the need for extensive training data while still providing 75% accuracy. the samajh-boojh system answers 11 types of questions ( the panchatantra collection foot_0 comprising of 65 short stories was used to experiment on the model. this dataset had variety of stories with different lengths. the questions on each of these stories were framed by multiple annotators and best of which were picked to validate the system. the answers given by the annotators were used as gold data to measure the quality of the system. ## architecture and design the samajh-boojh system is broadly classified into two parts: comprehension part and question answering part. the system works similar to human approach of answering reading comprehension questions. the system takes passage and queries corresponding to the passage as the input, and returns answers to the questions. in subsequent sections we dwell deeper into these parts and see how they function. ## experiments panchatantra is a collection of fables. it has five parts, mitra-bheda (the loss of friends), mitra-laabha (the winning of friends), kakolukiyam (on crows and owls), labdhapranasam(losing what you have gained) and apariksitakarakam (ill-considered actions). we have chosen a corpus of 65 stories from the tales across all parts of panchantantra to test our system. we collected the stories from the link mentioned in footnote 2 and fixed the syntax (punctuation and spellings). we annotated 440 questions for the above stories and assigned the question types, the ideal episode to be selected from the discourse collection and the correct answer for each of the questions. while annotating the questions, we made a conscious effort to involve more questions in type 'kya' and 'givennew' (refer story original sentence anaphora resolved sentence root node sentence 1 3/7 5/7 5/7 2 7/11 7/11 11/11 3 3/6 3/6 4/6 4 3/5 4/5 4/5 5 1/8 3/8 4/8 6 1/3 2/3 2/3 ## future work this model currently doesn't answer कै से [how] types of questions, which can be included in future. currently we don't resolve synonyms and antonyms to answer the questions, which when done, can improve episode selection algorithm and also aim at answering complex questions. the current model assumes the passage to be in chronological order. we can improve the model if we capture the relative time of the episode to suite the passages which don't follow the chronological order. versatile questions such as 'givennew' and 'kya' can be improved by increasing the scope of answer retrieval to multiple sentences or episodes around the selected episode unlike the single episode range implemented in our model. this will in-turn also increase the scope of model to include longer and complex texts. ## conclusion reading comprehension is a complex task, which involves comprehending the passage and answering the questions following the passage. once, we are able to structure this unstructured data(passage), we can answer the