Augmenting e B ooks with with recommended questions using contrastive fine-tuned T5 (2022)

Abstract: The recent advances in AI made generation of questions from natural language text possible, the approach is completely excludes human in the loop while generating the appropriate questions which improves the students learning engagement. The ever growing amount of educational content renders it increasingly difficult to manually generate sufficient practice or quiz questions to accompany it. Reading comprehension can be improved by asking the right questions, So, this work offers a Transformer based question generation model for autonomously producing quiz questions from educational information, such as eBooks. This work proposes an contrastive training approach for ‘Text-to-Text Transfer Transformer’ (T5) model where the model (T5-eQG) creates the summarized text for the input document and then automatically generates the questions. Our model shows promising results over earlier neural network-based and rules-based models for question generating task on benchmark datasets and NCERT eBooks.

## introduction textbooks are a primary source of information for students @xcite . besides this students tends to study ebooks, lecture notes, and moocs for further knowledge acquisition @xcite . with these reading materials students can only partially understand the material presented, and it does not makes their learning effective @xcite . asking questions about the reading content and evaluate the answer is a intuitive way of promoting learning @xcite . this motivates us to look into ways to generate educational questions for ebook content to aid the students learning. it is difficult to define the specific process for asking insightful educational questions about texts, which entails doing more than just writing fluid, natural-sounding texts. it is bit hard to generate relevant educational questions on text @xcite . typically, it involves gathering important instructional facts and turning them into questions. few attempts have been made in the recent years to complete this task by using statistical and neural network based algorithms to choose crucial passages @xcite and concepts and produce insightful questions @xcite . education question generation on text, however, has not received much attention. the proposed educational question generation (eqg) model presents a set of questions for each chapter of the ebook. teachers might use these questions for self-study, before discussing the subject in class, which would helps them for deep knowledge transfer to the students (kumar and chauhan, 2019; @xcite . we assess our methodology on qa dataset (hotpot qa @xcite , fairytaleqa foot_0 ) as well as prml @xcite and ncert foot_1 ebooks. the contributions of this work are two fold: ## background natural language creation has primarily evolved through statistical learning in recent years. to create manuscripts that resemble human writing, the models imitate linguistic conventions. in recent years, the natural language processing communities have shown a great deal of interest in the question generation (qg) task @xcite , which creates a natural question corresponding to the supplied text or answer phase. the syntactic cues have been used in the rule based model to create questions @xcite . a back translation tool was paired with a syntactic question generator to eliminate grammatical errors and increase robustness(dhole and manning, 2020). declarative sentences were transformed into natural questions by the emergence of sequence-to-sequence models @xcite . applying pre-trained transformers or various optimization objectives @xcite led to further advancements. previous research has explored the importance of qg model in teaching learning process @xcite . for qa and question generation, narrativeqa @xcite aims to incorporate important information from many places inside a paragraph. similar to this, the ms marco @xcite dataset combines many sources of responses to search queries. the employment of a reinforcement learning agent to align questions from various documents is proposed as a contrastive strategy, where supervised model is trained to produce questions on a text @xcite . to achieve good performance, questions with summaries and reports were generated using a rule-based methodology @xcite . the solutions discussed above typically don't take the educational component into account and could not be effective in the real world edu qg task. our research focuses on the generating question on e-book content, in this work we use fairytaleqa dataset @xcite . for each paragraph in fairytaleqa, experts typically create a different style of question. we propose that context is a key factor in determining the kinds of questions that ought to be made while reading e-books. ## methodology figure 2 depicts the overall architecture of our eqg system, which consists of two modules: i) text summarizer ii) question-generation(qg). we first create summaries of type s with the input paragraph d, and then generate the questions q on summarized text. the generated questions are said to be relevant if the question q i can be answered with the paragraph d i and this is formulated as maximizing the conditional probability p(q|d): ## experimental results in this work we trained t5 (text-to-text transfer transformer) base model from hugging face transformers 3 . an input sequence and a corresponding target sequence are required for every training run of t5. the model receives the input sequence via input ids. the target sequence is provided to the decoder using the decoder input ids after being prepended by a start-sequence token and moved to the right. the eos token is subsequently attached to the target sequence in teacher-forcing fashion, which correlates to the labels. the start-sequence token here is the pad token. data set:we used fairytaleqa 1 and hotpotqa data, the fairytaleqa has 10,580 qa-pairs, which were drawn from 278 different novels. the hot-potqa @xcite has ≈100k qa pairings on wikipedia articles. for fairyqa we divide the data into 8.5k/1k/1k, and for hotpot qa 84,512, 6k and 6k samples as train,validation and test data. for fine tuning the t-5 3 model we used the adafactor optimizer and a maximum sequence length is set to 512, model is trained for 4 epochs. we follow the grid-search approach for choosing the best set of training parameters (learningrate:{{2,3,5}e-3,4}} and batch size: {8,16,32,64}, warm-up ratio: {0.1}). during the experiment, we found that a mini-batch size of 32 (learning rate: 3e-3) produces acceptable results. we validated the quality of questions generated on three experimental configuration. qg model trained i)only on hotpotqa, (ii) only on fairy-taleqa, iii) on both fairytaleqa and hotpotqa. the third setting shows a significant improvement over the preceding setups, so this was chosen as our final qg model for further comparison to the earlier work. results are shown in ## conclusion and future scope the use of ai is constantly evolving in diverse applications. this study investigates the potential advantages of a natural language processing approach for education. this research presented an education question generating (eqg) approach that augments the ebook content with generated edu-questions to provide students with an effective learning platform. through experiments, we assessed the model's performance on a question generation task both before and after contrastive training. we discovered that a contrastive trained model can produce more pertinent questions on the input text and can comprehend key concepts more effectively. experiments on qa dataset, prml @xcite and ncert 2 ebook shows that our model succeeds to produces complex questions at scale. the possible future direction could be • design a context-aware qg model, where the generation of a new text is conditioned on previous generations as well as the ebook contents.