QLaMDA: txt2Que generation
Automated Question Generation (AQG) is essential in NLP, with applications in education, as- sessment, and conversational agents. qlamda presents a comprehensive approach to automated text summarization and question generation using NLP techniques. By leveraging the T5 transformer model, Sense2Vec, and Sentence Transformers, the system extracts key information and generates rel- evant questions. The integration of the MMR algorithm enhances keyword selection, while WordNet and Sense2Vec generate distractors, facilitating multiple-choice question creation. The results demon- strate the system’s ability to produce concise summaries and generate contextually relevant questions, showcasing its potential for educational and information retrieval applications.
The methodology encompasses several steps, in- cluding text summarization, keyword extraction, question generation, and distractor generation. Each step plays a crucial role in ensuring the ef- fectiveness of the AQG system.
For text summarization, the T5 transformer model was utilized. T5, short for ”(Text-to-Text Transfer Transformer)” [1], is a versatile model that can per- form a wide range of NLP tasks by converting them into a text-to-text format. The model was pre- trained on a large corpus of text data and fine-tuned specifically for summarization tasks. The summa- rization process involves encoding the input text with a special prefix (”summarize: ”) to guide the model towards generating a summary. The model’s output is then post-processed to ensure proper sen- tence capitalization and formatting.
Keyword extraction was enhanced using the Mul- tipartiteRank algorithm, which ranks keywords based on their relevance and importance within the text. Additionally, Sense2Vec was employed to un- derstand the semantic meaning of words, allowing for the identification of keywords that are semanti- cally similar to the original text. This step is vital for ensuring that the generated questions are con- textually relevant.
Question generation was also performed using the T5 model, but with a different fine-tuning ap- proach. The model was trained on the SQuAD (Stanford Question Answering Dataset) [2] to understand the context-answer-question format. Given a context and an answer, the model generates a question that could lead to the provided answer within the given context. This process is crucial for creating educational materials and quizzes.
Distractors for multiple-choice questions were gen- erated using two approaches: WordNet and Sense2Vec. WordNet was used to find synonyms and related words that could serve as distrac- tors, while Sense2Vec was used to find semantically similar words. The Maximal Marginal Relevance (MMR) algorithm [3] was applied to select a di- verse set of distractors that are both relevant to the context and distinct from each other.
