Although Question Answering (QA) have advanced to the human-level language skills in NLP tasks, there is still a problem: the QA model gets confused when there are similar sentences or paragraphs. Existing studies focus on enhancing the text understanding of the candidate answers to improve the overall performance of the QA models. However, since these methods focus on re-ranking queries or candidate answers, they fail to resolve the confusion when many generated answers are similar to the expected answer. To address these issues, we propose a novel contrastive learning framework called ContrastiveQA that alleviates the confusion problem in answer extraction. We propose a supervised method where we generate positive and negative samples from the candidate answers and the given answer, respectively. We thus introduce ContrastiveQA, which uses contrastive learning with sampling data to reduce incorrect answers. Experimental results on four QA benchmarks show the effectiveness of the proposed method.

Multi-hop question generation (QG) is the process of generating answer related questions, which requires aggregating multiple pieces of information and reasoning from different parts of the texts. This is opposed to single-hop QG which generates questions from sentences containing an answer in a given paragraph. Single-hop QG requires no reasoning or complexity, while multi-hop QG often requires logical reasoning to derive an answer related question, making it a dual task. Not enough research has been made on the multi-hop QG due to its complexity. Also, a question should be created using the question type and words related to the correct answer as a prompt so that multi-hop questions can get more information. In this view, we propose a new type-dependent prompt cycleQAG (cyclic question-answer-generation), with a cycle consistency loss in which QG and Question Answering (QA) are learnt in a cyclic manner. The novelty is that the cycle consistency loss uses the negative cross entropy to generate syntactically diverse questions that enable selecting different word representations. Empirical evaluation on the multi-hop dataset with automatic and human evaluation metrics outperforms the baseline model by about 10.38% based on ROUGE score.