0 Introduction

1. Described the steps typically involved in handling multiple choice algorithms, including vector representation of text and exploring semantic relationships to determine the probability of each option being the correct answer.

1. Related work

The importance of multiple-choice questions and existing model solutions, including concatenation problems, sequence matching, attention mechanisms, dual attention matching networks, as well as methods such as improving model accuracy through auxiliary knowledge and using contrastive learning to distinguish approximate answers. Emphasis was placed on the application of contrastive learning in machine reading comprehension, utilizing data augmentation methods to distinguish between positive and negative samples, making it easier for the model to make correct judgments.

2 CTM method

The three main components of CTM algorithm are encoding, TM module, and answer selection, as well as the role of CR module. Among them, the TM module integrates information from articles, questions, and options through triple matching operations, and uses a bidirectional attention mechanism for matching. The CR module eliminates the consistency of incorrect options by comparing regularization losses and highlights the difference information between correct and incorrect answers. The total loss of CTM algorithm includes two losses, TM and CR, which can be trained by combining the two losses through hyperparameters.

3 Related Algorithms

The key content is the analysis of three related algorithms, including CNN Matching, Co Matching, and DCMN+, as well as the proposed CTM algorithm. Among them, the CTM algorithm interacts between articles, questions, and options through triple matching and comparative regularization schemes, and distinguishes the difference between correct answers and approximate correct answers in more detail. At the same time, the CTM algorithm also considers the interaction of multiple aspects such as articles, problems, and options.

4 Experimental Results and Analysis

1. Selection of dataset and statistical data

5 Conclusion

The CTM algorithm proposed in this article introduces a context guided triple matching strategy, which estimates the semantic matching between one component (context) and two other components by enumerating triplets through a triple matching model. At the same time, introducing contrastive regularization to extract potential representations of correct answers can keep the model's prediction results away from incorrect answers. The experiment shows that this method has higher accuracy on two benchmark datasets. However, the algorithm is still not concise enough, and future work should focus on designing more concise and efficient reading comprehension algorithms.