SMM at the NTCIR-18 U4 Task

This paper presents the methods and results of Team SMM for the U4 task at NTCIR-18. In the Table Retrieval subtask, we designed methods for table retrieval using a cell-level multi-vector retriever and a single-vector retriever to enhance retrieval accuracy. The retriever first narrows down candidate tables to the top 10 based on retrieval score. Then, a cross-encoder-based reranker classifies these candidates into three categories: positive, negative, and hard negative. Finally, the table with the highest probability of being positive is selected as the final retrieved result. For the Table Question Answering subtask, we employ a T5-based model for answer generation to produce multiple candidate answers and introduce a Cell ID Estimator that identifies which cells in the table were used as the basis for generating each candidate answer by leveraging cell, row, and column embeddings. The estimator then selects the final answer based on the highest supporting cell score. The test set is divided into public and private splits, inspired by Kaggle's evaluation methodology. The public split is used for leaderboard updates, while the private split ensures robustness by preventing models from overfitting to leaderboard data. Final evaluations include both splits to provide a more reliable assessment of model performance. In the formal run, our method achieved an accuracy of 97.70\% (public) and 97.55\% (private) for Table Retrieval (ID 62), and for Table Question Answering, 86.34\% and 86.57\% on cell ID and value prediction, respectively, on the public split, with corresponding accuracies of 82.76\% and 81.94\% on the private split.