RSpell: Retrieval-augmented Framework for Domain Adaptive Chinese Spelling Check

Chinese Spelling Check (CSC) refers to the detection and correction of spelling errors in Chinese texts. In practical application scenarios, it is important to make CSC models have the ability to correct errors across different domains. In this paper, we propose a retrieval-augmented spelling check framework called RSpell, which searches corresponding domain terms and incorporates them into CSC models. Specifically, we employ pinyin fuzzy matching to search for terms, which are combined with the input and fed into the CSC model. Then, we introduce an adaptive process control mechanism to dynamically adjust the impact of external knowledge on the model. Additionally, we develop an iterative strategy for the RSpell framework to enhance reasoning capabilities. We conducted experiments on CSC datasets in three domains: law, medicine, and official document writing. The results demonstrate that RSpell achieves state-of-the-art performance in both zero-shot and fine-tuning scenarios, demonstrating the effectiveness of the retrieval-augmented CSC framework. Our code is available at https://github.com/47777777/Rspell

An Ontology-based Approach for Personalized Itinerary Search

Personalization plays an important role in information retrieval systems. In the field of transportation, and more specifically multimodal transportation, personalization represents an efficient way for travelers to find appropriate routes. Providing travelers with the relevant information to their needs and preferences is challenging for transportation systems. In this paper, we propose an ontology-based approach for personalized itinerary search. Our proposal is based on modeling each user using an ontological fuzzy modular profile that incorporates a set of fuzzy modules representing several aspects of the user’s description. The approach is applied in the transportation domain and integrates a new method of matching between the profile ontology and the domain ontology to obtain personalized responses for individual user profiles. Our proposal was implemented and evaluated. Obtained results show that personalization coupled with ontology matching enables an improvement of query reformulation