| Abstrakt: |
This paper presents a study of answer retrieval systems, which aim to automatically retrieve the most relevant answer(s) to a given question from a large collection of unstructured text data. Answer retrieval is a critical component in question-answering systems and has wideranging applications, from search engines to chatbots. We begin by providing an overview of the various techniques used in answer retrieval systems, including information retrieval models, neural network-based methods, and hybrid approaches. We then discuss the specific challenges faced by answer retrieval, such as the need to handle complex queries, the impact of noise and redundancy in the data, and the trade-offs between precision and recall. Next, we review the major datasets and evaluation metrics used in the field, highlighting their strengths and weaknesses. Finally, we discuss recent advances in answer retrieval systems, including the use of pre-trained language models, domain-specific knowledge, and multi-stage retrieval pipelines. Through our analysis, we identify key research directions for the continued development of answer retrieval systems, including the integration of conversational context, the use of multimodal data, and the development of more effective evaluation metrics. [ABSTRACT FROM AUTHOR] |
