Cross-genre Document Retrieval: Matching between Conversational and Formal Writings
This addresses a cross-genre retrieval challenge for applications like TV show analysis, but it is incremental as it builds on existing methods.
The paper tackles the problem of retrieving conversational documents using formal queries, achieving over 4% improvement with a structure reranking method.
This paper challenges a cross-genre document retrieval task, where the queries are in formal writing and the target documents are in conversational writing. In this task, a query, is a sentence extracted from either a summary or a plot of an episode in a TV show, and the target document consists of transcripts from the corresponding episode. To establish a strong baseline, we employ the current state-of-the-art search engine to perform document retrieval on the dataset collected for this work. We then introduce a structure reranking approach to improve the initial ranking by utilizing syntactic and semantic structures generated by NLP tools. Our evaluation shows an improvement of more than 4% when the structure reranking is applied, which is very promising.