On the Effectiveness of Contextualisation Techniques inrnSpoken Query Spoken Content Retrieval

摘要

In passage and XML retrieval, contextualisation techniquesrnseek to improve the rank of a relevant element by consideringrninformation from its surrounding elements and its containerrndocument. Recent research has demonstrated thatrnsome of these techniques are also particularly eu000bective inrnspoken content retrieval tasks (SCR). However, no previousrnresearch has directly compared contextualisation techniquesrnin an SCR setting, nor has it studied their potential to providernrobustness to speech recognition errors. In this paper,rnwe evaluate diu000berent contextualisation techniques, includingrna recently proposed technique based on positional languagernmodels (PLM) on the task of retrieving relevant spoken passagesrnin response to a spoken query. We study the beneftsrnof these techniques when queries and documents are transcribedrnwith increasingly higher error rates. Experimentalrnresults over the Japanese NTCIR SpokenQuery&Doc collectionrnshow that combining global and local context is bene-rnfcial for SCR and that models usually beneft from usingrnlarger amounts of context in highly noisy conditions.