Name
Abstract | ||
|---|---|---|
The importance of conversation and conversational models for complex information seeking tasks is well-established within information retrieval, initially to understand user behavior during interactive search [4, 8] and later to improve search accuracy during search sessions [1]. The rapid adoption of a new generation of conversational assistants such as Alexa, Siri, Cortana, Bixby, and Google Assistant increase the scope and importance of conversational approaches to information seeking and also introduce a broad range of new research problems [2]. The TREC Conversational Assistance Track (CAsT) is a new initiative to facilitate Conversational Information Seeking (CIS) research and to create a large-scale reusable test collection for conversational search systems. We define it as a task in which effective response selection requires understanding a question’s context (the dialogue history). It focuses attention on user modeling, analysis of prior retrieval results, transformation of questions into effective queries, and other topics that have been difficult to study with previous datasets.To make this tractable and reusable for the first year of CAsT, we begin with pre-determined conversation trajectories and passage responses. Our target conversations include several rounds of utterances that are coherent in topic and explore relevant information. The primary initial focus is on system understanding of information needs in a conversational format and finding relevant passages leveraging conversational context. The long-term vision of CAsT is to allow natural conversions with mixed-initiative, where the system performs a variety of information actions … |
Year | Venue | DocType |
|---|---|---|
2020 | TREC | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeffrey Dalton | 1 | 3 | 3.85 |
| Chen-Yan Xiong | 2 | 405 | 30.82 |
| James P. Callan | 3 | 6237 | 833.28 |