155 citations · 177 across the 12 of their papers we have counts for
9 papers · 1 filter
Scaling Laws For Dense Retrieval
Yan Fang, Jingtao Zhan, Qingyao Ai +4
Scaling up neural models has yielded significant advancements in a wide array of tasks, particularly in language generation. Previous studies have found that the performance of neu…
An Intent Taxonomy of Legal Case Retrieval
Yunqiu Shao, Haitao Li, Yueyue Wu +5
Legal case retrieval is a special Information Retrieval~(IR) task focusing on legal case documents. Depending on the downstream tasks of the retrieved case documents, users' inform…
Constructing Tree-based Index for Efficient and Effective Dense Retrieval
Haitao Li, Qingyao Ai, Jingtao Zhan +4
Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness…
Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval
Jingtao Zhan, Qingyao Ai, Yiqun Liu +4
Recent advance in Dense Retrieval (DR) techniques has significantly improved the effectiveness of first-stage retrieval. Trained with large-scale supervised data, DR models can enc…
Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy
Wenqiang Lei, Yao Zhang, Feifan Song +5
Proactive dialogue system is able to lead the conversation to a goal topic and has advantaged potential in bargain, persuasion and negotiation. Current corpus-based learning manner…
Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models
Jingtao Zhan, Xiaohui Xie, Jiaxin Mao +4
A retrieval model should not only interpolate the training data but also extrapolate well to the queries that are different from the training data. While neural retrieval models ha…
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
Chongming Gao, Shijun Li, Wenqiang Lei +6
The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive. This is…
Interpreting Dense Retrieval as Mixture of Topics
Jingtao Zhan, Jiaxin Mao, Yiqun Liu +3
Dense Retrieval (DR) reaches state-of-the-art results in first-stage retrieval, but little is known about the mechanisms that contribute to its success. Therefore, in this work, we…
POSSCORE: A Simple Yet Effective Evaluation of Conversational Search with Part of Speech Labelling
Zeyang Liu, Ke Zhou, Jiaxin Mao +1
Conversational search systems, such as Google Assistant and Microsoft Cortana, provide a new search paradigm where users are allowed, via natural language dialogues, to communicate…