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Neural Search Talks [5] — Few-shot Conversational Dense Retrieval (ConvDR) w/ Antonios Krasakis

In this episode of Neural Search Talks, we discuss Conversational Search with our usual cohosts Andrew Yates and Sergi Castella i Sapé; along with a special guest Antonios Minas Krasakis, PhD candidate at the University of Amsterdam specializing in Conversational Search.

We center our discussion around the ConvDR paper: "Few-Shot Conversational Dense Retrieval" by Shi Yu et al. which was the first work to perform Conversational Search without relying on an explicit conversation-to-query rewriting step.


00:00 Introduction

00:50 Conversational AI and Conversational Search

05:40 What makes Conversational Search challenging

07:00 ConvDR paper introduction

10:10 Passage representations

11:30 Conversation representations: query rewriting

19:12 ConvDR novel proposed method: teacher-student setup with ANCE

22:50 Datasets and benchmarks: CAsT, CANARD

25:32 Teacher-student advantages and knowledge distillation vs. ranking loss functions

28:09 TREC CAsT and OR-QuAC

35:50 Metrics: MRR, NDCG, holes@10

44:16 Main Results on CAsT and OR-QuAC (Table 2)

57:35 Ablations on combinations of loss functions (Table 4)

1:00:10 How fast is ConvDR? (Table 3)

1:02:40 Qualitative analysis on ConvDR embeddings (Figure 4)

1:04:50 How has this work aged? More recent works in similar directions: Contextualized Quesy Embeddings for Conversational Search.

1:07:02 Is "end-to-end" the silver-bullet for Conversational Search?

1:10:04 Will conversational search become more mainstream?

1:18:44 Latest initiatives for Conversational Search

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