Conversations based on search engine result pages  Martin de rjike  University of amsterdam

# Conversations based on search engine result pages 
### Martin de rjike / University of Amsterdam

Web Intelligence 2019 @Thessaloniki, Greece
での招待講演メモ（情報検索分野で有名な先生）

Information retrieval 
Technology to connect people to information 
Search engine recommender system 
Conversational assistant 

More mobile queries 
Spoken query

conversational search 
the idea of search as the conversation has been around since early 1980s 
making information retrieval interfaces feel more natural and convenient for their users 
recently task-oriented dialogue systems and factoid QA task
information goals
navigational, informational, and resource goals 

there is more than task-oriented dialogue systems and question answering 
informational consistency ~40-60% of all goals 


SERP-based conversations 
as our code of interaction changes, how can we support conversations based on search engine result pages 


where are we with SERP-based conversations?

SERP-based conversations 
Search engine 
question understanding 
passage selection passage summarization 
passage generation 

lets simplify things 
cut out the retrieval step and assume that an oracle has given a single document that may be used to inform the response 
background based conversation (BBC)
given some background knowledge eg, an article in the form of free text

back ground-based conversation 
example BBC

a key challenge in BBC is knowledge selection: finding an appropriate background 

responses produced bu extraction-based methods are directly copied from background sentences 
learn two pointers to 

generation based -BBC
influence generation processing the background material sequence-to-sequence models often have a hard time... 
natural responses but they often break semantics units 

advances in generation-based BC
often based on enc-dec models
often adapt local perspective generating one token at a time based on current decoding state 
recently proposed fixes 
teach decoder to select semantic units 
structured knowledge from a knowledge graph in which the background text is grounded 


enc-dec models with global perspective 

- background & context encoder 
- global knowledge selection 
- local knowledge selection 


advances in generation-based BBC
Holl-E dataset 
built for movie chats 
background consists of plots, comments, and reviews about movies collected from different websites 


[（論文）Towards Exploiting Background Knowledge for Building Conversation](https://arxiv.org/pdf/1809.08205.pdf)

background-based conversation
question understanding 

- inferring intent and shifts in intent 
- recognizing entities and relations 
- learning reformulations so as to obtain the best result
- learning to ask in mixded-initiative setting 


passage selection 
single passage may not be enough to answer a question or generate response 
taking broder context into account 


multi-document summarization 
summarize multiple multi-model search results 
optimize for "ideal summary" vs for "successful interaction"


response generation 
repettition problem 
appropriateness problem 
no single correct response 
what to talk about next


challenges and ambitions 
why did you so that 
explainable for developers 
failure analysis
explainable for users 
response/answer explanation


other scenarios 
talk with a dataset 
using conversations to browse large ...
wrap-up 
SERP-grounded conversations 
general idea, recent advances, challenges and ambitions 
work in progress a lot remains to be done