Conversational Search
53 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Conversational Search
| Trend | Dataset | Best Model | Paper | Code | Compare |
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CANARD
Most implemented papers
Summarizing and Exploring Tabular Data in Conversational Search
We propose to generate natural language summaries as answers to describe the complex information contained in a table.
Query Resolution for Conversational Search with Limited Supervision
nickvosk/sigir2020-query-resolution
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Context from the conversational history can be used to arrive at a better expression of the current turn query, defined as the task of query resolution.
Controlling the Risk of Conversational Search via Reinforcement Learning
In this work, we propose a risk-aware conversational search agent model to balance the risk of answering user's query and asking clarifying questions.
Open-Domain Conversational Search Assistant with Transformers
novasearch/conversational-search-assistant-transformers
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Open-domain conversational search assistants aim at answering user questions about open topics in a conversational manner.
Role of Attentive History Selection in Conversational Information Seeking
The rise of intelligent assistant systems like Siri and Alexa have led to the emergence of Conversational Search, a research track of Information Retrieval (IR) that involves interactive and iterative information-seeking user-system dialog.
User Engagement Prediction for Clarification in Search
isekulic/mimics-EL-benchmark
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Prompting the user for clarification in a search session can be very beneficial to the system as the user's explicit feedback helps the system improve retrieval massively.
Meta-evaluation of Conversational Search Evaluation Metrics
Conversational search systems, such as Google Assistant and Microsoft Cortana, enable users to interact with search systems in multiple rounds through natural language dialogues.
Few-Shot Conversational Dense Retrieval
thunlp/ConvDR
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In this paper, we present a Conversational Dense Retrieval system, ConvDR, that learns contextualized embeddings for multi-turn conversational queries and retrieves documents solely using embedding dot products.
High-Quality Diversification for Task-Oriented Dialogue Systems
smt-HS/I-SEE
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Many task-oriented dialogue systems use deep reinforcement learning (DRL) to learn policies that respond to the user appropriately and complete the tasks successfully.
MConv: An Environment for Multimodal Conversational Search across Multiple Domains
lizi-git/MMConv
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SIGIR 2021
Second, a set of benchmark results for dialogue state tracking, conversational recommendation, response generation as well as a unified model for multiple tasks are reported.
