Conversational Search
44 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
V3CTRON | Data Retrieval & Access System For Flexible Semantic Search & Retrieval Of Proprietary Document Collections Using Natural Language Queries.
serp-ai/V3CTRON-vector-database-embedding-neural-search-retrieval-chatgpt-plugin
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Social Science Research Network (SSRN) 2023
V3CTRON is an open source vector database that allows users to upload text based documents & document collections, which are automatically embedded for super-accurate semantic search & retrieval using natural language queries.
Attentive History Selection for Conversational Question Answering
prdwb/attentive_history_selection
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First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way.
Introducing MANtIS: a novel Multi-Domain Information Seeking Dialogues Dataset
Conversational search is an approach to information retrieval (IR), where users engage in a dialogue with an agent in order to satisfy their information needs.
Attentive Memory Networks: Efficient Machine Reading for Conversational Search
TomKenter/attentive-memory-networks-code
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We argue that the process of building a representation of the conversation can be framed as a machine reading task, where an automated system is presented with a number of statements about which it should answer questions.
User Intent Prediction in Information-seeking Conversations
Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations.
BERT with History Answer Embedding for Conversational Question Answering
prdwb/bert_hae
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One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question.
TREC CAsT 2019: The Conversational Assistance Track Overview
A common theme through the runs is the use of BERT-based neural reranking methods.
Conversations with Search Engines: SERP-based Conversational Response Generation
In this paper, we address the problem of answering complex information needs by conversing conversations with search engines, in the sense that users can express their queries in natural language, and directly receivethe information they need from a short system response in a conversational manner.
Open-Retrieval Conversational Question Answering
We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers.
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.
