23 papers with code • 1 benchmarks • 1 datasets
These leaderboards are used to track progress in Conversational Search
First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way.
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.
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.
One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question.
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.
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.
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.