User Intent Prediction in Information-seeking Conversations

  title={User Intent Prediction in Information-seeking Conversations},
  author={Chen Qu and Liu Yang and W. Bruce Croft and Yongfeng Zhang and Johanne R. Trippas and Minghui Qiu},
  journal={Proceedings of the 2019 Conference on Human Information Interaction and Retrieval},
  • Chen Qu, Liu Yang, Minghui Qiu
  • Published 11 January 2019
  • Computer Science
  • Proceedings of the 2019 Conference on Human Information Interaction and Retrieval
Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. 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. In this paper, we investigate two aspects of user intent prediction in an… 

IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems

This paper analyzes user intent patterns in information-seeking conversations and proposes an intent-aware neural response ranking model “IART”, which refers to “Intent-Aware Ranking with Transformers”.

Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations

This paper contributes with two hierarchical taxonomies for classifying user intents and recommender actions respectively based on grounded theory and defines various categories of feature considering content, discourse, sentiment, and context to predict users' intent and satisfaction by comparing different machine learning methods.

An LSTM-based Intent Detector for Conversational Recommender Systems

This paper proposes an LSTM-based Neural Network model and compares its performance to seven baseline Machine Learning (ML) classifiers and experiments revealed the superiority of the L STM model with 95% Accuracy and 94% F1-score on the full dataset despite the relatively small dataset size.

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. Previous conceptual work described

Attentive History Selection for Conversational Question Answering

This work proposes a positional history answer embedding method to encode conversation history with position information using BERT in a natural way, and shows that position information plays an important role in conversation history modeling.

Evaluating Mixed-initiative Conversational Search Systems via User Simulation

This paper proposes a conversational User Simulator, called USi, for automatic evaluation of conversational search systems, capable of automatically answering clarifying questions about the topic throughout the search session, and shows that responses generated by USi are both inline with the underlying information need and comparable to human-generated answers.

MANtIS: a novel information seeking dialogues dataset

Through the research, this paper has built a collection of over 80,000 conversations that fulfill the requirements of a conversational search dataset and benchmarked this dataset on three distinct tasks using multiple baselines.

Controlling the Risk of Conversational Search via Reinforcement Learning

A risk-aware conversational search agent model to balance the risk of answering user’s query and asking clarifying questions is proposed and is able to significantly outperform strong non-risk-aware baselines.

WSDM 2021 Tutorial on Conversational Recommendation Systems

This tutorial focuses on the foundations and algorithms for conversational recommendation, as well as their applications in real-world systems such as search engine, e-commerce and social networks.

Wizard of Search Engine

This work forms a pipeline for CIS with six subtasks: intent detection, keyphrase extraction, action prediction, query selection, passage selection, and response generation, and designs a neural architecture capable of training and evaluating both jointly and separately on the six sub-tasks.



Analyzing and Characterizing User Intent in Information-seeking Conversations

A new dataset designed for this purpose is introduced and used to analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns and finds some highly recurring patterns in user intent during an information- seeking process.

Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems

This paper proposes a learning framework on the top of deep neural matching networks that leverages external knowledge for response ranking in information-seeking conversation systems and incorporates external knowledge into deep neural models with pseudo-relevance feedback and QA correspondence knowledge distillation.

Classifying User Messages For Managing Web Forum Data

This paper studies the problem of classifying a given post as per its purpose in the discussion thread and employs features based on the post's content, structure of the thread, behavior of the participating users and sentiment analysis of post’s content.

Towards Conversational Search and Recommendation: System Ask, User Respond

This paper proposes a System Ask -- User Respond (SAUR) paradigm for conversational search, defines the major components of the paradigm, and designs a unified implementation of the framework for product search and recommendation in e-commerce.

Building Task-Oriented Dialogue Systems for Online Shopping

We present a general solution towards building task-oriented dialogue systems for online shopping, aiming to assist online customers in completing various purchase-related tasks, such as

Modelling Information Needs in Collaborative Search Conversations

A model of conversational information needs (CINs) is proposed based on a synthesis of relevant theories in Information Seeking and Retrieval and several behavioural patterns of CINs are shown based on the proposed model.

Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots

A sequential matching network (SMN) first matches a response with each utterance in the context on multiple levels of granularity, and distills important matching information from each pair as a vector with convolution and pooling operations.

A Joint Model of Intent Determination and Slot Filling for Spoken Language Understanding

A joint model is proposed based on the idea that the intent and semantic slots of a sentence are correlative, and it outperforms the state-of-the-art approaches on both tasks.

Using Context Information for Dialog Act Classification in DNN Framework

This paper proposes several ways of using context information for DA classification, all in the deep learning framework, and demonstrates that incorporating context information significantly improves DA classification and achieves new state-of-the-art performance.

VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text

Interestingly, using the authors' parsimonious rule-based model to assess the sentiment of tweets, it is found that VADER outperforms individual human raters, and generalizes more favorably across contexts than any of their benchmarks.