Dynamic Graph Reasoning for Conversational Open-Domain Question Answering

2022 ◽  
Vol 40 (4) ◽  
pp. 1-24
Author(s):  
Yongqi Li ◽  
Wenjie Li ◽  
Liqiang Nie
Keyword(s):  
Information Needs ◽  
Question Answering ◽  
Relevant Information ◽  
Conversational Agents ◽  
Open Domain ◽  
Transition Flow ◽  
Dynamic Graph ◽  
Answer Extraction ◽  
Exact Answer ◽  
The Web

In recent years, conversational agents have provided a natural and convenient access to useful information in people’s daily life, along with a broad and new research topic, conversational question answering (QA). On the shoulders of conversational QA, we study the conversational open-domain QA problem, where users’ information needs are presented in a conversation and exact answers are required to extract from the Web. Despite its significance and value, building an effective conversational open-domain QA system is non-trivial due to the following challenges: (1) precisely understand conversational questions based on the conversation context; (2) extract exact answers by capturing the answer dependency and transition flow in a conversation; and (3) deeply integrate question understanding and answer extraction. To address the aforementioned issues, we propose an end-to-end Dynamic Graph Reasoning approach to Conversational open-domain QA (DGRCoQA for short). DGRCoQA comprises three components, i.e., a dynamic question interpreter (DQI), a graph reasoning enhanced retriever (GRR), and a typical Reader, where the first one is developed to understand and formulate conversational questions while the other two are responsible to extract an exact answer from the Web. In particular, DQI understands conversational questions by utilizing the QA context, sourcing from predicted answers returned by the Reader, to dynamically attend to the most relevant information in the conversation context. Afterwards, GRR attempts to capture the answer flow and select the most possible passage that contains the answer by reasoning answer paths over a dynamically constructed context graph . Finally, the Reader, a reading comprehension model, predicts a text span from the selected passage as the answer. DGRCoQA demonstrates its strength in the extensive experiments conducted on a benchmark dataset. It significantly outperforms the existing methods and achieves the state-of-the-art performance.

scholarly journals Finding Answers to Questions, in Text Collections or Web, in Open Domain or Specialty Domains

2012 ◽  
pp. 344-370
Author(s):  
Brigitte Grau
Keyword(s):  
Natural Language ◽  
Information Management ◽  
Question Answering ◽  
Target Language ◽  
Open Domain ◽  
Text Collections ◽  
Factual Question ◽  
Structured Knowledge ◽  
Answering Questions ◽  
The Web

This chapter is dedicated to factual question answering, i.e., extracting precise and exact answers to question given in natural language from texts. A question in natural language gives more information than a bag of word query (i.e., a query made of a list of words), and provides clues for finding precise answers. The author first focuses on the presentation of the underlying problems mainly due to the existence of linguistic variations between questions and their answerable pieces of texts for selecting relevant passages and extracting reliable answers. The author first presents how to answer factual question in open domain. The author also presents answering questions in specialty domain as it requires dealing with semi-structured knowledge and specialized terminologies, and can lead to different applications, as information management in corporations for example. Searching answers on the Web constitutes another application frame and introduces specificities linked to Web redundancy or collaborative usage. Besides, the Web is also multilingual, and a challenging problem consists in searching answers in target language documents other than the source language of the question. For all these topics, this chapter presents main approaches and the remaining problems.

scholarly journals NELL’s subcategories from a question answering environment

2018 ◽  
Author(s):  
Wesley W. O. Souza ◽  
Diorge Brognara ◽  
João A. Leite ◽  
Estevam R. Hruschka Jr.
Keyword(s):  
Machine Learning ◽  
Knowledge Base ◽  
Data Storage ◽  
Language Processing ◽  
Processing Speed ◽  
Question Answering ◽  
Learning Algorithm ◽  
Conversational Agents ◽  
Learning Agent ◽  
The Web

With advances in machine learning, natural language processing, processing speed, and amount of data storage, conversational agents are being used in applications that were not possible to perform within a few years. NELL, a machine learning agent who learns to read the web, today has a considerably large ontology and while it can be used for multiple fact queries, it is also possible to expand it further and specialize its knowledge. One of the first steps to succeed is to refine existing knowledge in NELL’s knowledge base so that future communication between it and humans is as natural as possible. This work describes the results of an experiment where we investigate which machine learning algorithm performs best in the task of classifying candidate words to subcategories in the NELL knowledge base.

scholarly journals Exploring the Web and Semantic Knowledge-Driven Automatic Question Answering System

2018 ◽  
Vol 7 (3.6) ◽  
pp. 379
Author(s):  
S Jayalakshmi ◽  
Ananthi Sheshaayee
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Information Source ◽  
Semantic Knowledge ◽  
High Accuracy ◽  
Question Answering System ◽  
Exact Answer ◽  
Ontology Structure ◽  
The Right ◽  
The Web

The growth of information retrieval from the web sources are increased day by day, proving an effective and efficient way to the user for retrieving relevant documents from the web is an art. Asking the right question and retrieving a right answer to the posted query is a service which provide by the Natural Language Processing. Question Answering System is one of the best ways to identify the candidate answer with high accuracy. The web and Semantic Knowledge Driven Question Answering System (QAS) used to determine the candidate answer for the posted query in the NLP tools.  This method includes Query expansion techniques and entity linking method to identify the information source snippets with ontology structure, also ranking the sentences by applying conditional probability between query and Answer to identify the optimal answer from the web corpus. The result provides an exact answer with high accuracy than the baseline method.  

Classification of means and methods of the Web semantic retrieval

2017 ◽  
pp. 030-050
Author(s):  
J.V. Rogushina ◽  
Keyword(s):  
Search Engines ◽  
Domain Knowledge ◽  
Information Needs ◽  
Web Search ◽  
User Interaction ◽  
Query Languages ◽  
Semantic Search ◽  
Semantic Retrieval ◽  
The Web

Problems associated with the improve ment of information retrieval for open environment are considered and the need for it’s semantization is grounded. Thecurrent state and prospects of development of semantic search engines that are focused on the Web information resources processing are analysed, the criteria for the classification of such systems are reviewed. In this analysis the significant attention is paid to the semantic search use of ontologies that contain knowledge about the subject area and the search users. The sources of ontological knowledge and methods of their processing for the improvement of the search procedures are considered. Examples of semantic search systems that use structured query languages (eg, SPARQL), lists of keywords and queries in natural language are proposed. Such criteria for the classification of semantic search engines like architecture, coupling, transparency, user context, modification requests, ontology structure, etc. are considered. Different ways of support of semantic and otology based modification of user queries that improve the completeness and accuracy of the search are analyzed. On base of analysis of the properties of existing semantic search engines in terms of these criteria, the areas for further improvement of these systems are selected: the development of metasearch systems, semantic modification of user requests, the determination of an user-acceptable transparency level of the search procedures, flexibility of domain knowledge management tools, increasing productivity and scalability. In addition, the development of means of semantic Web search needs in use of some external knowledge base which contains knowledge about the domain of user information needs, and in providing the users with the ability to independent selection of knowledge that is used in the search process. There is necessary to take into account the history of user interaction with the retrieval system and the search context for personalization of the query results and their ordering in accordance with the user information needs. All these aspects were taken into account in the design and implementation of semantic search engine "MAIPS" that is based on an ontological model of users and resources cooperation into the Web.

Early Childhood Development Practitioners’ Information Practices through GMISP Lens: The Case of Ekurhuleni Metropolitan Municipality

Libri ◽  
2018 ◽  
Vol 68 (3) ◽  
pp. 205-217
Author(s):  
Kepi Madumo ◽  
Constance Bitso
Keyword(s):  
Information Seeking ◽  
Information Needs ◽  
Early Childhood Development ◽  
Information Service ◽  
Relevant Information ◽  
School Libraries ◽  
Department Of Education ◽  
Childhood Development ◽  
Group Discussions ◽  
Key Informant

Abstract In the interest of developing relevant information services for ECD practitioners in Ekurhuleni Metropolitan Municipality (EMM), as ECD is one of the national priorities, a study was conducted to ascertain their information needs and information-seeking behaviour. Using Leckie, Pettigrew and Sylvain’s General Model of the Information Seeking of Professionals (GMISP) as the theoretical framework, and situated within interpretivist paradigm, the study took a qualitative approach to collect data, with the results based on group discussions and an interview with a key informant. The research focused on establishing Grade R practitioners’ information needs, with information sources they often consulted, actions and strategies used when seeking information, as well as challenges they face when seeking information. Grade R practitioners need information to increase their knowledge for optimum performance of their duties. To satisfy the demand for information, it is recommended that the EMM libraries and Gauteng Department of Education school libraries should consider a coordinated and accessible library and information service (LIS) that supports ECD practitioners. The plans and design of LIS in the EMM should accommodate the information needs expressed by the Grade R practitioners.

scholarly journals Don’t Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering

2020 ◽  
Author(s):  
Yuxiang Wu ◽  
Pasquale Minervini ◽  
Pontus Stenetorp ◽  
Sebastian Riedel
Keyword(s):  
Question Answering ◽  
Open Domain ◽  
Adaptive Computation
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