Implementing clarification dialogues in open domain question answering

2005 ◽  
Vol 11 (4) ◽  
pp. 343-361 ◽  
Author(s):  
MARCO DE BONI ◽  
SURESH MANANDHAR
Keyword(s):  
Question Answering ◽  
Open Domain ◽  
Question Answering Systems

We examine the implementation of clarification dialogues, a mechanism for ensuring that question answering systems take into account user goals by allowing them to ask series of related questions either by refining or expanding on previous questions with follow-up questions, in the context of open domain Question Answering systems. We develop an algorithm for clarification dialogue recognition through the analysis of collected data on clarification dialogues and examine the importance of clarification dialogue recognition for question answering. The algorithm is evaluated and shown to successfully recognize the start and continuation of clarification dialogues in 94% of cases. We then show the usefulness of the algorithm by demonstrating how the recognition of clarification dialogues can simplify the task of answer retrieval.

scholarly journals Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 94341-94356
Author(s):  
Zhen Huang ◽  
Shiyi Xu ◽  
Minghao Hu ◽  
Xinyi Wang ◽  
Jinyan Qiu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Question Answering ◽  
Open Domain ◽  
Question Answering Systems ◽  
Recent Trends

scholarly journals Designing an interactive open-domain question answering system

2009 ◽  
Vol 15 (1) ◽  
pp. 73-95 ◽  
Author(s):  
S. QUARTERONI ◽  
S. MANANDHAR
Keyword(s):  
Web Search ◽  
Question Answering ◽  
Management Model ◽  
Open Domain ◽  
Wizard Of Oz ◽  
Question Answering System ◽  
Design And Implementation ◽  
Web Search Engine ◽  
Interactive Question

AbstractInteractive question answering (QA), where a dialogue interface enables follow-up and clarification questions, is a recent although long-advocated field of research. We report on the design and implementation of YourQA, our open-domain, interactive QA system. YourQA relies on a Web search engine to obtain answers to both fact-based and complex questions, such as descriptions and definitions. We describe the dialogue moves and management model making YourQA interactive, and discuss the architecture, implementation and evaluation of its chat-based dialogue interface. Our Wizard-of-Oz study and final evaluation results show how the designed architecture can effectively achieve open-domain, interactive QA.

scholarly journals A new Passage Retrieval Method in Arabic Question Answering Systems

2020 ◽  
Author(s):  
Lana Alsabbagh ◽  
Oumayma AlDakkak ◽  
Nada Ghneim
Keyword(s):  
Query Expansion ◽  
Question Answering ◽  
Document Retrieval ◽  
Open Domain ◽  
Retrieval Method ◽  
Passage Retrieval ◽  
Question Analysis ◽  
The Core ◽  
Question Answering Systems ◽  
Retrieval Phase

Abstract In this paper, we present our approach to improve the performance of open-domain Arabic Question Answering systems. We focus on the passage retrieval phase which aims to retrieve the most related passages to the correct answer. To extract passages that are related to the question, the system passes through three phases: Question Analysis, Document Retrieval and Passage Retrieval. We define the passage as the sentence that ends with a dot ".". In the Question Processing phase, we applied the traditional NLP steps of tokenization, stopwords and unrelated symbols removal, and replacing the question words with their stems. We also applied Query Expansion by adding synonyms to the question words. In the Document Retrieval phase, we used the Vector Space Model (VSM) with TF-IDF vectorizer and cosine similarity. For the Passage Retrieval phase, which is the core of our system, we measured the similarity between passages and the question by a combination of the BM25 ranker and Word Embedding approach. We tested our system on ACRD dataset, which contains 1395 questions in different domains, and the system was able to achieve correct results with a precision of 92.2% and recall of 79.9% in finding the top-3 related passages for the query.

Natural Language Intelligences

2008 ◽  
pp. 258-275
Author(s):  
Azamat Abdoullaev
Keyword(s):  
Web Search ◽  
Question Answering ◽  
Database Systems ◽  
General Purpose ◽  
Knowledge Systems ◽  
Open Domain ◽  
Keyword Query ◽  
Trade Name ◽  
Question Answering Systems ◽  
Special Value

Of all possible intelligent NL applications and semantic artifacts, a special value is today ascribed to building the question answering systems (Q&A) with broad and wide ontological learning (Onto Query Project, 2004), classified as open-domain Q&A knowledge systems [Question Answering, From Wikipedia, 2006]. This line of research is considered as upgrading of a traditional keyword query processing in database systems, as endowing the Web search engines with answering deduction capacities. Ideally, such a general-purpose Q&A agent should be able to cover questions (matters, subjects, topics, issues, themes) from any branch of knowledge and domain of interest by giving answers to any meaningful questions, like the Digital Aristotle, “an application that will encompass much of the world’s scientific knowledge and be capable of answering novel questions and advanced problemsolving” (Project Halo, 2004). The trade name of the Digital Aristotle was inspired by the scholar mostly admired for the depth and width of his perception, whose mind spread over ontology, physics, logics, epistemology, biology, zoology, medicine, psychology, literary theory, politics, and art.

A Brief Survey of Question Answering Systems

2021 ◽  
Vol 12 (5) ◽  
pp. 01-07
Author(s):  
Michael Caballero
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Open Domain ◽  
Knowledge Based ◽  
Current State ◽  
Introductory Overview ◽  
Building Systems ◽  
Question Answering Systems

Question Answering (QA) is a subfield of Natural Language Processing (NLP) and computer science focused on building systems that automatically answer questions from humans in natural language. This survey summarizes the history and current state of the field and is intended as an introductory overview of QA systems. After discussing QA history, this paper summarizes the different approaches to the architecture of QA systems -- whether they are closed or open-domain and whether they are text-based, knowledge-based, or hybrid systems. Lastly, some common datasets in this field are introduced and different evaluation metrics are discussed.

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