scholarly journals Common Ground, Frames and Slots for Comprehension in Dialogue Systems

2021 ◽  
Author(s):  
Philippe Blache ◽  
Matthis Houlès
Keyword(s):  
Natural Language ◽  
Common Ground ◽  
Bad News ◽  
Shared Knowledge ◽  
Dialogue Systems ◽  
Dialogue System ◽  
Language Understanding ◽  
Language Generation ◽  
The Universe ◽  
Universe Of Discourse

This paper presents a dialogue system for training doctors to break bad news. The originality of this work lies in its knowledge representation. All information known before the dialogue (the universe of discourse, the context, the scenario of the dialogue) as well as the knowledge transferred from the doctor to the patient during the conversation is represented in a shared knowledge structure called common ground, that constitute the core of the system. The Natural Language Understanding and the Natural Language Generation modules of the system take advantage on this structure and we present in this paper different original techniques making it possible to implement them efficiently.

Spoken Language Dialogue Systems

2014 ◽  
Author(s):  
Robert Dale
Keyword(s):  
Language Processing ◽  
Spoken Language ◽  
Dialogue Systems ◽  
Dialogue System ◽  
Computational System ◽  
Language Understanding ◽  
Language Generation ◽  
Key Issues ◽  
Dialogic Interaction ◽  
Build Systems

A spoken language dialogue system is a computational system which engages in multi-turn dialogic interaction with human users using speech as the means of communication. Any system which does this requires many of the capabilities of other natural language processing applications, encompassing both language understanding and language generation; but the nature of spoken language dialogue means that it also poses a number of additional challenges not faced by other applications. This chapter discusses a number of key issues that need to be addressed when attempting to build systems that can engage in natural dialogue, and provides an overview of existing research in these areas.

Spoken Language Dialogue Systems

2014 ◽  
Author(s):  
Robert Dale
Keyword(s):  
Language Processing ◽  
Spoken Language ◽  
Dialogue Systems ◽  
Dialogue System ◽  
Computational System ◽  
Language Understanding ◽  
Language Generation ◽  
Key Issues ◽  
Dialogic Interaction ◽  
Build Systems

A spoken language dialogue system is a computational system which engages in multi-turn dialogic interaction with human users using speech as the means of communication. Any system which does this requires many of the capabilities of other natural language processing applications, encompassing both language understanding and language generation; but the nature of spoken language dialogue means that it also poses a number of additional challenges not faced by other applications. This chapter discusses a number of key issues that need to be addressed when attempting to build systems that can engage in natural dialogue, and provides an overview of existing research in these areas.

scholarly journals Natural language understanding of map navigation queries in Roman Urdu by joint entity and intent determination

2021 ◽  
Vol 7 ◽  
pp. e615
Author(s):  
Javeria Hassan ◽  
Muhammad Ali Tahir ◽  
Adnan Ali
Keyword(s):  
Natural Language ◽  
Natural Language Understanding ◽  
Dialogue Systems ◽  
Word Embeddings ◽  
Dialogue System ◽  
Language Understanding ◽  
Input Sentence ◽  
Navigation Software ◽  
Task Oriented ◽  
Natural Way

Navigation based task-oriented dialogue systems provide users with a natural way of communicating with maps and navigation software. Natural language understanding (NLU) is the first step for a task-oriented dialogue system. It extracts the important entities (slot tagging) from the user’s utterance and determines the user’s objective (intent determination). Word embeddings are the distributed representations of the input sentence, and encompass the sentence’s semantic and syntactic representations. We created the word embeddings using different methods like FastText, ELMO, BERT and XLNET; and studied their effect on the natural language understanding output. Experiments are performed on the Roman Urdu navigation utterances dataset. The results show that for the intent determination task XLNET based word embeddings outperform other methods; while for the task of slot tagging FastText and XLNET based word embeddings have much better accuracy in comparison to other approaches.

The Story Of Computational Narratology

2018 ◽  
Author(s):  
Sharath Srivatsa ◽  
Shyam Kumar V N ◽  
Srinath Srinivasa
Keyword(s):  
Natural Language ◽  
Computational Modeling ◽  
Narrative Structure ◽  
Complex Problem ◽  
General Intelligence ◽  
Language Understanding ◽  
Language Generation ◽  
Narrative Generation ◽  
Growing Body ◽  
Computational Narratology

In recent times, computational modeling of narratives has gained enormous interest in fields like Natural Language Understanding (NLU), Natural Language Generation (NLG), and Artificial General Intelligence (AGI). There is a growing body of literature addressing understanding of narrative structure and generation of narratives. Narrative generation is known to be a far more complex problem than narrative understanding [20].

scholarly journals Prediction and Analysis of Extracting Relations using Spacy Model

2020 ◽  
Vol 8 (6) ◽  
pp. 3281-3287
Keyword(s):  
Natural Language ◽  
Information Extraction ◽  
Performance Measures ◽  
Text Summarization ◽  
Language Understanding ◽  
Language Generation ◽  
Automatic Text Summarization ◽  
Structured Information ◽  
Automatic Text ◽  
F Measure

Text is an extremely rich resources of information. Each and every second, minutes, peoples are sending or receiving hundreds of millions of data. There are various tasks involved in NLP are machine learning, information extraction, information retrieval, automatic text summarization, question-answered system, parsing, sentiment analysis, natural language understanding and natural language generation. The information extraction is an important task which is used to find the structured information from unstructured or semi-structured text. The paper presents a methodology for extracting the relations of biomedical entities using spacy. The framework consists of following phases such as data creation, load and converting the data into spacy object, preprocessing, define the pattern and extract the relations. The dataset is downloaded from NCBI database which contains only the sentences. The created model evaluated with performance measures like precision, recall and f-measure. The model achieved 87% of accuracy in retrieving of entities relation.

Guru

2012 ◽  
pp. 156-171 ◽  
Author(s):  
Andrew M. Olney ◽  
Natalie K. Person ◽  
Arthur C. Graesser
Keyword(s):  
Natural Language Processing ◽  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Natural Language Understanding ◽  
Natural Language Generation ◽  
Language Understanding ◽  
Language Generation ◽  
Processing Techniques

The authors discuss Guru, a conversational expert ITS. Guru is designed to mimic expert human tutors using advanced applied natural language processing techniques including natural language understanding, knowledge representation, and natural language generation.

scholarly journals End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis

2019 ◽  
Vol 33 ◽  
pp. 7346-7353
Author(s):  
Lin Xu ◽  
Qixian Zhou ◽  
Ke Gong ◽  
Xiaodan Liang ◽  
Jianheng Tang ◽  
...  
Keyword(s):  
Natural Language ◽  
Medical Diagnosis ◽  
Expert Knowledge ◽  
Medical Knowledge ◽  
Knowledge Graph ◽  
Dialogue Systems ◽  
Dialogue System ◽  
Self Reports ◽  
Medical Dialogue ◽  
End To End

Beyond current conversational chatbots or task-oriented dialogue systems that have attracted increasing attention, we move forward to develop a dialogue system for automatic medical diagnosis that converses with patients to collect additional symptoms beyond their self-reports and automatically makes a diagnosis. Besides the challenges for conversational dialogue systems (e.g. topic transition coherency and question understanding), automatic medical diagnosis further poses more critical requirements for the dialogue rationality in the context of medical knowledge and symptom-disease relations. Existing dialogue systems (Madotto, Wu, and Fung 2018; Wei et al. 2018; Li et al. 2017) mostly rely on datadriven learning and cannot be able to encode extra expert knowledge graph. In this work, we propose an End-to-End Knowledge-routed Relational Dialogue System (KR-DS) that seamlessly incorporates rich medical knowledge graph into the topic transition in dialogue management, and makes it cooperative with natural language understanding and natural language generation. A novel Knowledge-routed Deep Q-network (KR-DQN) is introduced to manage topic transitions, which integrates a relational refinement branch for encoding relations among different symptoms and symptomdisease pairs, and a knowledge-routed graph branch for topic decision-making. Extensive experiments on a public medical dialogue dataset show our KR-DS significantly beats stateof-the-art methods (by more than 8% in diagnosis accuracy). We further show the superiority of our KR-DS on a newly collected medical dialogue system dataset, which is more challenging retaining original self-reports and conversational data between patients and doctors.

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