Generative Chat Bot Implementation Using Deep Recurrent Neural Networks and Natural Language Understanding

2019 ◽  
Author(s):  
Niranjan Zalake ◽  
Gautam Naik
Keyword(s):  
Neural Networks ◽  
Natural Language ◽  
Recurrent Neural Networks ◽  
Natural Language Understanding ◽  
Language Understanding ◽  
Chat Bot

scholarly journals Development of Dialogue Management System for Banking Services

2021 ◽  
Vol 11 (22) ◽  
pp. 10995
Author(s):  
Samir Rustamov ◽  
Aygul Bayramova ◽  
Emin Alasgarov
Keyword(s):  
Neural Networks ◽  
Natural Language ◽  
Conditional Random Fields ◽  
Natural Language Understanding ◽  
Text Messages ◽  
Entity Extraction ◽  
Language Understanding ◽  
User Intention ◽  
Dialogue Management ◽  
Dialogue Manager

Rapid increase in conversational AI and user chat data lead to intensive development of dialogue management systems (DMS) for various industries. Yet, for low-resource languages, such as Azerbaijani, very little research has been conducted. The main purpose of this work is to experiment with various DMS pipeline set-ups to decide on the most appropriate natural language understanding and dialogue manager settings. In our project, we designed and evaluated different DMS pipelines with respect to the conversational text data obtained from one of the leading retail banks in Azerbaijan. In the work, the main two components of DMS—Natural language Understanding (NLU) and Dialogue Manager—have been investigated. In the first step of NLU, we utilized a language identification (LI) component for language detection. We investigated both built-in LI methods such as fastText and custom machine learning (ML) models trained on the domain-based dataset. The second step of the work was a comparison of the classic ML classifiers (logistic regression, neural networks, and SVM) and Dual Intent and Entity Transformer (DIET) architecture for user intention detection. In these experiments we used different combinations of feature extractors such as CountVectorizer, Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer, and word embeddings for both word and character n-gram based tokens. To extract important information from the text messages, Named Entity Extraction (NER) component was added to the pipeline. The best NER model was chosen among conditional random fields (CRF) tagger, deep neural networks (DNN), models and build in entity extraction component inside DIET architecture. Obtained entity tags fed to the Dialogue Management module as features. All NLU set-ups were followed by the Dialogue Management module that contains a Rule-based Policy to handle FAQs and chitchats as well as a Transformer Embedding Dialogue (TED) Policy to handle more complex and unexpected dialogue inputs. As a result, we suggest a DMS pipeline for a financial assistant, which is capable of identifying intentions, named entities, and a language of text followed by policies that allow generating a proper response (based on the designed dialogues) and suggesting the best next action.

scholarly journals Exploring and Improving Robustness of Multi Task Deep Neural Networks via Domain Agnostic Defenses

2019 ◽  
Author(s):  
Kashyap Coimbatore Murali
Keyword(s):  
Neural Networks ◽  
Natural Language ◽  
Deep Neural Networks ◽  
Natural Language Understanding ◽  
General Purpose ◽  
Absolute Difference ◽  
Language Understanding ◽  
Spell Checker

In this paper I explore the robustness of the Multi-Task Deep Neural Networks (MT-DNN) againstnon-targeted adversarial attacks across Natural Language Understanding (NLU) tasks as well assome possible ways to defend against them. Liu et al., have shown that the Multi-Task Deep NeuralNetwork, due to the regularization effect produced when training as a result of it’s cross task data, ismore robust than a vanilla BERT model trained only on one task (1.1%-1.5% absolute difference).I then show that although the MT-DNN has generalized better, making it easily transferable acrossdomains and tasks, it can still be compromised as after only 2 attacks (1-character and 2-character)the accuracy drops by 42.05% and 32.24% for the SNLI and SciTail tasks. Finally I propose a domainadaptable defense which restores the model’s accuracy (36.75% and 25.94% respectively) as opposedto a general purpose defense or an off-the-shelf spell checker.

scholarly journals Designing and Implementing Conversational Intelligent Chat-bot Using Natural Language Processing

2021 ◽  
pp. 262-266
Author(s):  
Asoke Nath ◽  
Rupamita Sarkar ◽  
Swastik Mitra ◽  
Rohitaswa Pradhan
Keyword(s):  
Neural Networks ◽  
Natural Language ◽  
Language Processing ◽  
Deep Neural Networks ◽  
Natural Language Understanding ◽  
Natural Language Generation ◽  
Representation Learning ◽  
Language Understanding ◽  
Language Generation ◽  
User Input

In the early days of Artificial Intelligence, it was observed that tasks which humans consider ‘natural’ and ‘commonplace’, such as Natural Language Understanding, Natural Language Generation and Vision were the most difficult task to carry over to computers. Nevertheless, attempts to crack the proverbial NLP nut were made, initially with methods that fall under ‘Symbolic NLP’. One of the products of this era was ELIZA. At present the most promising forays into the world of NLP are provided by ‘Neural NLP’, which uses Representation Learning and Deep Neural networks to model, understand and generate natural language. In the present paper the authors tried to develop a Conversational Intelligent Chatbot, a program that can chat with a user about any conceivable topic, without having domain-specific knowledge programmed into it. This is a challenging task, as it involves both ‘Natural Language Understanding’ (the task of converting natural language user input into representations that a machine can understand) and subsequently ‘Natural Language Generation’ (the task of generating an appropriate response to the user input in natural language). Several approaches exist for building conversational chatbots. In the present paper, two models have been used and their performance has been compared and contrasted. The first model is purely generative and uses a Transformer-based architecture. The second model is retrieval-based, and uses Deep Neural Networks.

The Distinction between Linguistic and Conceptual Semantics in Medical Terminology and its Implication for NLP-Based Knowledge Acquisition

1998 ◽  
Vol 37 (04/05) ◽  
pp. 327-333 ◽  
Author(s):  
F. Buekens ◽  
G. De Moor ◽  
A. Waagmeester ◽  
W. Ceusters
Keyword(s):  
Natural Language ◽  
Knowledge Acquisition ◽  
Natural Language Understanding ◽  
Knowledge Bases ◽  
Linguistic Knowledge ◽  
Medical Terminology ◽  
Language Understanding ◽  
Conceptual Semantics

AbstractNatural language understanding systems have to exploit various kinds of knowledge in order to represent the meaning behind texts. Getting this knowledge in place is often such a huge enterprise that it is tempting to look for systems that can discover such knowledge automatically. We describe how the distinction between conceptual and linguistic semantics may assist in reaching this objective, provided that distinguishing between them is not done too rigorously. We present several examples to support this view and argue that in a multilingual environment, linguistic ontologies should be designed as interfaces between domain conceptualizations and linguistic knowledge bases.

Automated Coding of Patient Discharge Summaries Using Conceptual Graphs

1995 ◽  
Vol 34 (04) ◽  
pp. 345-351 ◽  
Author(s):  
A. Burgun ◽  
L. P. Seka ◽  
D. Delamarre ◽  
P. Le Beux
Keyword(s):  
Natural Language ◽  
Patient Discharge ◽  
Natural Language Understanding ◽  
Assessment Procedure ◽  
Coding System ◽  
Language Understanding ◽  
Processing Scheme ◽  
Automated Coding ◽  
Conceptual Graph ◽  
Discharge Summaries

Abstract:In medicine, as in other domains, indexing and classification is a natural human task which is used for information retrieval and representation. In the medical field, encoding of patient discharge summaries is still a manual time-consuming task. This paper describes an automated coding system of patient discharge summaries from the field of coronary diseases into the ICD-9-CM classification. The system is developed in the context of the European AIM MENELAS project, a natural-language understanding system which uses the conceptual-graph formalism. Indexing is performed by using a two-step processing scheme; a first recognition stage is implemented by a matching procedure and a secondary selection stage is made according to the coding priorities. We show the general features of the necessary translation of the classification terms in the conceptual-graph model, and for the coding rules compliance. An advantage of the system is to provide an objective evaluation and assessment procedure for natural-language understanding.

Sign in / Sign up

Export Citation Format

Share Document