scholarly journals A General Planning-Based Framework for Goal-Driven Conversation Assistant

2019 ◽  
Vol 33 ◽  
pp. 9857-9858
Author(s):  
Zhuoxuan Jiang ◽  
Jie Ma ◽  
Jingyi Lu ◽  
Guangyuan Yu ◽  
Yipeng Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
General Framework ◽  
Planning Method ◽  
Conversational Agents ◽  
World Model ◽  
Language Understanding ◽  
Dialogue Management ◽  
Language Generation ◽  
Planning Methods

We propose a general framework for goal-driven conversation assistant based on Planning methods. It aims to rapidly build a dialogue agent with less handcrafting and make the more interpretable and efficient dialogue management in various scenarios. By employing the Planning method, dialogue actions can be efficiently defined and reusable, and the transition of the dialogue are managed by a Planner. The proposed framework consists of a pipeline of Natural Language Understanding (intent labeler), Planning of Actions (with a World Model), and Natural Language Generation (learned by an attention-based neural network). We demonstrate our approach by creating conversational agents for several independent domains.

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 Intent Detection and Slot Filling with Capsule Net Architectures for a Romanian Home Assistant

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1230
Author(s):  
Anda Stoica ◽  
Tibor Kadar ◽  
Camelia Lemnaru ◽  
Rodica Potolea ◽  
Mihaela Dînşoreanu
Keyword(s):  
Neural Network ◽  
Error Analysis ◽  
Natural Language ◽  
Network Model ◽  
Network Architecture ◽  
Natural Language Understanding ◽  
Wide Spread ◽  
Neural Network Architecture ◽  
Language Understanding ◽  
Slot Filling

As virtual home assistants are becoming more popular, there is an emerging need for supporting languages other than English. While more wide-spread or popular languages such as Spanish, French or Hindi are already integrated into existing home assistants like Google Home or Alexa, integration of other less-known languages such as Romanian is still missing. This paper explores the problem of Natural Language Understanding (NLU) applied to a Romanian home assistant. We propose a customized capsule neural network architecture that performs intent detection and slot filling in a joint manner and we evaluate how well it handles utterances containing various levels of complexity. The capsule network model shows a significant improvement in intent detection when compared to models built using the well-known Rasa NLU tool. Through error analysis, we observe clear error patterns that occur systematically. Variability in language when expressing one intent proves to be the biggest challenge encountered by the model.

scholarly journals Human Annotated Dialogues Dataset for Natural Conversational Agents

2020 ◽  
Vol 10 (3) ◽  
pp. 762
Author(s):  
Erinc Merdivan ◽  
Deepika Singh ◽  
Sten Hanke ◽  
Johannes Kropf ◽  
Andreas Holzinger ◽  
...  
Keyword(s):  
Natural Language ◽  
Detailed Analysis ◽  
Human Perception ◽  
Natural Language Understanding ◽  
Industrial Applications ◽  
Benchmark Dataset ◽  
Conversational Agents ◽  
High Quality ◽  
Language Understanding ◽  
Major Drawback

Conversational agents are gaining huge popularity in industrial applications such as digital assistants, chatbots, and particularly systems for natural language understanding (NLU). However, a major drawback is the unavailability of a common metric to evaluate the replies against human judgement for conversational agents. In this paper, we develop a benchmark dataset with human annotations and diverse replies that can be used to develop such metric for conversational agents. The paper introduces a high-quality human annotated movie dialogue dataset, HUMOD, that is developed from the Cornell movie dialogues dataset. This new dataset comprises 28,500 human responses from 9500 multi-turn dialogue history-reply pairs. Human responses include: (i) ratings of the dialogue reply in relevance to the dialogue history; and (ii) unique dialogue replies for each dialogue history from the users. Such unique dialogue replies enable researchers in evaluating their models against six unique human responses for each given history. Detailed analysis on how dialogues are structured and human perception on dialogue score in comparison with existing models are also presented.

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.

scholarly journals Story Realization: Expanding Plot Events into Sentences

2020 ◽  
Vol 34 (05) ◽  
pp. 7375-7382
Author(s):  
Prithviraj Ammanabrolu ◽  
Ethan Tien ◽  
Wesley Cheung ◽  
Zhaochen Luo ◽  
William Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
Human Subjects ◽  
Prior Work ◽  
Generation System ◽  
Language Generation ◽  
Story Generation ◽  
Sequence Of Events ◽  
End To End

Neural network based approaches to automated story plot generation attempt to learn how to generate novel plots from a corpus of natural language plot summaries. Prior work has shown that a semantic abstraction of sentences called events improves neural plot generation and and allows one to decompose the problem into: (1) the generation of a sequence of events (event-to-event) and (2) the transformation of these events into natural language sentences (event-to-sentence). However, typical neural language generation approaches to event-to-sentence can ignore the event details and produce grammatically-correct but semantically-unrelated sentences. We present an ensemble-based model that generates natural language guided by events. We provide results—including a human subjects study—for a full end-to-end automated story generation system showing that our method generates more coherent and plausible stories than baseline approaches 1.

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 Modelling a Conversational Agent with Complex Emotional Intelligence

2020 ◽  
Vol 34 (10) ◽  
pp. 13710-13711
Author(s):  
Billal Belainine ◽  
Fatiha Sadat ◽  
Hakim Lounis
Keyword(s):  
Emotional Intelligence ◽  
Natural Language ◽  
Natural Language Understanding ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Language Understanding ◽  
Leading Role ◽  
Complex Emotions

Chatbots or conversational agents have enjoyed great popularity in recent years. They surprisingly perform sensitive tasks in modern societies. However, despite the fact that they offer help, support, and fellowship, there is a task that is not yet mastered: dealing with complex emotions and simulating human sensations. This research aims to design an architecture for an emotional conversation agent for long-text conversations (multi-turns). This agent is intended to work in areas where the analysis of users feelings plays a leading role. This work refers to natural language understanding and response generation.

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