OWI: Open-World Intent Identification Framework for Dialog Based System

Parmar, Jitendra; Soni, Sanskar; Chouhan, Satyendra Singh

doi:10.1007/978-3-030-66665-1_21

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Their experiment results yielded a better performance as compared to the existing methods. Parmar et al [13] proposed an intent identification framework, based on a deep learning model. Their research goal is to employ "CNN" with one-vsrest as an output layer to identify unseen classes in an automated dialoguebased system.…”

Section: Intent Detectionmentioning

confidence: 99%

Primitive-based Learning Model for Intent Detection in Human-AI Agent Negotiation

Waseem,

Shoaib,

Arshad

2023

Preprint

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Intent detection task is one of the core problems in the natural language understanding (NLU) as it enables the conversational agents to comprehend the human language for meaningful interaction. But human-AI agent communication is not fully rational due to the dynamic behaviour of the human. Several research works have been carried out to ascertain intent detection methods for social media (Twitter, Facebook) chat, email conversation, and product reviews etc. However, we cannot deploy existing intent detection methods in a real-time human-AI agent negotiation scenario. In this article, we present the first primitive-based learning model, which analyses human (buyer) dynamic behavioral patterns at content level, and automatically predict their purchase or non-purchase intent during a bilateral business negotiation. Thereby, by evaluating and keeping track of two key human behaviors “affective” & “disappointment”, an AI agent can therefore, better comprehend the human intent and engage them in the negotiation, and achieve more agreements (deals). We employ negotiation primitives by setting them to pertinent (human-human) communication units for enhancing the cognition and decision-making capability of AI-agents and evaluate its performance on various machine learning models including BERT. With 93% accuracy, the proposed model itself has proven to be a more efficient method for classifying e-negotiation behavioral patterns.

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Section: Intent Detectionmentioning

confidence: 99%

Primitive-based Learning Model for Intent Detection in Human-AI Agent Negotiation

Waseem,

Shoaib,

Arshad

2023

Preprint

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“…Textual contents can either be sentences, paragraphs, or queries [2], [3]. There are many real-world applications of text classification such as sentiment analysis [4], news classification [5], intent classification [6], spam detection [7], and so on.…”

Section: Introductionmentioning

confidence: 99%

TextConvoNet:A Convolutional Neural Network based Architecture for Text Classification

Soni¹,

Chouhan²,

Rathore³

2022

Preprint

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In recent years, deep learning-based models have significantly improved the Natural Language Processing (NLP) tasks. Specifically, the Convolutional Neural Network (CNN), initially used for computer vision, has shown remarkable performance for text data in various NLP problems. Most of the existing CNN-based models use 1-dimensional convolving filters (n-gram detectors), where each filter specialises in extracting ngrams features of a particular input word embedding. The input word embeddings, also called sentence matrix, is treated as a matrix where each row is a word vector. Thus, it allows the model to apply one-dimensional convolution and only extract n-gram based features from a sentence matrix. These features can be termed as intra-sentence n-gram features. To the extent of our knowledge, all the existing CNN models are based on the aforementioned concept. In this paper, we present a CNNbased architecture TextConvoNet that not only extracts the intrasentence n-gram features but also captures the inter-sentence ngram features in input text data. It uses an alternative approach for input matrix representation and applies a two-dimensional multi-scale convolutional operation on the input. To evaluate the performance of TextConvoNet, we perform an experimental study on five text classification datasets. The results are evaluated by using various performance metrics. The experimental results show that the presented TextConvoNet outperforms state-ofthe-art machine learning and deep learning models for text classification purposes.

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TextConvoNet: a convolutional neural network based architecture for text classification

2022

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This paper presents, TextConvoNet , a novel Convolutional Neural Network (CNN) based architecture for binary and multi-class text classification problems. Most of the existing CNN-based models use one-dimensional convolving filters, where each filter specializes in extracting n-grams features of a particular input word embeddings (Sentence Matrix). These features can be termed as intra-sentence n-gram features. To the best of our knowledge, all the existing CNN models for text classification are based on the aforementioned concept. The presented TextConvoNet not only extracts the intra-sentence n-gram features but also captures the inter-sentence n-gram features in input text data. It uses an alternative approach for input matrix representation and applies a two-dimensional multi-scale convolutional operation on the input. We perform an experimental study on five binary and multi-class classification datasets and evaluate the performance of the TextConvoNet for text classification. The results are evaluated using eight performance measures, accuracy, precision, recall, f1-score, specificity, gmean1, gmean2, and Mathews correlation coefficient (MCC). Furthermore, we extensively compared presented TextConvoNet with machine learning, deep learning, and attention-based models. The experimental results evidenced that the presented TextConvoNet outperformed and yielded better performance than the other used models for text classification purposes.

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OWI: Open-World Intent Identification Framework for Dialog Based System

Cited by 5 publications

References 28 publications

Primitive-based Learning Model for Intent Detection in Human-AI Agent Negotiation

Primitive-based Learning Model for Intent Detection in Human-AI Agent Negotiation

TextConvoNet:A Convolutional Neural Network based Architecture for Text Classification

TextConvoNet: a convolutional neural network based architecture for text classification

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