Intent Classification Research Articles

Margin and Shared Proxies: Advanced Proxy Anchor Loss for Out-of-Domain Intent Classification

Out-of-Domain (OOD) intent classification is an important task for a dialog system, as it allows for appropriate responses to be generated. Previous studies aiming to solve the OOD intent classification task have generally adopted metric learning methods to generate decision boundaries in the embedding space. However, these existing methods struggle to capture the high-dimensional semantic features of data, as they learn decision boundary using scalar distances. They also use generated OOD samples for learning. However, such OOD samples are biased, and they cannot include all real-world OOD intents, thus representing a limitation. In the current paper, we attempt to overcome these challenges by using Advanced Proxy-Anchor loss, which introduces a margin proxy and shared proxy. First, to generate a decision boundary that has the high-dimensional semantic features of training data, we use a margin proxy for learnable embedding vectors. Next, the shared proxy, which is shared by all In-Domain (IND) samples, is introduced to make it possible to learn the discriminative feature between IND intents and OOD intent, ultimately leading to the improved classification of OOD samples. We conduct evaluations of the proposed method using three benchmark datasets. The experimental results demonstrate that our method achieved an improved performance compared to the methods described in previous studies.

An NLP-based system for modulating virtual experiences using speech instructions

The application of virtual reality (VR) technology in rehabilitation is a research area that has been attracting remarkable attention recently, especially as an intervention with learning and developmental disorders such as dyslexia and autism. Multi-modal VR-based rehabilitative systems can provide a rich experience to patients by responding to various types of bio-signals. This paper proposes a framework for incorporating voice input in rehabilitative games. The proposed framework equips collaborative learning environments with voice based interaction which allows for a more dynamic flow of therapy sessions between physically impaired patients and specialists in a rehabilitative VR environment. The proposed framework’s pipeline begins with a speech recognition module to parse utterances into sentences. Next, comes the feature extraction module to identify the intent of the user and entities as well. Finally, we modulate the 3D environment in Unity. The framework was designed in a way that guarantees smooth integration with any game or 3D environment. Several technologies were tested in terms of accuracy and speed to increase the overall performance of the pipeline. To the best of our knowledge, this research is the first to do the following: 1) build a dataset of instructions, inspired by a product already used in the healthcare market, 2) based on which we could realize a multi-modal system, with voice as one of the modalities, for the context of VR-based rehabilitation. We evaluate every stage in the system pipeline, and we show that we can achieve considerable results on each of these stages including the speech recognition module, with a 5.64% word error rate, and on intent classification where the BiLSTM model outperforms BERT in the Named Entity Recognition by 11% in terms of the F1-score. We envisage the proposed system to lend itself well to emerging healthcare frameworks in the Metaverse.

Few-shot out-of-scope intent classification: analyzing the robustness of prompt-based learning

Few-shot out-of-scope intent classification: analyzing the robustness of prompt-based learning

A Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

A Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

UNI – A Retrieval Augmented Generation powered Virtual Assistant for college related queries

This paper unveils an advanced chatbot engineered to cater specifically to college-related inquiries. Harnessing the power of BARD and incorporating a wake word activation system with automatic speech recognition, the chatbot offers an enhanced user experience marked by both linguistic sophistication and spoken command initiation. The methodology encompasses the nuanced process of pre-training on diverse corpora, fine-tuning to optimize responsiveness to college-specific queries, and the seamless integration of intent classification and entity recognition. These facets collectively empower the chatbot to understand and respond effectively to the intricacies of user inputs. A comprehensive knowledge base is curated to ensure not only accurate information retrieval but also to foster a depth of contextual understanding. This project signifies a pioneering leap in providing an innovative, user-friendly, and ethically driven solution for addressing college-related queries through natural language interactions. By showcasing practical advancements in chatbot technology tailored to the educational landscape, this research contributes to the evolving landscape of intelligent virtual assistants.

An intent recognition pipeline for conversational AI

Natural Language Processing (NLP) is one of the Artificial Intelligence applications that is entitled to allow computers to process and understand human language. These models are utilized to analyze large volumes of text and also support aspects like text summarization, language translation, context modeling, and sentiment analysis. Natural language, a subset of Natural Language Understanding (NLU), turns natural language into structured data. NLU accomplishes intent classification and entity extraction. The paper focuses on a pipeline to maximize the coverage of a conversational AI (chatbot) by extracting maximum meaningful intents from a data corpus. A conversational AI can best answer queries with respect to the dataset if it is trained on the maximum number of intents that can be gathered from the dataset which is what we focus on getting in this paper. The higher the intent we gather from the dataset, the more of the dataset we cover in training the conversational AI. The pipeline is modularized into three broad categories - Gathering the intents from the corpus, finding misspellings and synonyms of the intents, and finally deciding the order of intents to be picked up for training any classifier ML model. Several heuristic and machine-learning approaches have been considered for optimum results. For finding misspellings and synonyms, they are extracted through text vector neural network-based algorithms. Then the system concludes with a suggestive priority list of intents that should be fed to a classification model. In the end, an example of three intents from the corpus is picked, and their order is suggested for the optimum functioning of the pipeline. This paper attempts to pick intents in descending order of their coverage in the corpus in the most optimal way possible.

A Deep Multi-Tasking Approach Leveraging on Cited-Citing Paper Relationship For Citation Intent Classification

Citations are crucial artifacts to provide additional information to the reader to comprehend the research under concern. There are different roles that citations play in scientific discourse. Correctly identifying the intent of the citations finds applications ranging from predicting scholarly impact, finding idea propagation, to text summarization. With the rapid growth in scientific literature, the need for automated methods to classify citations is now growing intense. However, we can only fully understand the intent of a citation if we look at the citation context in the citing paper and also the primary purpose of the cited article. In this work, we propose a neural multi-task learning framework that harnesses the structural information of the research papers and the cited paper’s information for the effective classification of citation intents. We analyze the impact of three auxiliary tasks on the performance of our approach for citation classification. Our experiments on three benchmark citation classification datasets show that incorporating cited paper information (title) shows that our deep neural model achieves a new state-of-the-art on the ACL-ARC dataset with an absolute increase of 5.3% in the F1 score over the previous best model. We also achieve comparable performance with respect to the best-performing systems in the SDP 2021 3C Shared task on Citation Context Classification. We make our codes available at https://github.com/Tirthankar-Ghosal/citationclassification-SCIM

Hierarchical and Bidirectional Joint Multi-Task Classifiers for Natural Language Understanding

The MASSIVE dataset is a spoken-language comprehension resource package for slot filling, intent classification, and virtual assistant evaluation tasks. It contains multi-language utterances from human beings communicating with a virtual assistant. In this paper, we exploited the relationship between intent classification and slot filling to improve the exact match accuracy by proposing five models with hierarchical and bidirectional architectures. There are two variants for hierarchical architectures and three variants for bidirectional architectures. These are the hierarchical concatenation model, the hierarchical attention-based model, the bidirectional max-pooling model, the bidirectional LSTM model, and the bidirectional attention-based model. The results of our models showed a significant improvement in the averaged exact match accuracy. The hierarchical attention-based model improved the accuracy by 1.01 points for the full training dataset. As for the zero-shot setup, we observed that the exact match accuracy increased from 53.43 to 53.91. In this study, we observed that, for multi-task problems, utilizing the relevance between different tasks can help in improving the model’s overall performance.

Automated government form filling for aged and monolingual people using interactive tool

Purpose The Government of India offers various schemes for various classes of citizens. Most of the application forms of schemes to be filled are in English and it is observed that monolingual individuals find it difficult to access and fill the forms. This paper addresses the challenges faced by monolingual individuals in India, particularly the elderly, people with impairments, and those from marginalized communities. The proposed work is to create an interactive system called "Dhvani" voicebot, specifically designed for the Kannada language. It helps users in identifying suitable government schemes and fills forms in English. Materials and Methods The proposed system is developed using the RASA chatbot framework and NLP techniques to comprehend user utterances. RNN and SVM algorithms are employed to ensure smooth conversation flow and interaction with the users. To enhance scheme suggestion accuracy, a knowledge graph is created, containing relevant data on government schemes. Results The intent classification model achieves an accuracy of 97%, indicating its ability to accurately understand user intentions. The integration of a knowledge graph improves the accuracy of scheme identification and suggestion to users. The system automates the process of filling out government scheme forms based on user inputs. Conclusion Dhvani voicebot system presents a practical solution to address the challenges faced by monolingual individuals in accessing government schemes in India. The high accuracy of intent classification and the use of a knowledge graph contribute to the system’s effectiveness. The study suggests that this system can be extended to other languages.

Multi-task learning model for citation intent classification in scientific publications

Multi-task learning model for citation intent classification in scientific publications

A System for Interviewing and Collecting Statements Based on Intent Classification and Named Entity Recognition Using Augmentation

In cases of child sexual abuse, interviewing and obtaining trustworthy statements from victims and witnesses is essential because their statements are the only evidence. It is crucial to ascertain objectively the credibility of the victim’s statements, which may vary based on the nature of the questions posed by the forensic interviewer. Therefore, interview skills that eliminate subjective opinions require a high level of training for forensic interviewers. To reduce high-risk subjective interviews, objectively analyzing statements is essential. Understanding the victim’s intent and named entity recognition (NER) in the statements is necessary to give the victim open-ended questions and memory recall. Therefore, the system provides an intent classification and NER method that follows the National Institute of Child Health and Human Development Investigative Interview Protocol, which outlines the collection of objective statements. Large language models such as BERT and KoBERT, along with data augmentation techniques, were proposed using a restricted training dataset of limited size to achieve effective intent classification and NER performance. Additionally, a system that can collect objective statements with the proposed model was developed and it was confirmed that it could assist statement analysts. The verification results showed that the model achieved average F1-scores of 95.5% and 97.8% for intent classification and NER, respectively, which improved the results of the limited data by 3.4% and 3.7%, respectively.

Survey: Unconventional Categories of Chatbots that make use of Machine Learning Techniques

A growing trend in computer interaction approaches is human-computer interaction, which describes communication between a person and a computer and is known as a chatbot. A conversational agent, often known as a chatbot, is a computer program that attempts to provide responses that appear human during a conversation. Studies on several chatbot applications, are been carried out in this research on the areas of Natural Language Processing (NLP), the Natural Language understanding (NLU), and Intent recognition in recent years. There are several NLU applications in various fields, including Chatbots, search in natural language, voice-driven assistants, web- scale information extraction, legal discovery, and content summarization. Intent classification is one of the biggest challenges in NLP. Some of the chatbots classifies students requests to make them simpler to comprehend. A chatbot is an appliance of software that replicates and processes human communication to offer real-time digital assistance. Various tasks that were previously handled by human agents, like assistance to customers, medical counselling, digital commerce and issues addressing, are now being assigned to chatbots. Due to the appearance of machine learning techniques, the main step in chatbot development was defining the rules that would be used to create responses. Here are some strategies for chatbot applications and the machine-learning techniques that underpin them.

Joint Modelling of Slot Filling and Intent Detection in Constrained Resource Scenarios

In the era of pervasive smart devices, natural language understanding (NLU) holds a pivotal role for facilitating intelligent interactions and decision-making. Core to NLU are slot filling and intent recognition, essential tasks for comprehending user input. While joint modelling of these tasks has gained prominence, the challenges of realizing efficient joint models on resource-constrained devices have emerged as significant. These devices possess limited computational capacity and real-time requirements, necessitating lightweight and efficient models. In this study, we explore the design of a resource-efficient joint model for slot filling and intent recognition. Through leveraging BERT, , graph neural networks, and mask mechanisms, our model achieves the dual goals of semantic slot prediction and intent classification. We focus on model design, training, and real-time inference, aiming to contribute to the paradigm of resource-constrained natural language understanding. Our investigation demonstrates the efficacy of our approach, even when working with a reduced dataset, underscoring the model's applicability to real-world scenarios with limited resources.

Civil Aviation Travel Question and Answer Method Using Knowledge Graphs and Deep Learning

In this paper, a civil aviation travel question and answer (Q&A) method based on integrating knowledge graphs and deep learning technology is proposed to establish a highly efficient travel information Q&A platform and quickly and accurately obtain question information and give corresponding answers to passengers. In the proposed method, a rule-based approach is employed to extract triads from the acquired civil aviation travel dataset to construct a civil aviation travel knowledge graph. Then, the ELECTRA-BiLSTM-CRF model is constructed to recognize the entity, and an improved ALBERT-TextCNN model is used for intent classification. Finally, Cypher query templates are transformed into Cypher query statements and retrieved in the Neo4j database, and the query returns the result, which realizes a new civil aviation travel Q&A method. A self-built civil aviation dataset is selected to prove the effectiveness of the proposed method. The experimental results show that the proposed method based on integrating knowledge graphs and deep learning technology can achieve better Q&A results, and it has better generalization and high accuracy.

Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary

Open intent classification, which aims to correctly classify the known intents into their corresponding classes while identifying the new unknown (open) intents, is an essential but challenging task in dialogue systems. In this paper, we introduce novel K-center contrastive learning and adjustable decision boundary learning (CLAB) to improve the effectiveness of open intent classification. First, we pre-train a feature encoder on the labeled training instances, which transfers knowledge from known intents to unknown intents. Specifically, we devise a K-center contrastive learning algorithm to learn discriminative and balanced intent features, improving the generalization of the model for recognizing open intents. Second, we devise an adjustable decision boundary learning method with expanding and shrinking (ADBES) to determine the suitable decision conditions. Concretely, we learn a decision boundary for each known intent class, which consists of a decision center and the radius of the decision boundary. We then expand the radius of the decision boundary to accommodate more in-class instances if the out-of-class instances are far from the decision boundary; otherwise, we shrink the radius of the decision boundary. Extensive experiments on three benchmark datasets clearly demonstrate the effectiveness of our method for open intent classification.For reproducibility, we submit the code at: https://github.com/lxk00/CLAP

Improving Dialogue Intent Classification with a Knowledge-Enhanced Multifactor Graph Model (Student Abstract)

Although current Graph Neural Network (GNN) based models achieved good performances in Dialogue Intent Classification (DIC), they leaf the inherent domain-specific knowledge out of consideration, leading to the lack of ability of acquiring fine-grained semantic information. In this paper, we propose a Knowledge-Enhanced Multifactor Graph (KEMG) Model for DIC. We firstly present a knowledge-aware utterance encoder with the help of a domain-specific knowledge graph, fusing token-level and entity-level semantic information, then design a heterogeneous dialogue graph encoder by explicitly modeling several factors that matter to contextual modeling of dialogues. Experiment results show that our proposed method outperforms other GNN-based methods on a dataset collected from a real-world online customer service dialogue system on the e-commerce website, JD.

Efficient intent classification and entity recognition for university administrative services employing deep learning models

The design and implementation of a domain specific conversational agent requires efficient Natural Language Understanding (NLU). The task is harder when multiple languages have to be supported, and training datasets can be beneficial. This work focuses on the development of an intelligent system, an automated multilingual customer service conversational agent (chatbot) for university students, which supports both Greek and English and combines Intent Classification or Intent Extraction (IE) and Named Entity Recognition (NER) to understand the content (i.e. type of actions conveyed and respective entities) of users' messages. We focus on the development of the fundamental tasks required by a conversational agent to provide customer services in the education industry and manage requests with instant responses and increased customer satisfaction. Instead of handling IE and NER separately, as it is common in the related work, we develop a joint model that combines Bidirectional Long Short-Term Memory (BiLSTM) and Conditional Random Fields (CRF) layers and generates outputs both for IE and NER. We introduce a novel, open access dataset for customer services in education industry, the UniWay dataset, that has been used for training and evaluating our model, comprises students' questions in English and Greek about essential information related to their studies. A comparative evaluation of the proposed model versus state-of-the-art standalone and joint model solutions in UniWay and xSID datasets, results in improvement of the performance for the IE task up to 1.4% and it is on par with the state-of-the-art for the NER task. These results justify the intuition that closed domains can benefit from less sophisticated architectures, but less costly in terms of computational and memory resources, that jointly resolve multiple NLU tasks.

Fine Grained Analysis of Intention for Social Media Reviews Using Distance Measure and Deep Learning Technique

Intent analysis and classification are performed to identify the expressions of intent in the given text. In this paper, the dataset is classified into emotion classifications by utilizing machine learning model SVM, Bipolar classification, Fine Grained Analysis, and Sarcasm detection, with Naïve Bayes and Random Forest techniques of deep learning, including Long Short-Term Memory to perform intention analysis on social media data. Then Fine-grained or Multi-Class Sentiment analysis is used for further classification of the five classes, viz. negative, strong negative, neutral, positive, and strong positive, which detects the sarcastic reviews in the movie dataset. The emotional intention behind the review comments is classified as happiness, rage, sadness, joy, anger, and disgust by using SVM. The reviews are analyzed and calculated based on their subjectivity and context level similarity using Related Relaxed Word Mover Distance (RRWMD) semantic similarity measure. With the advantage of the RRWMD algorithm, the reviews from the context containing deviated or irrelevant contents were removed before being applied to the classification algorithms, thereby reducing the execution time, which obtains a 3% improvement in accuracy. The disadvantage of the RRWMD algorithm is only one deep learning algorithm is compared. From the observed accuracy scores and classification reports, the LSTM has provided higher accuracy, despite the long execution time. The Naïve Bayes model has produced lower accuracy than the neural network model but was efficient, taking less time to fit and classify. The results from various experiments have proven that the semantic similarity measure provides more accurate results than the state-of-the-art model.

An intent classification method for questions in "Treatise on Febrile diseases" based on TinyBERT-CNN fusion model

"Treatise on Febrile Diseases" is an important classic book in the academic history of Chinese material medica. Based on the knowledge map of traditional Chinese medicine established by the study of "Treatise on Febrile Diseases", a question-answering system of traditional Chinese medicine was established to help people better understand and use traditional Chinese medicine. Intention classification is the basis of the question-answering system of traditional Chinese medicine, but as far as we know, there is no research on question intention classification based on "Treatise on Febrile Diseases". In this paper, the intent classification research is carried out based on the Chinese material medica-related content materials in "Treatise on Febrile Diseases" as data. Most of the existing models perform well on long text classification tasks, with high costs and a lot of memory requirements. However, the intent classification data of this paper has the characteristics of short text, a small amount of data, and unbalanced categories. In response to these problems, this paper proposes a knowledge distillation-based bidirectional Transformer encoder combined with a convolutional neural network model (TinyBERT-CNN), which is used for the task of question intent classification in "Treatise on Febrile Diseases". The model used TinyBERT as an embedding and encoding layer to obtain the global vector information of the text and then completed the intent classification by feeding the encoded feature information into the CNN. The experimental results indicated that the model outperformed other models in terms of accuracy, recall, and F1 values of 96.4%, 95.9%, and 96.2%, respectively. The experimental results prove that the model proposed in this paper can effectively classify the intent of the question sentences in "Treatise on Febrile Diseases", and provide technical support for the question-answering system of "Treatise on Febrile Diseases" later.

Intent Classification by the Use of Automatically Generated Knowledge Graphs

Intent classification is an essential task for goal-oriented dialogue systems for automatically identifying customers’ goals. Although intent classification performs well in general settings, domain-specific user goals can still present a challenge for this task. To address this challenge, we automatically generate knowledge graphs for targeted data sets to capture domain-specific knowledge and leverage embeddings trained on these knowledge graphs for the intent classification task. As existing knowledge graphs might not be suitable for a targeted domain of interest, our automatic generation of knowledge graphs can extract the semantic information of any domain, which can be incorporated within the classification process. We compare our results with state-of-the-art pre-trained sentence embeddings and our evaluation of three data sets shows improvement in the intent classification task in terms of precision.