Intent Classification in Question-Answering Using LSTM Architectures

Gennaro, Giovanni Di; Buonanno, Amedeo; Girolamo, Antonio Di; Ospedale, Armando; Palmieri, F.

doi:10.1007/978-981-15-5093-5_11

Cited by 17 publications

(10 citation statements)

References 7 publications

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“…In this paper, we focus on various issues related to the results obtained from training W2V. In our experience, also in obtaining W2V for the Italian language [12] and in its usage [11], we found that some important choices have become so common that they are used almost mechanically, without questioning about their effectiveness. More specifically, what is the correct number of epochs that need to be used before we can declare an embedding satisfactory?…”

Section: The Importance Of Learning Timementioning

confidence: 99%

“…In Natural Language Processing (NLP) problems approached with neural networks, individual words, that typically belong to large vocabularies, must be transformed into compressed representations. Although the state-of-the-art of NLP is today almost totally based on the use of Transformers [10,30,34], the difficulty of training such structures (both related to computational costs and the need for huge datasets) often leads to a preference for different approaches [5,11,17,18,26] where each word needs to be individually coded.…”

Section: Introductionmentioning

confidence: 99%

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Considerations about learning Word2Vec

2021

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Despite the large diffusion and use of embedding generated through Word2Vec, there are still many open questions about the reasons for its results and about its real capabilities. In particular, to our knowledge, no author seems to have analysed in detail how learning may be affected by the various choices of hyperparameters. In this work, we try to shed some light on various issues focusing on a typical dataset. It is shown that the learning rate prevents the exact mapping of the co-occurrence matrix, that Word2Vec is unable to learn syntactic relationships, and that it does not suffer from the problem of overfitting. Furthermore, through the creation of an ad-hoc network, it is also shown how it is possible to improve Word2Vec directly on the analogies, obtaining very high accuracy without damaging the pre-existing embedding. This analogy-enhanced Word2Vec may be convenient in various NLP scenarios, but it is used here as an optimal starting point to evaluate the limits of Word2Vec.

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Section: The Importance Of Learning Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Considerations about learning Word2Vec

2021

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“…In recent years, search engines (Google), chatbots (SIRI, ALEXA, and CORTANA) are becoming better at going beyond by answering the exact answer to our question. The Question Answering System has also seen significant changes in the architecture from basic Recurrent Neural Network (RNN) to transformers [8,12] over the years.…”

Section: Introductionmentioning

confidence: 99%

Automatic question-answer pairs generation and question similarity mechanism in question answering system

2021

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With the swift growth of the information over the past few years, taking full benefit is increasingly essential. Question Answering System is one of the promising methods to access this much information. The Question Answering System lacks humans’ common sense and reasoning power and cannot identify unanswerable questions and irrelevant questions. These questions are answered by making unreliable and incorrect guesses. In this paper, we address this limitation by proposing a Question Similarity mechanism. Before a question is posed to a Question-Answering system, it is compared with possible generated questions of the given paragraph, and then a Question Similarity Score is generated. The Question Similarity mechanism effectively identifies the unanswerable and irrelevant questions. The proposed Question Similarity mechanism incorporates a human way of reasoning to identify unanswerable and irrelevant questions. This mechanism can avoid the unanswerable and irrelevant questions altogether from being posed to the Question Answering system. It helps the Question Answering Systems to focus only on the answerable questions to improve their performance. Along with this, we introduce an application of the Question Answering System that generates the question-answer pairs given a passage and is useful in several fields.

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“…Lastly, the output gate will decide which of the information is relevant enough to be a part of the output. The superior capabilities of LSTM have been recognized and implemented in several applications, such as machine translation [ 39 , 40 , 41 ], natural language processing [ 42 , 43 , 44 ], and time series analysis [ 45 , 46 , 47 , 48 ]. In this manuscript, the capabilities of both CNN and LSTM are motivating factors in our choice to employ both architectures for fall detection.…”

Section: Introductionmentioning

confidence: 99%

Optimal Training Configurations of a CNN-LSTM-Based Tracker for a Fall Frame Detection System

Mohamed

Zulkifley

Ibrahim

et al. 2021

Sensors

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In recent years, there has been an immense amount of research into fall event detection. Generally, a fall event is defined as a situation in which a person unintentionally drops down onto a lower surface. It is crucial to detect the occurrence of fall events as early as possible so that any severe fall consequences can be minimized. Nonetheless, a fall event is a sporadic incidence that occurs seldomly that is falsely detected due to a wide range of fall conditions and situations. Therefore, an automated fall frame detection system, which is referred to as the SmartConvFall is proposed to detect the exact fall frame in a video sequence. It is crucial to know the exact fall frame as it dictates the response time of the system to administer an early treatment to reduce the fall’s negative consequences and related injuries. Henceforth, searching for the optimal training configurations is imperative to ensure the main goal of the SmartConvFall is achieved. The proposed SmartConvFall consists of two parts, which are object tracking and instantaneous fall frame detection modules that rely on deep learning representations. The first stage will track the object of interest using a fully convolutional neural network (CNN) tracker. Various training configurations such as optimizer, learning rate, mini-batch size, number of training samples, and region of interest are individually evaluated to determine the best configuration to produce the best tracker model. Meanwhile, the second module goal is to determine the exact instantaneous fall frame by modeling the continuous object trajectories using the Long Short-Term Memory (LSTM) network. Similarly, the LSTM model will undergo various training configurations that cover different types of features selection and the number of stacked layers. The exact instantaneous fall frame is determined using an assumption that a large movement difference with respect to the ground level along the vertical axis can be observed if a fall incident happened. The proposed SmartConvFall is a novel technique as most of the existing methods still relying on detection rather than the tracking module. The SmartConvFall outperforms the state-of-the-art trackers, namely TCNN and MDNET-N trackers, with the highest expected average overlap, robustness, and reliability metrics of 0.1619, 0.6323, and 0.7958, respectively. The SmartConvFall also managed to produce the lowest number of tracking failures with only 43 occasions. Moreover, a three-stack LSTM delivers the lowest mean error with approximately one second delay time in locating the exact instantaneous fall frame. Therefore, the proposed SmartConvFall has demonstrated its potential and suitability to be implemented for a real-time application that could help to avoid any crucial fall consequences such as death and internal bleeding if the early treatment can be administered.

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Intent Classification in Question-Answering Using LSTM Architectures

Cited by 17 publications

References 7 publications

Considerations about learning Word2Vec

Considerations about learning Word2Vec

Automatic question-answer pairs generation and question similarity mechanism in question answering system

Optimal Training Configurations of a CNN-LSTM-Based Tracker for a Fall Frame Detection System

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