Deep Learning Framework for Natural Language Processing

Abstract

The present invention provides a deep learning framework for Natural Language Processing (NLP) that integrates a dynamic attention mechanism, multi-modal training capabilities, and advanced optimization techniques to enhance computational efficiency, scalability, and accuracy across a range of NLP tasks. By incorporating both textual and non-textual data, such as images and audio, the framework enables improved contextual understanding and more effective processing of complex, real-world data. The dynamic attention mechanism dynamically adjusts focus during training and inference to reduce computational load, while transfer learning techniques facilitate rapid adaptation to domain-specific tasks, reducing the need for extensive labeled datasets. The invention is particularly suitable for real-time NLP applications, such as sentiment analysis, machine translation, and question answering, and can be scaled for large enterprise-level systems.

Specification

Description:The present invention relates to deep learning systems, specifically tailored for Natural Language Processing (NLP) tasks. It addresses the optimization and enhancement of deep learning models to efficiently process and understand natural language, with applications spanning across tasks such as text classification, sentiment analysis, machine translation, question answering, and more. The invention focuses on improving the scalability, adaptability, and performance of NLP systems through innovative model architectures, training techniques, and optimization strategies.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

Natural Language Processing (NLP) has become a pivotal aspect of artificial intelligence, enabling machines to understand, interpret, and generate human language. Over the years, various deep learning models have been developed to tackle NLP tasks, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based architectures. These models have shown remarkable performance in tasks such as text generation, language translation, and named entity recognition. However, despite their successes, these existing models often face challenges related to scalability, efficiency, and generalization across various NLP tasks.

A major limitation of current deep learning models in NLP is their computational inefficiency during training and inference. Models such as RNNs and LSTMs suffer from long training times and difficulty handling long-range dependencies in textual data. Transformer-based models, while effective, can still be computationally expensive, especially when dealing with large datasets or real-time applications. Additionally, these models may not perform optimally when applied to domain-specific tasks or data that diverge from the general language corpus on which they were trained.

Another challenge in NLP is the need for multi-modal understanding, where models must process not only textual data but also incorporate other forms of data such as images, speech, and sensory inputs. This is especially critical in tasks that require more nuanced interpretations of language, such as sentiment analysis in social media or context-aware machine translation. Existing NLP frameworks tend to focus exclusively on textual data, limiting their ability to process information from multiple sources and reducing their overall effectiveness.

Furthermore, despite advancements in pre-training methods such as transfer learning, existing deep learning models often require vast amounts of task-specific data for fine-tuning. This makes the deployment of NLP models costly and time-consuming, especially for industries that lack large-scale labeled datasets for specific applications. A solution that can reduce the reliance on large labeled datasets and improve the adaptability of models across diverse tasks and domains is thus needed.

OBJECTIVE OF THE INVENTION

Some of the objects of the present disclosure, which at least one embodiment herein satisfies are listed herein below.

The primary objective of the present invention is to provide a novel deep learning framework that overcomes the limitations of existing NLP models, specifically in terms of computational efficiency, scalability, and adaptability to diverse applications.

An additional objective of the invention is to develop a dynamic attention mechanism within the deep learning framework that improves training and inference performance by focusing attention on the most relevant parts of the input data, thereby reducing the computational cost and increasing the speed of NLP tasks.

Another objective is to introduce a multi-modal training approach that allows the framework to incorporate not only textual data but also non-textual data such as images, audio, and video, enhancing the model's understanding and application to real-world NLP tasks that involve multiple modalities of information.

A further objective is to provide a solution that enables transfer learning and fine-tuning, where the model is pre-trained on a large, general-purpose dataset and can then be efficiently adapted to specific NLP tasks or domain-specific datasets, reducing the amount of task-specific data required for training.

Another objective is to incorporate advanced optimization techniques, such as gradient descent methods and batch normalization, to speed up model convergence, improve training stability, and minimize overfitting, thereby enhancing the generalization capability of the model.

Yet another objective is to design a deep learning framework that is not only efficient in training but also scalable, capable of processing large-scale datasets without sacrificing accuracy or computational efficiency, making it suitable for deployment in enterprise-level applications.

Finally, the invention aims to provide a solution that allows for real-time performance in NLP tasks, addressing the need for low-latency processing in applications such as conversational agents, sentiment analysis, and machine translation.

SUMMARY OF THE INVENTION
This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

The present invention provides an innovative deep learning framework for natural language processing (NLP) that integrates a dynamic attention mechanism, multi-modal training capabilities, and advanced optimization techniques. The framework is designed to address the computational inefficiencies and scalability challenges commonly encountered in traditional NLP models, while also enabling better generalization across different domains and tasks.

By incorporating dynamic attention, the framework dynamically adjusts its focus during both training and inference, improving model efficiency. The multi-modal training component allows for the integration of both textual and non-textual data, facilitating more comprehensive understanding of language. Additionally, the use of transfer learning and task-specific fine-tuning reduces the need for extensive labeled datasets, enabling rapid deployment across a range of NLP applications.

BRIEF DESCRIPTION OF DRAWINGS
The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that invention of such drawings includes the invention of electrical components, electronic components or circuitry commonly used to implement such components.

FIG. 1 illustrates an exemplary deep learning framework for natural language processing (NLP), in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The present invention describes a deep learning framework specifically designed for enhancing the performance and scalability of Natural Language Processing (NLP) tasks. The core of this invention lies in its novel combination of architectural improvements, multi-modal training, dynamic attention mechanisms, and transfer learning techniques, all of which work synergistically to optimize NLP model efficiency, accuracy, and adaptability.

At the heart of the deep learning framework is a multi-layer neural network architecture, which comprises an input layer, transformer-based layers, dynamic attention mechanisms, and output layers. The input layer is designed to receive tokenized text, which is processed by the subsequent layers. The transformer-based layers are enhanced by a dynamic attention mechanism that adjusts the weight of attention given to different parts of the input data based on contextual relevance. This allows the model to focus computational resources on the most significant portions of the input text, thereby reducing unnecessary computations and improving the speed of training and inference.

One of the innovative aspects of this framework is the dynamic attention mechanism, which adjusts its focus depending on the contextual relationships present in the input data. Unlike traditional attention mechanisms that use fixed weights throughout the model's operation, the dynamic mechanism enables adaptive learning by selecting which parts of the input should be prioritized. This results in improved performance, particularly for tasks that involve complex dependencies, such as machine translation, text generation, and question answering. By reducing the number of unnecessary computations, the dynamic attention mechanism enhances the overall efficiency of the NLP model.

In addition to processing textual data, the framework allows for the integration of non-textual data sources in a multi-modal training process. Non-textual data, such as images, audio, or even sensory input, can be fed into the model alongside textual input, providing richer context for NLP tasks. For instance, in sentiment analysis on social media, integrating images or audio could give the model a better understanding of user emotions based on both textual and non-textual signals. This multi-modal capability allows the model to handle more complex, real-world NLP tasks, thereby broadening its range of applications.

The invention incorporates transfer learning to facilitate pre-training of the model on large, general-purpose datasets, such as large-scale language corpora or publicly available text datasets. Once pre-trained, the model can be fine-tuned on domain-specific datasets for more targeted tasks, such as legal document classification or medical text analysis. This method reduces the need for extensive labeled datasets for every specific task, thus saving time and resources. The fine-tuning process allows the model to adapt quickly to new domains, ensuring that it performs optimally even in specialized fields.

The framework also includes a comprehensive optimization layer, which leverages advanced techniques such as gradient descent and batch normalization. These techniques enable faster convergence, stabilizing the training process and allowing the model to reach an optimal solution in a shorter time frame. The optimization layer is critical for ensuring that the deep learning model performs effectively even when processing large datasets or when deployed in real-time applications that demand low-latency processing.

To address the issue of scalability, the deep learning framework is designed to handle large datasets efficiently. The incorporation of the dynamic attention mechanism and multi-modal capabilities ensures that the model can adapt to new data sources and contexts without requiring major architectural changes. This adaptability allows the framework to scale to meet the demands of enterprise-level applications and large-scale real-time systems.

The invention is also designed to perform efficiently in real-time environments. With the focus on computational efficiency, particularly through the dynamic attention mechanism and model optimization strategies, the framework can process large amounts of data quickly, making it suitable for real-time applications like chatbots, voice assistants, and live machine translation. This ensures that NLP tasks can be executed promptly, making the framework suitable for high-performance environments.

In one embodiment, the deep learning framework is applied to the task of text classification in social media. The framework is trained on a general-purpose dataset to learn basic language structures and nuances. It is then fine-tuned on a domain-specific dataset of social media posts to classify sentiments and identify trends in user behavior. The dynamic attention mechanism enables the model to prioritize key phrases, emojis, or hashtags, which might carry more weight in understanding sentiment. Additionally, the model integrates non-textual data, such as images or videos accompanying posts, to gain further insights into the context and tone of the content. This embodiment demonstrates how multi-modal capabilities and dynamic attention improve the performance of sentiment analysis tasks.

In another embodiment, the deep learning framework is used for machine translation in the legal domain. Legal texts, such as contracts or statutes, require a high level of accuracy due to their specialized language and structure. The framework is pre-trained on a large multilingual corpus and then fine-tuned on a dataset of legal documents in multiple languages. The dynamic attention mechanism helps the model focus on important terms or phrases that may have complex legal meanings, improving translation quality. The multi-modal training component could be used to incorporate contextual knowledge, such as images of legal documents or audio recordings of legal proceedings, to further enhance the understanding of the content. This embodiment illustrates how the deep learning framework adapts to specialized fields like law while maintaining high accuracy and efficiency.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation.
, Claims:1. A deep learning framework for natural language processing (NLP), comprising:
• an input layer configured to accept tokenized text;
• a plurality of transformer-based layers incorporating an adaptive attention mechanism;
• an output layer configured to provide results for specific NLP tasks;
• wherein the adaptive attention mechanism dynamically adjusts attention based on the context of the input data.

2. The deep learning framework of claim 1, wherein the adaptive attention mechanism reduces computational load by selectively focusing on key parts of the input data during training and inference.
3. The deep learning framework of claim 1, further comprising a multi-modal training component that integrates both textual and non-textual data sources for enhancing NLP task performance.
4. The deep learning framework of claim 3, wherein the non-textual data includes at least one of images or speech data, allowing the model to perform cross-modal understanding for NLP tasks.
5. The deep learning framework of claim 1, wherein the framework utilizes transfer learning to pre-train the model on a general-purpose dataset and fine-tune the model on a specific NLP task or domain-specific corpus.
6. The deep learning framework of claim 1, wherein the framework incorporates an optimization layer utilizing gradient descent methods and batch normalization to improve training efficiency and reduce overfitting.

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