Large model connection context dialogue method based on LSTM (Long Short-Term Memory) model

Abstract

The invention provides a large model connection context dialogue method based on an LSTM (Long Short-Term Memory) model, which belongs to the field of intelligent deep learning and comprises the steps of data preprocessing, input sequence coding, hidden state and memory cell initialization, decoding response generation, output sequence decoding, training and optimization, evaluation and adjustment. The method has high accuracy and response speed when processing a complex dialogue scene, and can be widely applied to the fields of intelligent customer service, intelligent assistants and the like.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence deep learning, and in particular to a large-model contact context dialogue method based on an LSTM long short-term memory model. Background Art

[0002] With the rapid development of science and technology, artificial intelligence technology has penetrated into every aspect of our lives. As one of the important applications, dialogue systems have achieved remarkable results in the fields of intelligent customer service, intelligent assistants, etc. However, traditional dialogue systems mainly rely on rule matching and template matching technologies, which makes them incapable of coping with complex and changing dialogue scenarios.

[0003] To meet this challenge, researchers began to explore dialogue systems based on machine learning, especially dialogue systems based on deep learning. Such systems learn from a large amount of data and automatically extract features from it, thereby responding to complex dialogue scenarios. Among the many deep learning models, models such as recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and gated recurrent units (GRUs) are widely used in dialogue systems. The LSTM model has received widespread attention because of its better performance in processing long text sequences and its ability to effectively learn long-term dependencies. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a large-model contact context dialogue method based on the LSTM long short-term memory model, aiming to further improve the accuracy and response speed of the dialogue system.

[0005] The technical solution of the present invention is:

[0006] A large-model contextual dialogue method based on the LSTM long short-term memory model, including

[0007] 1) Data preprocessing: The conversation data needs to be preprocessed, including word segmentation, removal of stop words and punctuation marks, etc., so that the model can better understand the text content.

[0008] 2) Input sequence encoding: Convert the preprocessed user input sequence (such as a sentence) into a numerical form that the model can process, usually by mapping the vocabulary into vectors through an embedding layer. These vectors are then encoded through an LSTM model to capture the contextual information in the input sequence.

[0009] 3) Initialize hidden states and memory cells: In each iteration of the dialogue system, the hidden states and memory cells of the LSTM model need to be initialized. These states will be transferred from the state of the previous dialogue, or start from a new state.

[0010] 4) Decode to generate response: Use the LSTM model to decode the input sequence and generate the response sequence. This usually involves a decoding layer, which uses the hidden state of the previous time step to predict the next word. During the decoding process, different strategies can be used, such as greedy decoding, beam search, or sampling decoding.

[0011] 5) Output sequence decoding: Convert the sequence of predicted words produced by the decoding layer into readable natural language text. This usually involves converting the predicted word index back to the original word.

[0012] 6) Training and optimization: The training process of the dialogue system involves forward propagation and back propagation. During the training process, the model minimizes the difference between the predicted response and the true response by continuously adjusting the weights. This usually requires the use of optimization algorithms (such as Adam, RMSprop, etc.) to update the model parameters.

[0013] 7) Evaluation and Adjustment: Evaluate the effectiveness of the model by evaluating the performance of the dialogue system, such as BLEU score, perplexity or other dialogue quality indicators. Based on the evaluation results, further adjustments and optimizations may be required to the model.

[0014] Furthermore,

[0015] Data preprocessing, including:

[0016] Word segmentation: Split the original text into smaller units, with the goal of converting the text into discrete units that the model can process;

[0017] Word embedding: Map the segmented words or subwords into continuous vectors;

[0018] Remove stop words;

[0019] Punctuation processing: retain their original role in the text, or convert them into labels or features that the model can recognize;

[0020] Normalization: unify different forms in the text;

[0021] Entity recognition: Identify these entities and give them special labels so that the information can be better processed in subsequent processing;

[0022] Language conversion: Convert the input text into a language that the model can handle.

[0023] Furthermore,

[0024] Input sequence code, including:

[0025] a) Embedding layer: In the neural network model, the embedding layer is responsible for converting discrete words into continuous vectors; each word corresponds to a unique vector, which captures the semantic information of the word in a high-dimensional space;

[0026] b) Vector representation: Through the embedding layer, each word is converted into a fixed-size vector, which is then passed as input to the LSTM model;

[0027] c) LSTM model encoding: In the LSTM model, each vector of the input sequence is processed sequentially and encoded by the LSTM unit;

[0028] d) Contextual information capture: The LSTM model captures long-term dependencies in the input sequence through its recurrent structure;

[0029] e) Hidden states and memory cells: In each iteration of the LSTM model, the hidden states and memory cells are updated with the contextual information of the input sequence; these states are subsequently used as the initial states in the decoding process, allowing the model to leverage previous conversation information when generating responses.

[0030] Furthermore,

[0031] Initialize hidden states and memory cells, including:

[0032] a) Hidden state transfer: In successive iterations of the conversation, the hidden state of the LSTM model is transferred from one iteration to the next; this state transfer allows the model to maintain the context of the conversation and remember previous content even in long conversations;

[0033] b) Forgetting and remembering: The forget gate and memory gate of the LSTM model control the forgetting and remembering of information. When initializing the hidden state and memory cells, the forget gate determines which information should be forgotten from the state of the previous moment, while the memory gate determines which information should be retained or enhanced.

[0034] c) Initialize a new state: At the beginning of a conversation or when the conversation switches to a new topic, start from a completely new state; hidden states and memory cells will be initialized to random values ​​or general values ​​determined by heuristic methods;

[0035] d) Integration of contextual information: During the conversation, the model needs to integrate contextual information from different sources and update its hidden states and memory cells to reflect these new contextual clues;

[0036] e) Training and optimization: During the training process, the model learns how to initialize hidden states and memory cells through continuous forward propagation and backpropagation;

[0037] f) Dynamic adjustment: In the actual application of the dialogue system, the initialization strategy of the hidden state and memory cells is dynamically adjusted according to the progress of the dialogue.

[0038] Furthermore,

[0039] Decoding generates a response involving a decoding layer, which uses the hidden state of the previous time step to predict the next word; different strategies can be used during the decoding process.

[0040] Furthermore,

[0041] Training and Optimization:

[0042] a) Dataset preparation: Before training, prepare a dataset containing annotated dialogues. This dataset should cover various dialogue scenarios and topics.

[0043] b) Forward propagation: In the forward propagation phase, the model receives the user's input sequence and converts it into vector form through the embedding layer; these vectors are then fed into the LSTM model, where the hidden state and memory cells are updated based on the input sequence; at each time step, the model generates a predicted response vector;

[0044] c) Loss function calculation: There is a difference between the predicted response of the model and the actual response, and this difference is quantified by the loss function;

[0045] d) Backpropagation: During the backpropagation phase, the model adjusts the weights based on the difference calculated by the loss function; this process involves the calculation of gradients, which are vectors whose direction points in the direction of the fastest increase in loss and whose magnitude indicates the rate at which the loss is increasing. The backpropagation algorithm calculates these gradients by comparing the predicted response with the true response.

[0046] e) Weight Update: Once the gradients are calculated, an optimization algorithm is used to update the weights of the model; the goal of the optimization algorithm is to reduce the value of the loss function, thereby improving the prediction accuracy of the model;

[0047] f) Learning rate adjustment: During the training process, the learning rate is adjusted according to the performance of the model;

[0048] g) Training monitoring: During the training process, the performance of the model needs to be monitored;

[0049] h) Early stopping method: Prevent the model from overfitting on the training data and stop training when the performance on the validation set no longer improves.

[0050] The beneficial effects of the present invention are

[0051] Improve conversation quality: Through training, the model can better understand and generate natural language, thereby generating more accurate and relevant responses and providing a higher quality conversation experience.

[0052] Enhanced user experience: The optimized model can better maintain contextual consistency in conversations, understand user intent, and provide more personalized interactions, thereby improving user satisfaction and loyalty.

[0053] Improve generalization ability: A fully trained and optimized model can perform better when encountering unseen data, which means that the model has better generalization ability and can adapt to more dialogue scenarios.

[0054] Reducing errors and contradictions: By continuously adjusting weights, the model is able to reduce errors and contradictory responses in conversations, making the conversation more coherent and credible.

[0055] Speed ​​up the conversation process: Optimizing algorithms helps the model learn faster and converge to optimal performance, which means that the conversation system can be deployed in a shorter time and provide services quickly.

[0056] Save development costs: Using effective optimization algorithms can reduce the number of manual parameter adjustments, save development and maintenance costs, and reduce resource waste.

[0057] Adapting to diverse user needs: Diverse conversation data can be added during the training process, so that the model can adapt to the needs of users from different regions and cultural backgrounds, increasing the inclusiveness and versatility of the conversation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 This is a diagram of the working principle of the LSTM model. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0060] The present invention provides a large model contact context dialogue method based on LSTM long short-term memory model, comprising:

[0061] Data preprocessing:

[0062] a) Tokenization: Split the original text into smaller units, usually words or sub - words. In Chinese, this may involve splitting the text into individual Chinese characters or words. The purpose of tokenization is to convert the text into discrete units that the model can process.

[0063] b) Word Embedding: Map the tokenized words or sub - words into continuous vectors. This can be achieved through pre - trained word embedding models (such as Word2Vec, GloVe, or FastText), or by learning new embedding vectors during training. Word embeddings help the model understand the semantic and contextual information of words.

[0064] c) Stopword Removal: Stopwords are words that frequently appear in conversations but do not contribute much to understanding the conversation content, such as "的" (de), "是" (shi), "在" (zai), etc. Removing these words can reduce the complexity of the model and focus on more meaningful vocabulary.

[0065] d) Punctuation Handling: In conversations, punctuation marks play important roles. For example, a question mark indicates a question, and a period indicates the end of a sentence. During the pre - processing stage, punctuation marks need to be handled, either by preserving their original roles in the text or by converting them into labels or features that the model can recognize.

[0066] e) Normalization: Unify different forms in the text. For example, convert plural forms to singular forms, or convert different tenses of verbs to their base forms. This helps the model focus on the semantic content of the vocabulary rather than formal differences.

[0067] f) Named Entity Recognition (NER): In conversations, specific entities such as person names, locations, organizations, etc. may be mentioned. The purpose of named entity recognition is to identify these entities and assign them special labels so that this information can be better processed in subsequent processing.

[0068] g) Language Conversion: If the dialogue system needs to support multiple languages, language conversion may be required to convert the input text into a language system that the model can process.

[0069] Input Sequence Encoding:

[0070] a) Embedding Layer: In the neural network model, the embedding layer is responsible for converting discrete words into continuous vectors. Each word corresponds to a unique vector, which can capture the semantic information of the word in a high-dimensional space. The weights of the embedding layer are learned through the training process, and they reflect the semantic and contextual relationships between words. For example, in the embedding layer, the vectors of "cat" and "dog" may have similar components because they are both related to animals.

[0071] b) Vector representation: Through the embedding layer, each word is converted into a fixed-size vector. These vectors contain not only the semantic information of the word, but also the statistical characteristics of the word in the training data. These vectors are then passed as input to the LSTM model.

[0072] c) LSTM model encoding: In the LSTM model, each vector of the input sequence is processed in turn and encoded through the LSTM unit. The LSTM unit has a forget gate, an input gate, and an output gate, which control the flow and forgetting of information. The forget gate determines which information should be discarded or retained from the cell state. The input gate is responsible for updating the cell state, first through the sigmoid function to decide which values ​​we are going to update, and then through the tanh function to create a new candidate value vector, which can be added to the state. After the forget gate forgets the information of the old state, we multiply this candidate value with the output of the sigmoid gate to decide which part of the state to actually update. The output gate determines which part of the cell state will be output to the next hidden state.

[0073] d) Contextual information capture: The LSTM model is able to capture long-term dependencies in the input sequence through its recurrent structure. In the context of a conversation, this means that the model can remember the content of previous conversations and use this information to generate more relevant responses. For example, if a user asks about the price of a product, the model needs to remember the name of the product that the user mentioned before, as well as any relevant details that may have come up in the conversation.

[0074] e) Hidden states and memory cells: In each iteration of the LSTM model, the hidden states and memory cells are updated to contain the contextual information of the input sequence. These states are then used as the initial state in the decoding process, allowing the model to leverage previous conversation information when generating responses.

[0075] Initialize hidden states and memory cells:

[0076] a) Hidden State Transfer: In successive iterations of the conversation, the hidden state of the LSTM model can be transferred from one iteration to the next. This state transfer allows the model to maintain contextual information of the conversation and remember previous content even in long conversations. For example, if the user mentioned early in the conversation that they wanted to book a restaurant, the model needs to remember this information so that it can return to the context of restaurant reservations after discussing other topics.

[0077] b) Forgetting and remembering: The forget gate and memory gate of the LSTM model control the forgetting and remembering of information. When initializing the hidden state and memory cells, the forget gate determines which information should be forgotten from the state of the previous moment, while the memory gate determines which information should be retained or enhanced. This mechanism enables LSTM to effectively forget irrelevant information while remembering information that is important to the current task.

[0078] c) Initialize a new state: At the beginning of a conversation or when the conversation switches to a new topic, it may be necessary to start from a completely new state. This means that the hidden state and memory cells will be initialized to random values ​​or general values ​​determined by some heuristic method. This initialization method helps the model establish an independent context on the new topic and avoid the influence of previous conversation information.

[0079] d) Integration of contextual information: During a conversation, the model may need to integrate contextual information from different sources. For example, in a multi-turn conversation, users may provide additional information or correct previous statements. In this case, the model needs to be able to update its hidden states and memory cells to reflect these new contextual clues.

[0080] e) Training and optimization: During the training process, the model learns how to initialize hidden states and memory cells through continuous forward propagation and backpropagation. Optimization algorithms (such as Adam or RMSprop) are used to update the model parameters to minimize the difference between the predicted response and the true response.

[0081] f) Dynamic Adjustment: In practical applications of dialogue systems, it may be necessary to dynamically adjust the initialization strategy of hidden states and memory cells according to the progress of the dialogue. For example, the system can automatically reset the state when the dialogue becomes stagnant or inconsistent to restore the coherence of the dialogue.

[0082] Decoding generates the response:

[0083] a) Use the LSTM model to decode the input sequence and generate the response sequence. This usually involves a decoding layer that uses the hidden state of the previous time step to predict the next word. During the decoding process, different strategies can be used, such as greedy decoding, beam search, or sampling decoding.

[0084] Output sequence decoding:

[0085] a) Convert the sequence of predicted words produced by the decoding layer into readable natural language text. This usually involves converting the predicted word indices back to the original words.

[0086] Training and Optimization:

[0087] a) Dataset preparation: Before training, you need to prepare a dataset containing a large number of annotated conversations. This dataset should cover a variety of conversation scenarios and topics so that the model can learn a wide range of conversation patterns and contextual information.

[0088] b) Forward propagation: In the forward propagation phase, the model receives the user's input sequence and converts it into vector form through the embedding layer. These vectors are then fed into the LSTM model, where the hidden state and memory cells are updated based on the input sequence. At each time step, the model generates a predicted response vector.

[0089] c) Loss function calculation: There is usually a difference between the predicted response of the model and the actual response, and this difference can be quantified by a loss function. Common loss functions include cross-entropy loss and mean squared error (MSE). Cross-entropy loss is suitable for classification problems, while mean squared error is suitable for regression problems.

[0090] d) Backpropagation: During the backpropagation phase, the model adjusts the weights based on the difference calculated by the loss function. This process involves the calculation of gradients, which are vectors whose direction points in the direction of the fastest increase in loss and whose magnitude indicates the rate at which the loss is increasing. The backpropagation algorithm calculates these gradients by comparing the predicted response with the true response.

[0091] e) Weight Update: Once the gradient is calculated, an optimization algorithm is used to update the weights of the model. The goal of the optimization algorithm is to reduce the value of the loss function, thereby improving the prediction accuracy of the model. Adam and RMSprop are two popular optimization algorithms that are able to adjust the learning rate based on the historical value of the gradient, thereby improving training efficiency and convergence speed.

[0092] f) Learning rate adjustment: During the training process, the learning rate is a key hyperparameter. It determines the magnitude of the weight update. If the learning rate is too large, the model may overfit or fail to converge; if the learning rate is too small, the training process may become very slow. Therefore, it is often necessary to adjust the learning rate according to the performance of the model.

[0093] g) Training Monitoring: During the training process, the performance of the model needs to be monitored to ensure that it not only performs well on the training data, but also generalizes well on unseen data. This usually involves evaluating the accuracy and relevance of the model on a validation set.

[0094] h) Early Stopping: To prevent the model from overfitting on the training data, early stopping can be used. Early stopping stops training when the performance on the validation set no longer improves. This helps find the optimal state of the model while avoiding overtraining.

[0095] Evaluate and adjust:

[0096] a) Evaluate the effectiveness of the model by evaluating the performance of the dialogue system, such as BLEU score, perplexity or other dialogue quality indicators. Depending on the evaluation results, further adjustments and optimizations may be required to the model.

[0097] This dialogue system based on the LSTM model can better understand the user's questions and needs through its powerful learning ability, thereby providing more accurate and appropriate answers. At the same time, since the LSTM model has good parallel computing capabilities, the response speed of the dialogue system has been significantly improved. This can not only improve the user experience, but also expand the application scope of artificial intelligence technology in various fields.

[0098] In general, the dialogue method based on the LSTM long short-term memory model proposed in this invention provides a new direction for the development of dialogue systems. In the future, as artificial intelligence technology becomes more mature, we have reason to believe that this dialogue system will play a greater role in various fields and bring more convenience to human life.

[0099] The above description is only a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, and is not used to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A large-model contact context dialogue method based on LSTM long short-term memory model, characterized in that: include: Data preprocessing: Preprocess the conversation data so that the model can better understand the text content; Input sequence encoding: The preprocessed user input sequence is converted into a numerical form that the model can handle, and the vocabulary is mapped into vectors through the Embedding Layer. These vectors are then encoded through the LSTM model to capture the contextual information in the input sequence. Initialize hidden states and memory cells: In each iteration of the dialogue system, initialize the hidden states and memory cells of the LSTM model; these states will be transferred from the state of the previous dialogue, or start from a new state; Decode and generate response: Use the LSTM model to decode the input sequence and generate a response sequence; Output sequence decoding: Convert the predicted word sequence generated by the decoding layer into readable natural language text; Training and optimization: The training process of the dialogue system involves forward propagation and backpropagation. During the training process, the model continuously adjusts the weights to minimize the difference between the predicted response and the actual response. Evaluation and adjustment: The effectiveness of the model can be evaluated by evaluating the performance of the dialogue system; based on the evaluation results, the model can be further adjusted and optimized.

2. The method according to claim 1, characterized in that Data preprocessing, including: Word segmentation: Split the original text into smaller units, with the goal of converting the text into discrete units that the model can process; Word embedding: Map the segmented words or subwords into continuous vectors; Remove stop words; Punctuation processing: retain their original role in the text, or convert them into labels or features that the model can recognize; Normalization: unify different forms in the text; Entity recognition: Identify these entities and give them special labels so that the information can be better processed in subsequent processing; Language conversion: Convert the input text into a language that the model can handle.

3. The method according to claim 1, characterized in that Input sequence code, including: a) Embedding layer: In the neural network model, the embedding layer is responsible for converting discrete words into continuous vectors; each word corresponds to a unique vector, which captures the semantic information of the word in a high-dimensional space; b) Vector representation: Through the embedding layer, each word is converted into a fixed-size vector, which is then passed as input to the LSTM model; c) LSTM model encoding: In the LSTM model, each vector of the input sequence is processed sequentially and encoded by the LSTM unit; d) Contextual information capture: The LSTM model captures long-term dependencies in the input sequence through its recurrent structure; e) Hidden states and memory cells: In each iteration of the LSTM model, the hidden states and memory cells are updated with the contextual information of the input sequence; these states are subsequently used as the initial states in the decoding process, allowing the model to leverage previous conversation information when generating responses.

4. The method according to claim 1, characterized in that Initialize hidden states and memory cells, including: a) Hidden state transfer: In successive iterations of the conversation, the hidden state of the LSTM model is transferred from one iteration to the next; this state transfer allows the model to maintain the context of the conversation and remember previous content even in long conversations; b) Forgetting and remembering: The forget gate and memory gate of the LSTM model control the forgetting and remembering of information; c) Initialize a new state: At the beginning of a conversation or when the conversation switches to a new topic, start from a completely new state; hidden states and memory cells will be initialized to random values ​​or general values ​​determined by heuristic methods; d) Integration of contextual information: During the conversation, the model needs to integrate contextual information from different sources and update its hidden states and memory cells to reflect these new contextual clues; e) Training and optimization: During the training process, the model learns how to initialize hidden states and memory cells through continuous forward propagation and backpropagation; f) Dynamic adjustment: In the actual application of the dialogue system, the initialization strategy of the hidden state and memory cells is dynamically adjusted according to the progress of the dialogue.

5. The method according to claim 4, characterized in that When initializing hidden states and memory cells, the forget gate determines which information should be forgotten from the state of the previous moment, while the memory gate determines which information should be retained or enhanced.

6. The method according to claim 1, characterized in that Decoding generates a response involving a decoding layer, which uses the hidden state of the previous time step to predict the next word; different strategies can be used during the decoding process.

7. The method according to claim 1, characterized in that Training and Optimization: a) Dataset preparation: Before training, prepare a dataset containing annotated dialogues. This dataset should cover various dialogue scenarios and topics. b) Forward propagation: In the forward propagation phase, the model receives the user's input sequence and converts it into vector form through the embedding layer; these vectors are then fed into the LSTM model, where the hidden state and memory cells are updated based on the input sequence; at each time step, the model generates a predicted response vector; c) Loss function calculation: There is a difference between the predicted response of the model and the actual response, and this difference is quantified by the loss function; d) Back-propagation: During the back-propagation phase, the model adjusts the weights based on the differences calculated by the loss function; e) Weight Update: Once the gradients are calculated, an optimization algorithm is used to update the weights of the model; the goal of the optimization algorithm is to reduce the value of the loss function, thereby improving the prediction accuracy of the model; f) Learning rate adjustment: During the training process, the learning rate is adjusted according to the performance of the model; g) Training monitoring: During the training process, the performance of the model needs to be monitored; h) Early stopping method: Prevent the model from overfitting on the training data and stop training when the performance on the validation set no longer improves.

8. The method according to claim 7, characterized in that The process of backpropagation involves the calculation of gradients, which are vectors whose direction points to the direction in which the loss increases fastest and whose magnitude indicates the rate at which the loss increases; the backpropagation algorithm calculates these gradients by comparing the predicted response with the actual response.

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