Eric Shi, 2023-03-18
Photo by Google DeepMind on Unsplash
Transfer learning is one of the key techniques that have enabled the outstanding performance of top-tier AI models in AI writing.
Transfer learning involves transferring knowledge learned from one task to another related task. For example, an AI model can be fine-tuned on a language translation task and then transfer the knowledge learned from this task to a text summarization task. This allows the AI model to learn more efficiently and effectively and to achieve better performance on a wide range of language tasks.
For instance, knowledge learned from a language translation job can be transferred to a text summarization job. This is possible because the two seemingly very different jobs share certain “embedded” characteristics.
In language translation, an AI model learns to generate a new sentence in a target language that conveys the same meaning as the input sentence in the source language. In order to do this, the AI model must learn to capture the most important information in the input sentence and express it in a way that makes sense in the target language.
Similarly, in text summarization, the AI model must learn to capture the most important information in a longer piece of text and express it in a shorter summary that conveys the same meaning. This requires the ability to identify the most important information and to express it in a concise and coherent way.
AI researchers have recognized that the knowledge learned from language translation can be transferred to text summarization, at least in the following few different ways.
Attention Mechanisms: In language translation, the AI model learns to use attention mechanisms to focus on the most important parts of the input sentence when generating the output sentence. This same mechanism can be applied to text summarization, where the AI model can use attention to focus on the most important parts of the input text when generating the summary.
Representation Learning: In language translation, the AI model learns to represent the input sentence as a high-dimensional vector that captures its meaning. A similar vectorial representation can be used in text summarization, where the AI model can learn to represent the input text as a vector and use it to generate the summary.
Language Modeling: In language translation, the AI model learns to generate natural language outputs that are grammatically correct and make sense in the context of the input sentence. This same ability can be applied to text summarization, where the AI model can learn to generate natural language summaries that are coherent and convey the same meaning as the input text.
In natural language processing (NLP), one common way of representing words as vectors are through word embeddings which are dense, one-dimensional vectors expressed as 1D arrays of numerical components. The numerical values of the components of the vectors/embeddings are finalized through an iterative learning process of the neural network-based model. These vectors can capture the semantic and syntactic relationships between words.
For example, the hypothetical word embeddings for the sentence and phrase -- "It is so miserable" and "what a charm" -- can be expressed as the following 1D arrays with corresponding component values:
In the sentence -- "It is so miserable", the word "miserable" might have a vector representation like [0.2, -0.5, 0.1, ..., 0.3], where each component of the vector corresponds to a numerical value that captures a different aspect of the word's meaning. The sentence as a whole might have a vector representation like [0.1, -0.3, 0.2, ..., -0.1], where the components of the vector are combined to capture the overall meaning of the sentence.
In the phrase -- "what a charm," -- the word "charm" might have a vector representation like [0.4, 0.1, -0.2, ..., 0.5], where each component of the vector corresponds to a numerical value that captures a different aspect of the word's meaning. The phrase as a whole might have a vector representation like [0.3, 0.2, -0.1, ..., 0.4], where the components of the vector are combined to capture the overall meaning of the phrase.
In NLP, embeddings are a way of representing words or phrases as vectors in a high-dimensional space. These vectors capture the semantic and syntactic properties of the words or phrases and can be used as inputs to machine learning (ML) models.
In addition to the word embeddings mentioned above, named entity embeddings belong to another type of embeddings that represents named entities, such as people, organizations, and locations, as vectors. These embeddings can be learned during the training process as well or can be pre-trained on a large corpus of text data.
In named entity recognition (NER), the AI model learns to identify named entities in text, such as people, organizations, and locations. In relation extraction (RE), the AI model must identify the relationships between these named entities. In terms of transfer learning, as an example, the knowledge learned from the NER job can be transferred to the RE job in one of the following ways:
The AI model can use the named entity embeddings learned during NER to represent the named entities during RE. This allows the AI model to identify the relationships between the named entities more easily.
Attention mechanisms can be used in both NER and RE to identify the most relevant parts of the input text. By focusing on the most relevant parts of the text during RE, the AI model can more easily identify the relationships between the named entities.
The AI model can be fine-tuned as a pre-trained model on a RE dataset after training on a NER dataset. This will enable the AI model to transfer the knowledge learned during NER to RE.
In addition to word embeddings and named entity embeddings, sentiment embeddings are yet another type of embedding that represents the sentiment or emotional content of the text as vectors. These embeddings can be learned during the training process as well or can be pre-trained on a large corpus of text data.
In sentiment analysis (SA), the AI model learns to classify text as positive, negative, or neutral. In text classification (TC), the AI model must classify text into different categories, such as news articles or product reviews. In terms of transfer learning, as an example, the knowledge learned from the SA job can be transferred to the TC job in one of the following ways:
The AI model can use the features learned during SA to represent the input text during TC. With the help of a feature extraction (FE) algorithm, the AI model can learn to identify patterns in the text that are relevant for both SA and TC jobs.
The AI model can be fine-tuned as a pre-trained model on a TC dataset after training on a SA dataset. This allows the AI model to transfer the knowledge learned during SA to the TC work.
The AI model can use the embeddings learned during SA to represent the input text during the TC. This allows the AI model to capture the meaning of the text more effectively and to identify patterns that are relevant for both tasks.
The overall training process typically involves the following steps.
(1) An AI model is first pre-trained on a large corpus of text data using language modeling.
(2) Then, the AI model is fine-tuned on a sub-task (e.g., language translation), where the AI model learns to handle the sub-job (e.g., translating text from one language to another).
(3) After step (2), the AI model is fine-tuned on yet another sub-task (e.g., text summarization), where the AI model learns to handle the second sub-task (e.g., generating summaries of texts).
(4) The knowledge learned from the first sub-task is typically transferred as part of the training for the second sub-task (i.e., in step (3)). This is made possible because both sub-tasks share certain common characteristics.
Overall, the knowledge learned from language translation can be transferred to text summarization through the use of attention mechanisms, representation learning, and language modeling. By transferring this knowledge, the AI model can learn to summarize text more efficiently and effectively and generate high-quality summaries that capture the most important information.
In sentiment analysis, the components of the vectors for the words in a sentence can be combined to produce a vector representation for the entire sentence, which can then be used to predict the sentiment of the sentence. In text classification, the components of the vectors for the words in a document can be combined to produce a vector representation of the document, which can then be used to classify the document into one or more categories.
Note:
This article is the first of a set of three. The titles of the three articles are as follows,
1. Application of Transfer Learning to AI Writing
2. Training Methods That Can Impart Human Writing Skills to Computers
3. Thinking Techniques That Have Enabled a Computer to Write Better Than Average Human
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