Content Comparison

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For example, a classification algorithm (classifier) might learn to predict whether a given email is spam or not, as illustrated below. This is a binary classification task, where the goal is to categorize the input data into two mutually exclusive classes. In this case, the training data is labeled with binary categories, such as "spam" and "not spam," "true" and "false," or "positive" and "negative." These labels guide the model in learning the differences between the two categories, allowing it to make accurate predictions when exposed to new data.

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You can do just the basic reading of the above. There are also more in-depth explanations on the following third page.

• Grokking Deep Learning

• https://www.geeksforgeeks.org/introduction-machine-learning/

• https://medium.com/@jainvidip/understanding-train-test-and-validation-data-in-machine-learning-f8276165619c

• https://www.datacamp.com/blog/classification-machine-learning

A General Pipeline of Task-Oriented Spoken Dialogue Systems

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Whisper is a commonly used general-purpose speech recognition model developed by OpenAI. It is trained on a large dataset of diverse audio and is also a multi-tasking model that can perform multi-lingual speech recognition, speech translation, and language identification.

NLU with BERT

Unlike the open-domain dialogue (e.g., chitchat), task-oriented dialogue’s pattern is restricted by a dialogue ontology, which defines all possible intents, slots, and their corresponding candidate values in specific domains. The NLU component maps a user’s utterance to a structured semantic representation, which includes the intent behind the utterance and a set of key-value pairs known as slots and values. This mapping enables dialogue systems to understand user needs and respond appropriately. For example, given the transcribed utterance “Recommend a restaurant at China Town““I want to cook Italian pizza“, the NLU model can identify: the intent as “inform“ “addFilter“ and the value of the slot “destination“as “China Town““ingredienttype“as “italian pizza“.

NLU task → Intent and Slot Classification

The NLU task can be approached as joint learning of intent classification (IC) and slot filling (SF), with the slot labels typically formatted in the widely-used BIO format, as shown below. In general, joint learning of intent and slot classification models are mutually beneficial. https://arxiv.org/abs/2011.00564 models are mutually beneficial.

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• BERT, Bidirectional Encoder Representations from Transformers, is a widely used transformer-based language model designed for various natural language processing tasks, including classification. It consists of two types of training procedures:

• During pre-training, BERT is trained on a large corpus of English text in a self-supervised manner. This means it is trained on large-scale, raw, unlabeled text without human annotations, using an automatic process to generate input-output pairs from the text.

• During fine-tuning, BERT is first initialized with its pre-trained parameters, and then all parameters are fine-tuned using labeled data from downstream tasks, allowing it to adapt to specific applications.

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• -supervised manner. This means it is trained on large-scale, raw, unlabeled text without human annotations, using an automatic process to generate input-output pairs from the text.

• During fine-tuning, BERT is first initialized with its pre-trained parameters, and then all parameters are fine-tuned using labeled data from downstream tasks, allowing it to adapt to specific applications.

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LLMs on Hugging Face

Hugging Face is an AI community and platform that offers an easy-to-use interface for accessing and utilizing pretrained large language models (LLMs) like BERT released by various organizations and researchers. Here is a simple example of how to use this model to get the features of a given text in PyTorch:

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