Seq2Seq Models in Machine Learning
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Which of the following best describes the core function of Sequence-to-Sequence (Seq2Seq) models?

  • Performing statistical analysis on numerical data.
  • Transforming one data sequence into another data sequence. (correct)
  • Transforming one fixed-length vector into another fixed-length vector.
  • Classifying data into predefined categories.

    • What is a key characteristic of the data that Seq2Seq models are designed to handle?

  • Data must be in a tabular format with labels.
  • Data must be represented as a single, continuous vector.
  • Data must be in a fixed, numerical format.
  • Data is typically composed of sequences of varying sizes. (correct)

    • Which of the following is NOT a typical application of Seq2Seq models?

  • Image classification (correct)
  • Speech Recognition
  • Text Summarization
  • Machine Translation

    • What type of neural network architecture is most closely associated with Seq2Seq models?

    <p>Recurrent Neural Networks (RNNs) (D)</p> Signup and view all the answers

    In the context of Seq2Seq models, what is the primary challenge that they address concerning input and output data?

    <p>Handling input and output sequences of different length. (B)</p> Signup and view all the answers

    Besides machine translation and text summarization, what is another area where seq2seq models show significant utility?

    <p>Speech Recognition (A)</p> Signup and view all the answers

    If you had to summarize the benefit of Seq2Seq in text summarisation, which option would best represent this?

    <p>It is able to reduce the text length, whilst retaining key information. (D)</p> Signup and view all the answers

    Which of the following is a common use case for sequence-to-sequence models?

    <p>Translating a text document from English to Spanish. (A)</p> Signup and view all the answers

    What is the main advantage of LSTMs over traditional RNNs?

    <p>They can handle vanishing gradients effectively. (A)</p> Signup and view all the answers

    What function does the forget gate in an LSTM serve?

    <p>It sets the previous state value to zero if necessary. (C)</p> Signup and view all the answers

    Which of the following statements about LSTM configuration is true?

    <p>LSTMs use three gates to manage memory inputs. (C)</p> Signup and view all the answers

    What does the input gate in an LSTM control?

    <p>How much information to input at any time point. (A)</p> Signup and view all the answers

    In an LSTM network, what is the purpose of the hidden state?

    <p>To perform computations and modify states. (D)</p> Signup and view all the answers

    What is the main advantage of using Recurrent Neural Networks for text data?

    <p>They reduce the number of parameters by avoiding one-hot encoding. (B)</p> Signup and view all the answers

    In a Sequence to Sequence model for machine translation, what are the functionalities of the encoder and decoder?

    <p>The encoder learns input characteristics; the decoder predicts output words. (A)</p> Signup and view all the answers

    What type of output does a many-to-one sequence model typically produce?

    <p>A probability distribution across multiple classes. (C)</p> Signup and view all the answers

    How does a language model using RNN predict the next word in a sequence?

    <p>By utilizing an artificial zero input followed by previous predictions. (A)</p> Signup and view all the answers

    What is the purpose of the embedding layer in RNNs?

    <p>To create vector representations of tokens. (C)</p> Signup and view all the answers

    Which application is NOT typically associated with Recurrent Neural Networks?

    <p>Image recognition tasks. (D)</p> Signup and view all the answers

    In text generation using RNNs, how is the output of one prediction used in subsequent predictions?

    <p>It serves as the new input for the following prediction. (B)</p> Signup and view all the answers

    What does an RNN model evaluate to predict the next token?

    <p>The combination of the last k tokens. (C)</p> Signup and view all the answers

    What is the main consequence of the vanishing gradient problem in RNNs?

    <p>Earlier layers do not learn effectively due to small gradients. (A)</p> Signup and view all the answers

    Which function is known to contribute to the vanishing gradient problem due to its bounded nature?

    <p>Sigmoid (A)</p> Signup and view all the answers

    In contrast to standard RNN cells, what is an additional feature of Gated Recurrent Unit (GRU) cells?

    <p>They compute a candidate state before updating the memory. (C)</p> Signup and view all the answers

    What key issue do RNNs face when it comes to learning long-term dependencies?

    <p>They experience vanishing or exploding gradients. (B)</p> Signup and view all the answers

    How do RNNs compare to other n-gram models in terms of memory usage?

    <p>They take up less RAM than n-gram models. (D)</p> Signup and view all the answers

    What happens to gradient values as they are backpropagated through many layers in RNNs?

    <p>They shrink exponentially, leading to smaller gradient values. (A)</p> Signup and view all the answers

    What is one reason that GRU cells have fewer parameters compared to LSTM cells?

    <p>They lack an output gate and context vector. (B)</p> Signup and view all the answers

    What effect does a small gradient have on the learning process of an RNN?

    <p>It results in minimal adjustments to the weights. (A)</p> Signup and view all the answers

    What is a key advantage of RNNs over traditional N-gram models?

    <p>RNNs can track dependencies that are further apart. (D)</p> Signup and view all the answers

    What happens to the retention of information in RNNs as more data is processed?

    <p>Retention of past information becomes weaker. (C)</p> Signup and view all the answers

    What does the weight matrix Wa do in an RNN during forward propagation?

    <p>It is shared among all inputs across the steps. (A)</p> Signup and view all the answers

    In which phase do we compute a hidden state to carry past information in RNNs?

    <p>In the forward propagation phase. (C)</p> Signup and view all the answers

    What describes the back propagation process in RNNs effectively?

    <p>It propagates error through time. (D)</p> Signup and view all the answers

    How does information propagate through a recurrent unit in an RNN?

    <p>Both forward and through time in a structured manner. (A)</p> Signup and view all the answers

    What is indicated by the term 'horizontal direction' in the context of RNNs?

    <p>The movement through the sequence over time. (D)</p> Signup and view all the answers

    What does the term 'hidden states' refer to in the context of RNNs?

    <p>The internal variables carrying past information. (A)</p> Signup and view all the answers

    What is the primary function of the encoder in a Seq2Seq model?

    <p>To map the input data into a numerical representation (B)</p> Signup and view all the answers

    Which neural networks can be utilized in the encoder-decoder architecture for Seq2Seq models?

    <p>Recurrent Neural Networks (RNN), LSTM, CNN, and transformers (B)</p> Signup and view all the answers

    What does the decoder in a Seq2Seq model predict?

    <p>The next element in the output sequence based on the encoded input (C)</p> Signup and view all the answers

    Which of the following applications best utilizes Seq2Seq models?

    <p>Natural language processing for generating responses in chatbots (D)</p> Signup and view all the answers

    In terms of input and output, what is a key characteristic of Seq2Seq models?

    <p>They can handle variable-length sequences for both input and output. (C)</p> Signup and view all the answers

    How do Seq2Seq models perform time series prediction?

    <p>By analyzing historical data to forecast future values. (A)</p> Signup and view all the answers

    What role does the context vector play in image captioning using Seq2Seq models?

    <p>It captures important features from the input image. (C)</p> Signup and view all the answers

    Which aspect of Seq2Seq models is primarily focused on generating descriptive texts for video content?

    <p>Video Captioning (C)</p> Signup and view all the answers

    Study Notes

    Web and Text Analytics 2024-25, Week 8

    Data Preparation - Preprocessing Examples 1.1

    • Missing values are removed using df.dropna(inplace=True).
    • Ratings equal to 3 (neutral) are removed.
    • Ratings greater than 3 are coded as 1 (positive), otherwise as 0 (negative).
    • Frequency counts for each rating are calculated and plotted.
    • A simplified text preprocessing function is defined.

    Data Preparation - Preprocessing Examples 1.2

    • Missing values are removed.
    • Ratings equal to 3 are removed.
    • Ratings greater than 3 are coded as 1 (positive), otherwise as 0 (negative).
    • Positive and negative reviews are extracted.
    • Data is split into training and test sets.
    • TF-IDF is used for feature extraction.
    • Class imbalance is handled (e.g., scale_pos_weight).

    Data Preparation - Preprocessing Examples 1.3

    • Missing values are removed.
    • Neutral ratings (equal to 3) are removed.
    • Positively rated reviews (4, 5) are coded as 1, and negatively rated reviews (1,2) as 0.
    • Negations in text are removed and replaced with "not_" followed by the negated word.
    • Text is lemmatized using WordNetLemmatizer.

    Feature extraction

    • Two features are extracted from the reviews: positive words and negative words counts
    • The model is trained using these features.

    Train and test a classifier, Naïve Bayes (1)

    • Data is split into training and test sets (with 80% training and 20% testing).
    • TF-IDF is used to extract features (with specified settings).
    • Class imbalance is addressed (using scale_pos_weight).
    • A Naïve Bayes Classifier is trained.
    • A confusion matrix is used for visualization.

    Train and test a classifier, Naïve Bayes (2)

    • Data is converted into count-based features using CountVectorizer (with unigrams and bigrams).
    • Or tfidfVectorizer (with unigrams and trigrams).
    • A Naïve Bayes classifier is trained using count based data.
    • Probabilities are predicted to assign True (1) for probabilities greater than 0.8.
    • Accuracy, precision, recall, and F1 score are calculated and reported.

    Train and test a classifier, XGBoost (1)

    • A XGBoost classifier is created and used with RandomizedSearchCV to find the best hyperparameters.
    • A parameter grid is defined to search for optimal hyperparameters.
    • The best hyperparameter values are printed.
    • The best estimator from the RandomizedSearchCV is stored in the variable 'model'.

    Train and test a classifier, XGBoost (2)

    • The model predicts the sentiment of a given review.

    Train and test a classifier, XGBoost (3)

    • The model's accuracy, precision, recall, and F1-score are presented, along with a confusion matrix.

    Online Courses

    • DataCamp course on Recurrent Neural Networks with Keras is linked.
    • Coursera course on Natural Language Processing with Sequence Models is listed.

    Sequence to Sequence (seq2seq) models

    • Seq2Seq models transform one sequence into another.
    • They are useful for tasks where both input and output sequences may have varying lengths (e.g., translation, summarization).

    Use Cases of the Sequence to Sequence Models

    • Seq2Seq models are used for various applications such as machine translation, text summarization, speech recognition, chatbots, image captioning, video captioning, time series prediction, and code generation.

    Encoder-Decoder Architecture

    • The encoder-decoder architecture is a common way to build Seq2Seq models.
    • The encoder transforms the input sequence into a fixed-length representation.
    • The decoder takes the representation and produces the output sequence.
    • Different neural network architectures (like RNN, LSTM) can be used with the encoder-decoder framework.

    Recurrent Neural Networks (RNN)

    • RNNs are a type of deep learning model used for sequence data.
    • RNNs share weights among their steps and propagate information through the sequence.
    • They are effective for NLP tasks, but can struggle with long dependencies (vanishing gradient).

    RNN example

    • RNNs consider all previous words in a sentence unlike N-grams.

    RNN example

    • RNNs are able to track dependencies between words in a text corpus.
    • The same weights are applied to each word in the sequence.

    Training RNN models

    • RNNs propagate forward and backward.
    • The process is called "back propagation through time".

    RNN - Forward Propagation

    • Hidden states are important for RNNs to carry prior information during computation.
    • A weight matrix (Wa) is shared among steps in the sequence.

    RNN - Back Propagation through time

    • The gradient shrinks as it propagates backward through time, which leads to the "vanishing gradient" problem.
    • RNNs can struggle with long-term dependencies.

    Vanishing Gradient Problem

    • As information propagates backward further in a sequence, the gradient can approach zero.

    RNNs and Vanishing Gradients

    • RNNs have advantages such as being able to capture long-range dependencies and taking up less memory than n-gram methods.
    • RNNs have disadvantages like struggling to deal with long-range dependencies because of the vanishing gradient issue.

    Simple RNN cell

    • In the simple RNN cell, the weight matrix Wa is shared across inputs and steps.

    GRU cells

    • GRU cells use gates to control the flow of information (like input, update, and forget).
    • GRU cells address some of the problems with basic RNNs.

    GRU and LSTM

    • GRU is a variant of LSTM with fewer parameters.
    • GRU and LSTM architectures are effective for various sequence learning tasks, yet GRU has fewer processing steps.

    LSTM

    • LSTMs are designed to better handle long-range dependencies in sequences.
    • LSTMs contain cell state and hidden states with gates.

    LSTM

    • LSTM cells have 3 gates: input, forget, and output.
    • The forget gate decides if information from previous steps should be discarded.

    RNN - Embeddings

    • Embedding layers can be utilized to produce vector representations of tokens.

    RNN example

    • RNNs can consider previous words in a sentence.

    RNN example

    • RNNs track dependencies in text and employ the same weights for every word.

    RNN example

    • LSTMs and GRUs are used to address the RNN's long-term dependency limitations.

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    Web and Text Analytics 2024-25 Week 8 PDF

    Description

    This quiz explores the core functions and characteristics of Sequence-to-Sequence (Seq2Seq) models in machine learning. It covers applications, challenges, and the advantages of LSTMs in handling sequential data. Test your understanding of how Seq2Seq models are applied in various tasks, including machine translation and text summarization.

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