LSTM for text in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does LSTM stand for in machine learning?

LSTM stands for Long Short-Term Memory. It is a type of neural network designed to remember information for long periods, especially useful for sequences like text.

intermediate

Why are LSTMs better than simple RNNs for text data?

LSTMs can remember important information over longer sequences and avoid the problem of forgetting early data, which simple RNNs struggle with due to vanishing gradients.

intermediate

Name the three main gates inside an LSTM cell.

The three main gates are: Forget Gate (decides what to forget), Input Gate (decides what new information to add), and Output Gate (decides what to output).

beginner

How does an LSTM process a sentence for text classification?

An LSTM reads the sentence word by word, updating its memory at each step. After the last word, it uses the final hidden state to predict the sentence's category.

beginner

What is a common metric to evaluate LSTM models on text classification tasks?

Accuracy is commonly used to measure how many sentences the LSTM correctly classifies out of all tested sentences.

What problem does an LSTM solve better than a simple RNN?

A Remembering long-term dependencies in sequences

B Faster training on images

C Reducing model size

D Handling missing data

LSTMs are designed to remember information over long sequences, which simple RNNs struggle with.

Which gate in an LSTM decides what information to forget?

A Input Gate

B Forget Gate

C Output Gate

D Memory Gate

The Forget Gate controls which information is removed from the cell's memory.

In text classification, what does the LSTM use to make the final prediction?

A The final hidden state after reading the sentence

B The average of all word embeddings

C The first word embedding

D The length of the sentence

The final hidden state summarizes the entire sentence and is used for prediction.

Which of these is NOT a typical use of LSTMs?

A Text generation

B Speech recognition

C Machine translation

D Image classification

Image classification usually uses convolutional neural networks, not LSTMs.

What is a common input format for LSTM models working on text?

A Raw text strings

B Pixel values

C One-hot encoded vectors or word embeddings

D Audio waveforms

LSTMs require numerical input like one-hot vectors or embeddings representing words.

Explain how an LSTM processes a sentence step-by-step for text classification.

Think about how the LSTM reads and remembers words in order.

Describe the role of the forget, input, and output gates inside an LSTM cell.

Each gate controls a different part of the memory update.