Teacher Forcing In Recurrent Neural Networks (RNNs): An Advanced Concept Made Simple

by | Oct 12, 2023 | Artificial Intelligence, Machine Learning, Natural Language Processing

What is teacher forcing?

Teacher forcing is a training technique commonly used in machine learning, particularly in sequence-to-sequence models like Recurrent Neural Networks (RNNs) and sequence-to-sequence models with attention mechanisms, such as the Transformer.

In training sequence generation models, like language models or machine translation systems, teacher forcing involves using the true or ground truth target sequence as input during training.

How does teacher forcing work?

During training, the model is provided with an input sequence, expected to generate an output sequence step by step. The model generates an output token for each step based on the input and previously generated tokens.

The model is given the actual target (correct) sequence as input at each step instead of its previously generated tokens in teacher forcing. This means that the model gets to “see” the correct output sequence during training and is guided by it.

The model is given the actual target (correct) sequence as input at each step instead of its previously generated tokens in teacher forcing

The model is given the actual target (correct) sequence as input at each step instead of its previously generated tokens.

The model’s parameters are updated based on the loss between its generated output and the true target output at each step. This helps the model learn to create sequences closer to the desired target.

It has several advantages during training:

  • Faster Convergence: It can lead to faster convergence during training because the model receives more accurate and consistent supervision.
  • Reduced Error Propagation: It mitigates the issue of error propagation, where mistakes made early in the sequence can compound and affect the quality of later predictions.

However, it’s important to note that it also has some limitations:

  • Exposure Bias: The model may become overly reliant on the teacher-provided signals and struggle to generate sequences independently during inference (i.e., without teacher forcing). This is known as exposure bias.
  • Mismatch Between Training and Inference: If the model relies heavily on teacher forcing during training, it may not perform as well during inference when it has to generate sequences step by step without access to the true target.

To address these limitations, a common approach is to use a “scheduled sampling” technique during training. Scheduled sampling gradually transitions from using it to using the model’s predictions as input, which helps the model adapt to generating sequences independently.

Teacher forcing is a valuable training technique for sequence generation models. Still, it should be used judiciously, and potential issues like exposure bias should be considered when applying it.

Step-by-step guide on how to implement teacher forcing

Teacher forcing is a training technique that plays a pivotal role in sequence-to-sequence models, enhancing their capacity to generate accurate sequences. To truly grasp how it operates, let’s break down the process step by step:

Step 1: Data Preparation 

Before diving into the inner workings, you need a dataset containing pairs of input sequences and their corresponding target sequences. These pairs serve as the foundation for training your sequence generation model.

Step 2: Training the Model 

The core idea behind teacher forcing is to use the ground truth or actual target sequence as the input during training, at least in the early stages of the process. This enables the model to receive precise guidance from the outset.

  • Input: At each time step, the model receives a token from the true target sequence. This serves as the initial input.
  • Predictions: The model then predicts based on this initial token and its learned parameters.
  • Feedback: The true target sequence provides immediate feedback. The model can compare its prediction to the correct token at this step.
  • Loss Calculation: A loss function measures the disparity between predicted and actual tokens. This is typically done using techniques like cross-entropy loss.
  • Parameter Update: To minimize the loss, the model’s parameters are adjusted through backpropagation and optimization methods (e.g., gradient descent).

Step 3: Iteration 

The above steps are repeated for each time step in the sequence. The model’s training process involves generating one token at a time while relying on the true target sequence to guide its predictions. This iterative process continues until the entire target sequence has been generated.

Step 4: Gradual Transition 

One vital aspect is that teacher forcing doesn’t have to be used throughout training. Using it exclusively can lead to exposure bias, where the model struggles to generate sequences independently during inference.

A technique called “scheduled sampling” is often employed to address this. Scheduled sampling gradually transitions from using the true target as input to using the model’s predictions. This helps the model adapt to generating sequences independently, reducing the reliance on teacher forcing.

Step 5: Inference 

Once the model has been trained using teacher forcing and has learned to generate sequences effectively, it can be used for inference. The model cannot access the true target during inference and must create sequences based on its predictions.

Teacher forcing is a crucial training technique for sequence-to-sequence models, ensuring faster convergence and reduced error propagation during training. However, it is vital to know its limitations, such as exposure bias, and use techniques like scheduled sampling to balance training and inference performance. Understanding how teacher forcing works is fundamental to harnessing the power of sequence generation models in various applications.

What are the advantages of teacher forcing?

Teacher forcing is a powerful technique in machine learning that offers several critical advantages during the training of sequence-to-sequence models. Understanding these advantages helps us appreciate the significance of teacher forcing in various applications. Here are the main benefits:

1. Faster Convergence:

  • During training, it provides the model with accurate and consistent guidance using the true target sequence as input. This helps the model converge to a solution more rapidly than training without teacher forcing.
  • The direct supervision provided by teacher forcing reduces the need for the model to explore a wide range of possibilities, making it more efficient in learning the correct sequence generation.

2. Reduced Error Propagation:

  • One common issue in sequence generation is error propagation. Mistakes made in earlier steps of sequence generation can accumulate and affect the quality of subsequent predictions.
  • Teacher forcing helps mitigate this problem by ensuring that each step of the model’s training receives accurate inputs, allowing it to correct errors and make better predictions.

3. Stable Training:

  • Training sequence-to-sequence models can be challenging due to the intricacies of sequence data. Teacher forcing provides a stable and consistent training process by maintaining a clear and well-defined input-output relationship.
  • Compared to models that generate their inputs, the reduced variability in training data contributes to more stable convergence and better training outcomes.

4. Explicit Supervision:

  • It provides explicit, ground truth supervision at every time step during training. This means that the model can access the “correct” answer, which can be particularly valuable for tasks where the target sequence follows a specific structure or pattern.
  • For tasks like machine translation or speech recognition, teacher forcing can ensure that the model effectively learns the target language’s grammar, vocabulary, and structural nuances.

5. Controlled Exploration:

  • Teacher forcing enables the model to explore the sequence space in a controlled manner. It ensures that the model encounters the correct examples early in training, allowing it to form a solid foundation for generating sequences.
  • By guiding the model’s exploration, teacher forcing helps the model generalize well to unseen examples and produce more coherent and contextually accurate sequences.

6. Easier Evaluation:

  • Since teacher forcing uses the true target sequences during training, evaluating the model’s performance is relatively easy. You can directly compare the generated sequences to the target sequences for quantitative assessment.
  • This simplicity in evaluation makes monitoring the model’s progress and making necessary adjustments during training convenient.

Teacher forcing is a valuable training technique that offers faster convergence, reduced error propagation, and more stable training for sequence-to-sequence models. Its advantages are particularly beneficial for tasks involving sequence generation in various domains, from natural language processing to speech recognition, making it an essential tool in the machine learning toolkit. However, it is critical to know its limitations and consider techniques like scheduled sampling to address potential issues.

What are the limitations of teacher forcing?

While teacher forcing offers substantial advantages in training sequence-to-sequence models, it has limitations. Understanding these drawbacks is crucial for making informed decisions about when and how to use this technique. Here are the fundamental limitations:

1. Exposure Bias:

  • One of the most significant limitations is the potential for exposure bias. Exposure bias occurs when the model relies on the perfect supervision provided during training.
  • Since the model is accustomed to receiving true target sequences as input during training, it may struggle to generate sequences independently during inference or real-world use. This leads to a disconnect between training and deployment performance.

2. Lack of Real-World Noise:

  • Teacher forcing relies on pristine, error-free target sequences for training. In real-world scenarios, data often contains noise, errors, and variations.
  • Models trained with teacher forcing may not learn to handle noisy or imperfect input data effectively. This makes them less robust in practical applications where input data may deviate from the ideal.

3. Limited Exploration:

  • Teacher forcing constrains the model’s exploration by consistently guiding it with the true target sequence. This can limit the model’s ability to discover novel or creative solutions.
  • It may not be the best choice in scenarios where creativity and diversity are essential, such as text generation for artistic purposes.

4. Mismatch Between Training and Inference:

  • Models trained with teacher forcing may exhibit a mismatch between their performance during training and inference.
  • The model can access the true target during training, but it must generate sequences based on its predictions during inference. This transition can result in suboptimal performance.

5. Incomplete Training Data:

  • In some cases, the true target sequences may be incomplete or truncated. For instance, the reference translation may not cover all possible valid translations in language translation.
  • The model may struggle to handle unseen variations or provide appropriate translations.

6. Resource-Intensive:

  • Training with teacher forcing often requires substantial training data, making it resource-intensive. It relies on a large set of paired input-output sequences.
  • Generating or curating high-quality training data can be time-consuming and costly.

7. Difficulty in Reinforcement Learning Integration:

  • Combining teacher forcing with reinforcement learning to fine-tune a model can be challenging. Transitioning from it to reinforcement learning can introduce model training and stability complications.

8. Ethical and Bias Concerns:

  • When using teacher forcing, being aware of the training data’s biases and potential ethical concerns is crucial. If the training data contains biases, the model may learn and perpetuate those biases.

Teacher forcing is a valuable training technique but is not a one-size-fits-all solution. Understanding its limitations is vital for practitioners to make informed choices about when and how to use it. Techniques like scheduled sampling and curriculum learning are often employed to address some of these limitations and balance the benefits and drawbacks of teacher forcing.

Scheduled Sampling and Addressing Limitations

To mitigate the limitations of teacher forcing and strike a balance between training and inference performance, researchers have developed a technique known as “scheduled sampling.” This technique aims to address some of the challenges associated with it. This section will explore scheduled sampling and how it helps tackle these limitations.

What is Scheduled Sampling?

Scheduled sampling is a training strategy that gradually transitions from using teacher forcing to using the model’s predictions as input during training. Instead of providing the true target sequence as input at every time step, the transition allows the model to adapt to generating sequences independently.

A schedule controls the transition from teacher forcing to the model’s predictions. Early in training, the schedule favours teacher forcing, ensuring the model receives significant guidance. As training progresses, the schedule gradually shifts towards using the model’s predictions, reducing the reliance on the true target.

Addressing Limitations with Scheduled Sampling

Scheduled sampling addresses several limitations associated with teacher forcing:

1. Exposure Bias Mitigation:

By gradually exposing the model to its predictions, scheduled sampling helps reduce exposure bias. The model learns to generate sequences in a manner that is more consistent with how it will operate during inference.

2. Improved Inference Performance:

Since the model becomes less reliant on teacher forcing over time, it is better prepared for generating sequences independently during inference. This transition aligns training with real-world use cases, improving deployment performance.

3. Handling Noisy Data:

Scheduled sampling enables the model to handle noisy and imperfect data effectively. As the schedule shifts towards using the model’s predictions, it adapts to generating sequences even when the input contains errors or variations.

4. Encouraging Exploration:

The gradual transition encourages exploration and creativity in sequence generation. It allows the model to take calculated risks and explore alternative solutions rather than sticking rigidly to the teacher-provided guidance.

5. Reduced Resource Requirements:

While teacher forcing requires a large set of paired input-output sequences, scheduled sampling can be more resource-efficient. The model relies less on true targets over time so it may require less pristine training data.

Challenges of Scheduled Sampling

Scheduled sampling is a valuable technique, but it comes with its own set of challenges:

  • Designing an effective schedule can be non-trivial, as it involves determining when and how the transition from teacher forcing to self-generated inputs should occur.
  • Striking the right balance between teacher forcing and model-generated inputs can be delicate and require experimentation.
  • The choice of schedule may vary depending on the specific problem and dataset.

Practical Implementation

Practically implementing scheduled sampling involves defining the schedule, monitoring model performance, and adjusting the schedule as needed during training. Researchers and practitioners may use heuristics, such as annealing the probability of using teacher forcing over time, to create an effective schedule.

Scheduled sampling is a valuable technique that addresses the limitations of teacher forcing. It facilitates a gradual transition from teacher forcing to the model’s predictions, improving the model’s ability to generate sequences independently during inference. While it introduces challenges, it is a powerful tool for training sequence-to-sequence models that must balance training efficiency with deployment performance.

Applications of Teacher Forcing

Teacher forcing is a versatile training technique with applications in various domains within machine learning, particularly in tasks involving sequence generation. Here, we’ll explore some key applications where teacher forcing plays a crucial role:

1. Natural Language Processing (NLP):

  • Machine Translation: Teacher forcing is commonly used in training neural machine translation models. It enables the model to learn the correct translation of sentences in different languages by providing the true translation as input during training.
  • Text Generation: In text generation tasks, such as language modelling or generative text tasks, it guides the model to produce coherent and contextually appropriate text.

2. Speech Recognition:

  • Teacher forcing is instrumental in training automatic speech recognition (ASR) models. These models transcribe spoken language into text, and it helps the model recognize and transcribe speech accurately.

3. Handwriting Recognition:

  • In tasks where handwritten text is converted into machine-readable text, teacher forcing assists in training models that can accurately recognize and transcribe handwritten characters and words.

4. Image Captioning:

  • Image captioning models use it to learn how to generate descriptive captions for images. The true captions are provided as input during training, allowing the model to align visual content with textual descriptions.

5. Dialogue Systems:

  • In developing chatbots and conversational agents, it is employed during training to ensure the model generates contextually appropriate responses. It helps in teaching the model how to engage in meaningful conversations.

6. Text Summarization:

  • Teacher forcing is used to train models for automatic text summarization. The model learns to extract and condense essential information from longer text passages by providing the correct summaries as input during training.

7. Music Generation:

  • Teacher forcing is applied to train models that can generate music sequences in music generation tasks. It guides the model to produce harmonious and coherent musical compositions.

8. Time Series Forecasting:

  • Teacher forcing is valuable in time series forecasting tasks. Models that predict future values in a time series use teacher forcing to learn from past data and generate accurate predictions.

9. Game Playing Agents:

  • In reinforcement learning tasks, where an agent learns to play games, teacher forcing can be used in the early stages of training to guide the agent toward optimal moves and strategies.

10. Content Recommendation:

  • In recommendation systems, where content or products are recommended to users, teacher forcing can assist in training models to predict user preferences and generate personalized recommendations.

11. Video Captioning:

  • Models for generating video captions leverage it to understand the content of video frames and produce relevant textual descriptions.

12. Autonomous Vehicles:

  • In the development of autonomous vehicles, it can be used to train models that process sensor data and make decisions, such as steering and braking, based on a sequence of inputs.

In these applications, teacher forcing helps to expedite training, improve model performance, and ensure that generated sequences are accurate and contextually meaningful. While it is a valuable technique, practitioners should be mindful of its limitations, such as exposure bias, and consider techniques like scheduled sampling to address them when necessary.

Practical Tips for Implementing Teacher Forcing

Implementing teacher forcing effectively is crucial for training sequence-to-sequence models. Here are practical tips to guide you in utilizing it for your machine learning projects:

1. Start with Teacher Forcing:

Begin your training process with teacher forcing. This provides a stable foundation for your model by allowing it to learn from the true target sequences.

2. Gradual Transition with Scheduled Sampling:

Use scheduled sampling to mitigate exposure bias and prepare your model for independent sequence generation. Define a schedule that gradually reduces the probability of using teacher forcing and increases the likelihood of using the model’s predictions as input.

3. Monitor Model Performance:

Continuously evaluate your model’s performance during training. Track metrics relevant to your specific task, such as loss, accuracy, or BLEU scores in machine translation.

4. Balance the Schedule:

Adjust the schedule for scheduled sampling based on how well your model is learning. If the model struggles to generate sequences independently, you may need to extend the period of teaching.

5. Experiment with Schedule Strategies:

Explore different strategies for creating your schedule. Some common approaches include linear annealing, exponential annealing, or curriculum learning. Choose the one that best suits your problem and dataset.

6. Introduce Noise and Perturbations:

To improve your model’s robustness to real-world data, consider introducing noise, variations, and perturbations into the training data. This can help your model learn to handle imperfect input.

7. Diverse Training Data:

Ensure that your training dataset is diverse and representative of the real-world scenarios your model will encounter. A diverse dataset helps the model generalize better.

8. Data Preprocessing:

Preprocess your data carefully. Depending on your specific application, this may involve tokenization, padding, or any necessary data transformations.

9. Experiment with Network Architectures:

Experiment with different neural network architectures to find the one that works best for your task. Common choices include LSTM, GRU, and transformer models.

10. Attention Mechanisms:

Explore the use of attention mechanisms, which can improve the model’s ability to focus on relevant parts of the input sequence when generating the output.

11. Hyperparameter Tuning:

Conduct hyperparameter tuning to optimize various aspects of your model, such as learning rate, batch size, and the size of hidden layers.

12. Address Ethical Concerns:

Be aware of the ethical considerations when training models using it. Ensure that your training data is free from biases, or implement strategies to mitigate bias in the model’s predictions.

Implementing teacher forcing effectively requires a combination of best practices, experimentation, and a deep understanding of your specific task. By following these practical tips and continuously improving your approach, you can harness its power to train accurate and reliable sequence-to-sequence models.


Teacher forcing is a vital training technique in sequence-to-sequence models, offering a structured and efficient way to guide models in generating sequences with precision. In this blog post, we’ve delved into the core aspects, understanding how it works, its advantages, limitations, and strategies to address these limitations.

From its applications in natural language processing and speech recognition to aiding dialogue systems, music generation, and more, it has proven valuable in many machine learning domains. Its ability to expedite training, reduce error propagation, and ensure accurate sequence generation is undeniable.

However, it’s essential to acknowledge the challenges of teacher forcing, particularly the exposure bias that can hinder model performance during inference. This is where scheduled sampling emerges as a valuable tool, allowing for a gradual transition from teacher forcing to independent sequence generation and bridging the gap between training and deployment.

Implementing teacher forcing effectively requires a balance of best practices, hyperparameter tuning, data preprocessing, and careful consideration of ethical concerns, especially in applications where bias could be a concern.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

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