Abstract

Recurrent Neural Networks (RNNs) represent a foundational architecture in natural language processing because of their ability to model sequential data in a natural and flexible way. Unlike traditional n-gram or feed-forward language models that rely on fixed-sized context windows, RNNs incorporate a dynamic mechanism for capturing temporal dependencies in language. This is achieved through recurrent connections that allow the network to maintain and update a hidden state, effectively functioning as a memory of previous inputs.

At each time step, an RNN takes the current input—such as a word in a sentence—and combines it with the hidden state from the previous step. This feedback loop enables the model to “remember” information from earlier in the sequence, potentially extending hundreds of words into the past. As a result, RNNs overcome the limited-context problem inherent in earlier models and provide a richer, more adaptive representation of linguistic context.

RNN language models (Mikolov et al., 2010) exemplify this capability by processing text one word at a time and predicting the next word based on both the current input and the accumulated hidden state. This allows for modeling long-range dependencies, a key requirement for understanding and generating natural language.

Training an RNN language model uses a self-supervised learning approach. The sequential structure of natural text serves as the supervision signal: at each position in the training corpus, the model is asked to predict the next word. The learning objective is to minimize the cross-entropy loss, which measures how far the model’s predicted probability distribution deviates from the true next-word distribution. Because no manual labeling is required, this process leverages large amounts of raw text efficiently and effectively.

Beyond language modeling, RNNs have played an important role in a wide range of sequence labeling tasks, including part-of-speech tagging, named entity recognition, and sentiment analysis. Their strength lies in capturing contextual information that spans across the sequence, enabling more accurate and context-aware predictions. RNNs also excel in real-time sequential processing scenarios, such as analyzing sensor data or detecting anomalies, where inputs are received one at a time and immediate decisions are required.

Sequence models are the backbone of many modern NLP applications:
• Machine Translation: Converting text from one language to another.
• Speech Recognition: Transcribing spoken language into text.
• Sentiment Analysis: Determining the emotional tone or sentiment of a text.
• Chatbots and Virtual Assistants: Generating human-like, context-aware responses in conversations.
• Named Entity Recognition (NER): Identifying and classifying key information (like names, locations, and dates) within text.
• Text Summarization: Automatically creating concise summaries of longer documents.

Understanding RNNs—along with their architectural variations and mathematical foundations—is essential for applying them to modern NLP tasks. Despite being surpassed in many areas by Transformer-based models, RNNs remain crucial for grasping the evolution of sequence modeling and for applications where lightweight, online, or low-latency processing is required. In this seminar I will explore the principles of RNNs, their training methodologies, and their applications in sequence labeling, highlighting their significance in the broader landscape of natural language processing.