What is multi-turn understanding and how is it modeled?

Multi-turn understanding is the ability of a natural language understanding (NLU) system to comprehend and maintain the context of a dialogue over multiple exchanges between the user and the system. Unlike a single-turn query, where only the current input is analyzed, multi-turn considers the dialogue history to correctly interpret intentions and entities.

To model multi-turn understanding, the following approaches are typically used:

• Context storage: storing the state of the dialogue (e.g., previous questions, answers, identified slots) in memory or a database.
• Recurrent neural networks (RNN, LSTM, GRU): capable of considering the sequence and context of previous messages.
• Transformers with attention mechanisms: allow effective modeling of long contexts and highlighting important parts of the dialogue history.
• Dialogue managers: implement logic for transitioning between dialogue states using rules or trainable models.

An example of a simplified approach in Python using a list to store history:

history = []

def update_context(user_input):
    history.append(user_input)
    # Analyze history to determine the current intent
    # For example, if the history contains a question about the weather, the next query "and tomorrow?" will be interpreted as clarification

# User inputs several utterances
update_context("What is the weather today?")
update_context("And tomorrow?")

Thus, multi-turn understanding is a key element for creating natural and coherent dialogue systems.