Dialogue Systems: From Chatbots to Conversational AI
Module: Natural Language Processing | Difficulty: Advanced
Task-Oriented Dialogue
- NLU: Understand user intent
- DST: Track dialogue state
- Policy: Decide next action
- NLG: Generate response
Open-Domain Dialogue
Retrieval-Enhanced Dialogue
Dialogue State Tracking
import torch
import torch.nn as nn
class DialogueStateTracker(nn.Module):
def __init__(self, bert_model, n_slots):
super().__init__()
self.bert = bert_model
self.slot_classifier = nn.Linear(768, n_slots)
self.intent_classifier = nn.Linear(768, 10)
def forward(self, input_ids, attention_mask):
outputs = self.bert(input_ids, attention_mask=attention_mask)
cls_output = outputs.last_hidden_state[:, 0, :]
slot_logits = self.slot_classifier(outputs.last_hidden_state)
intent_logits = self.intent_classifier(cls_output)
return slot_logits, intent_logits
Research Insight: Modern dialogue systems combine retrieval and generation. Retrieval provides factual accuracy, while generation enables creative responses. The challenge is balancing factual consistency with engaging dialogue.