dialoguegeneration

Dialogue generation is the task of producing natural language responses in a conversational agent or chatbot. It encompasses open-domain chat and task-oriented systems, where models must generate coherent, relevant, and contextually appropriate utterances given a conversational history.

Historically, approaches ranged from rule-based and statistical methods to neural networks. Modern dialogue generation commonly uses neural sequence-to-sequence architectures and transformer-based models. Techniques include encoder-decoder frameworks with attention, copy mechanisms for factual fidelity, and retrieval-augmented generation that combines neural generation with retrieved content. Some systems incorporate knowledge grounding to maintain consistency with external databases or domain knowledge.

Training relies on large conversational corpora, such as open-domain chat logs or task-specific dialogues. Objective functions typically maximize the likelihood of the next utterance, with additional objectives for diversity, factual accuracy, or consistency. Evaluation uses automatic metrics (BLEU, ROUGE, METEOR, perplexity, BERTScore, BLEURT, COMET) and human judgments assessing relevance, coherence, fluency, and safety. Limitations of automatic metrics motivate human evaluation in many settings.

Applications include customer service, virtual assistants, tutoring, entertainment, and gaming. Challenges include maintaining long-term coherence and persona, avoiding unsafe or biased content, controlling style and content, and adapting to new domains. Data quality, data privacy, and scalability are ongoing concerns, especially for low-resource languages.

Emerging directions seek improved evaluation methods, controllable and personalized generation, robust retrieval-grounded systems, multimodal dialogue that incorporates vision and other signals, and more efficient, privacy-preserving training methods.