Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods

Understand

Natural Language Generation (NLG) has made great progress in recent years due to the development of deep learning techniques such as pre-trained language models.

• This advancement has resulted in more fluent, coherent and even properties controllable (e.g.
• stylistic, sentiment, length etc.) generation, naturally leading to development in downstream tasks such as abstractive summarization, dialogue generation, machine translation, and data-to-text generation.
• However, the faithfulness problem that the generated text usually contains unfaithful or non-factual information has become the biggest challenge, which makes the performance of text generation unsatisfactory for practical applications in many real-world scenarios.