Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality. Most NLG systems in common use employ rules and heuristics and tend to generate rigid and stylised responses without the natural variation of human language. They are also not easily scaled to systems covering multiple domains and languages. This paper presents a statistical language generator based on a semantically controlled Long Short-term Memory (LSTM) structure. The LSTM generator can learn from unaligned data by jointly optimising sentence planning and surface realisation using a simple cross entropy training criterion, and language variation can be easily achieved by sampling from output candidates. With fewer heuristics, an objective evaluation in two differing test domains showed the proposed method improved performance compared to previous methods. Human judges scored the LSTM system higher on informativeness and naturalness and overall preferred it to the other systems.
As noted above, corpus-based NLG aims at learning generation decisions from data with minimal dependence on rules and heuristics. A pioneer in this direction is the class-based n-gram language model (LM) approach proposed by . later addressed some of the limitations of class-based LMs in the over-generation phase by using a modified generator based on a syntactic dependency tree. proposed a phrase-based NLG system based on factored LMs that can learn from a semantically aligned corpus. Although active learning @cite_27 was also proposed to allow learning online directly from users, the requirement for human annotated alignments limits the scalability of the system. Another similar approach casts NLG as a template extraction and matching problem, e.g., train a set of log-linear models to make a series of generation decisions to choose the most suitable template for realisation. later show that the outputs can be further improved by an SVM reranker making them comparable to human-authored texts. However, template matching approaches do not generalise well to unseen combinations of semantic elements.
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Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents Bagel, a statistical language generator which uses dynamic Bayesian networks to learn from semantically-aligned data produced by 42 untrained annotators. A human evaluation shows that Bagel can generate natural and informative utterances from unseen inputs in the information presentation domain. Additionally, generation performance on sparse datasets is improved significantly by using certainty-based active learning, yielding ratings close to the human gold standard with a fraction of the data.