Backward and Forward Language Modeling for Constrained Sentence Generation

Understand

Recent language models, especially those based on recurrent neural networks (RNNs), make it possible to generate natural language from a learned probability.

• Language generation has wide applications including machine translation, summarization, question answering, conversation systems, etc.
• Existing methods typically learn a joint probability of words conditioned on additional information, which is (either statically or dynamically) fed to RNN's hidden layer.
• In many applications, we are likely to impose hard constraints on the generated texts, i.e., a particular word must appear in the sentence.