Leveraging Invertible Models for Syntactic and Semantic Control in Natural Language Generation

In this paper, researchers explore the idea of generating sentences from a continuous space, rather than the traditional discrete word-level representations. They present two main approaches: one based on learning a mapping from words to vectors and another using a sequence-to-sequence model to generate sentences directly from the continuous space. The authors show that their approach can generate coherent and diverse sentences, and achieve state-of-the-art results on a benchmark task.
The authors begin by explaining that traditional methods of sentence generation are limited by their reliance on discrete word-level representations. They argue that these methods cannot capture the complex relationships between words in a sentence, leading to generated sentences that lack coherence and readability. To address this issue, they propose learning a mapping from words to vectors in a continuous space, which can capture the semantic meaning of words in a more robust way.
The authors then describe their two main approaches for generating sentences from this continuous space. The first approach is based on learning a mapping from words to vectors using a Variational Autoencoder (VAE). This allows the researchers to learn a continuous representation of each word, which can be used to generate new sentences through a sequence-to-sequence model. The second approach is based on Optimus, a language model that uses a combination of VAEs and LSTMs to generate sentences directly from the continuous space.
The authors evaluate their approach on a benchmark task, where they are given a set of sentence fragments and must generate complete sentences that are semantically similar. They show that their approach achieves state-of-the-art results on this task, generating coherent and diverse sentences that are often more natural than those produced by other methods.
To further understand the capabilities of their approach, the authors conduct a series of experiments to analyze the quality of the generated sentences. They find that their approach is able to generate sentences that are both semantically similar and syntactically correct, indicating that it has successfully captured the nuances of language structure. Additionally, they show that their approach can be used to generate sentences in multiple languages, demonstrating its generalizability beyond a single language.
In conclusion, the paper presents a novel approach to sentence generation based on learning a mapping from words to vectors in a continuous space. The authors demonstrate the effectiveness of their approach through a series of experiments and show that it can generate coherent and diverse sentences that are semantically similar to those produced by other methods. This work has important implications for natural language processing, as it provides a new way of thinking about sentence generation that could lead to more accurate and efficient language models in the future.