CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling (1811.10996v1)

Abstract: In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements. Existing language generation techniques are usually based on recurrent neural networks (RNNs). However, it is non-trivial to impose constraints on RNNs while maintaining generation quality, since RNNs generate sentences sequentially (or with beam search) from the first word to the last. In this paper, we propose CGMH, a novel approach using Metropolis-Hastings sampling for constrained sentence generation. CGMH allows complicated constraints such as the occurrence of multiple keywords in the target sentences, which cannot be handled in traditional RNN-based approaches. Moreover, CGMH works in the inference stage, and does not require parallel corpora for training. We evaluate our method on a variety of tasks, including keywords-to-sentence generation, unsupervised sentence paraphrasing, and unsupervised sentence error correction. CGMH achieves high performance compared with previous supervised methods for sentence generation. Our code is released at https://github.com/NingMiao/CGMH

• The paper presents the innovative integration of Metropolis-Hastings sampling into sentence generation to enforce complex constraints efficiently.
• It demonstrates unsupervised applications in paraphrase generation, keyword-to-sentence formulation, and sentence error correction with competitive performance.
• Results indicate that CGMH maintains high fluency and naturalness while eliminating the need for parallel corpora, paving the way for flexible NLP models.

Insights into CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling

The paper "CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling" presents a novel approach to the challenge of constrained natural language generation, which is a critical component of several real-world applications including paraphrase generation, sentence error correction, and keywords-to-sentence production. The authors, Ning Miao, Hao Zhou, Lili Mou, Rui Yan, and Lei Li, explore the limitations in existing recurrent neural network (RNN)-based generation techniques that struggle to incorporate constraints efficiently while maintaining quality.

One of the main contributions of this research is the application of the Metropolis-Hastings (MH) algorithm, a Markov chain Monte Carlo (MCMC) method, into the field of sentence generation. This method allows for the sampling of sentences directly from the sentence space while incorporating complex constraints during inference. This stands in contrast to earlier methods that either altered sentence structure solely in a supervised manner requiring parallel corpora or had particular constraints that were difficult to manage effectively.

The authors illustrate the versatility of the CGMH method by implementing it in various tasks:

• Keywords-to-Sentence Generation: The paper demonstrates how CGMH can outperform state-of-the-art models in generating sentences from keywords, as evidenced by lower negative log-likelihood (NLL) scores and improved human evaluation metrics.
• Unsupervised Paraphrase Generation: Here, the proposed methodology does not rely on parallel corpora, thus providing a more accessible model for paraphrase tasks. The authors leverage LLMs and semantic similarity measures to ensure that constrained sentences retain the desired properties.
• Unsupervised Sentence Error Correction: CGMH achieves competitive performance with models trained on extensive parallel data, highlighting its potential even without labeled training datasets.

The research findings strongly indicate the efficacy of CGMH in generating linguistically meaningful and constraint-respecting sentences without requiring parallel data. The numerical results shared in the paper highlight CGMH's ability to adhere to constraints while maintaining high fluency and naturalness of generated sentences, essentially bridging a gap towards more flexible and potentially unsupervised natural language processing models.

This paper makes a valuable contribution theoretically and practically by offering an unsupervised alternative to existing methodologies. Future developments in this field may focus on optimizing the acceptance rates of proposals within CGMH and adjusting the setup for even broader applications. Additionally, one could consider integrating CGMH with LLMs to refine its performance further and explore other domains of constrained generation, thereby expanding its scope of application significantly. The entire approach and its implications signify a promising horizon for constraint-based language generation tasks, potentially reshaping the methodologies of unsupervised learning in natural language processing.