A Fast and Simple Algorithm for Training Neural Probabilistic Language Models (1206.6426v1)

Abstract: In spite of their superior performance, neural probabilistic LLMs (NPLMs) remain far less widely used than n-gram models due to their notoriously long training times, which are measured in weeks even for moderately-sized datasets. Training NPLMs is computationally expensive because they are explicitly normalized, which leads to having to consider all words in the vocabulary when computing the log-likelihood gradients. We propose a fast and simple algorithm for training NPLMs based on noise-contrastive estimation, a newly introduced procedure for estimating unnormalized continuous distributions. We investigate the behaviour of the algorithm on the Penn Treebank corpus and show that it reduces the training times by more than an order of magnitude without affecting the quality of the resulting models. The algorithm is also more efficient and much more stable than importance sampling because it requires far fewer noise samples to perform well. We demonstrate the scalability of the proposed approach by training several neural LLMs on a 47M-word corpus with a 80K-word vocabulary, obtaining state-of-the-art results on the Microsoft Research Sentence Completion Challenge dataset.

• The paper introduces a novel noise-contrastive estimation method that reduces training times by over an order of magnitude.
• It demonstrates that using 25 noise samples achieves comparable performance to traditional maximum likelihood approaches.
• The approach enables efficient training on large datasets, paving the way for practical deployment of neural probabilistic language models.

Overview of "A Fast and Simple Algorithm for Training Neural Probabilistic LLMs"

This research paper addresses the persistent challenge of training neural probabilistic LLMs (NPLMs) effectively, by proposing a novel algorithm leveraging noise-contrastive estimation (NCE). While NPLMs have demonstrated superior accuracy over traditional n-gram models, their practical use is often hindered by extensive training durations, spanning several weeks for moderate datasets. This inefficiency stems from the necessity of considering every word in a vocabulary when computing log-likelihood gradients, leading to computational complexity proportional to the product of vocabulary size and word feature dimensionality.

Methodology

The authors introduce a streamlined training algorithm utilizing NCE, a method suitable for estimating unnormalized continuous distributions. Unlike conventional maximum likelihood (ML) approaches, which involve costly gradient computations, this new method employs sampling techniques that are significantly more efficient and stable. Contrary to importance sampling, NCE does not require dynamic adaptations of sample sizes or proposal distributions to maintain learning stability. Experiments were carried out using the log-bilinear (LBL) model, a simpler variant of NPLM known for outperforming n-grams, though typically trailing behind more sophisticated neural models.

Experimental Evaluation

The proposed algorithm was evaluated on the Penn Treebank corpus, demonstrating substantial reductions in training time by over an order of magnitude without compromising model quality. Specifically, models trained using NCE with 25 noise samples achieved similar performance to those trained with traditional ML methods but were significantly faster. The empirical results show an exponential decrease in model testing perplexity with increasing noise samples, confirming NCE’s effectiveness.

Furthermore, the algorithm was benchmarked on the Microsoft Research Sentence Completion Challenge, utilizing a 47-million-word corpus with an 80,000-word vocabulary. The LBL models employing the NCE algorithm outperformed previously established models by offering enhanced accuracy on this dataset. Intriguingly, models with substantial context sizes and feature dimensions excelled in filling sentence gaps accurately, achieving an accuracy rate of 54.7%, surpassing prior models like LSA.

Implications and Future Work

The introduction of NCE for the training of NPLMs marks a significant improvement in training efficiency while retaining model performance quality. This advancement opens practical applications of NPLMs in large-scale language tasks, which were previously inaccessible due to resource constraints.

The paper suggests potential explorations into context-dependent noise distributions could further streamline training processes. Additionally, the broader applicability of NCE to other probabilistic classifiers, especially those with sizable class counts, presents intriguing opportunities. There is a potential for exploring other estimators within the family that includes NCE to further optimize training processes for neural LLMs.

In conclusion, this work presents a valuable contribution to the domain of computational linguistics, providing a practical approach to enhance the feasibility of deploying advanced LLMs across varied applications in AI and natural language processing.