Dynamic Evaluation of Neural Sequence Models (1709.07432v2)

Abstract: We present methodology for using dynamic evaluation to improve neural sequence models. Models are adapted to recent history via a gradient descent based mechanism, causing them to assign higher probabilities to re-occurring sequential patterns. Dynamic evaluation outperforms existing adaptation approaches in our comparisons. Dynamic evaluation improves the state-of-the-art word-level perplexities on the Penn Treebank and WikiText-2 datasets to 51.1 and 44.3 respectively, and the state-of-the-art character-level cross-entropies on the text8 and Hutter Prize datasets to 1.19 bits/char and 1.08 bits/char respectively.

• The paper introduces dynamic evaluation to update model parameters during evaluation using gradient descent.
• It proposes an enhanced update rule with RMSprop and sparse adaptation for reduced computational overhead.
• Experiments show state-of-the-art improvements in word-level perplexity and character-level cross-entropy.

Dynamic Evaluation of Neural Sequence Models: A Summary

The paper "Dynamic Evaluation of Neural Sequence Models" by Ben Krause, Emmanuel Kahembwe, Iain Murray, and Steve Renals introduces a methodology that enhances the adaptability of neural sequence models by employing dynamic evaluation. This technique improves the models' performance by allowing them to adjust to recent history through a gradient descent based mechanism, enabling higher probabilities to be assigned to recurring patterns within a sequence. This concept has shown not only superior performance compared to existing adaptation approaches but also achieves state-of-the-art results in word-level perplexities and character-level cross-entropies on standard datasets.

Technical Contributions and Findings

The cornerstone of this paper is the introduction of dynamic evaluation—a methodology where models continuously adapt to parts of a sequence during evaluation by updating their parameters using gradient descent. This adaptation results in localized model parameters that better approximate the prevailing sequence distribution, providing a substantial advantage during evaluation. The paper makes several key methodological improvements to dynamic evaluation, especially over its traditional counterpart, such as:

1. Improved Update Rule: The authors propose an update rule based on RMSprop with a global decay prior and segment-based backpropagation. This update rule optimizes the model's adaptability by using averaged squared gradients gathered during training rather than solely on recent test data. Results show this approach yields significant performance improvements over the traditional stochastic gradient descent (SGD) updates.
2. Sparse Dynamic Evaluation: To reduce computational overhead, the authors introduce a sparse dynamic evaluation technique that only updates a subset of parameters. By introducing an adaptation matrix to transform a limited selection of hidden units, this method dramatically reduces the number of adaptation parameters and makes the methodology feasible for larger sequence modelling tasks.
3. Time-Scale Analysis: The performance advantage of dynamic evaluation manifests after processing several hundred characters, which persists and often increases with longer sequences. This feature underscores the technique's ability to manage long-term dependencies and adjust effectively to shifts in the data distribution.

Experimental Validation

The practical benefits of dynamic evaluation are validated across several LLMling tasks, achieving unprecedented results:

• Word-Level LLMling: On datasets like Penn Treebank (PTB) and WikiText-2, dynamic evaluation significantly reduces perplexities compared to baseline models and even surpasses sophisticated techniques like neural caching. For instance, using dynamic evaluation on the AWD-LSTM model improves test set perplexity on PTB from 57.7 to 51.1.
• Character-Level LLMling: Dynamic evaluation also yields substantial gains in character-level modelling for datasets like text8 and Hutter Prize, improving baseline cross-entropy from 1.24 to 1.08 bits/char in the latter.

Implications and Future Directions

The results highlight the potential of dynamic evaluation in various applications, such as machine translation and speech recognition, where understanding and adapting to context over long durations is crucial. The approach can substantially improve models' consistency by allowing them to intuitively adjust weights based on recent data patterns.

Avenues for future research include the extension of dynamic evaluation to other architectures and tasks, optimization of computation costs further, and integration with other real-time adaptive approaches. Additionally, the exploration of entirely different update rules or alternate forms of dynamic evaluation could yield even greater adaptability and efficiency for neural sequence models.

This paper contributes significantly to the field of sequence modelling by providing a robust and flexible framework for model adaptation during evaluation, marking a notable advancement in the methodology of neural network models for natural language processing and related tasks.