Insertion Transformer: Flexible Sequence Generation via Insertion Operations (1902.03249v1)

Abstract: We present the Insertion Transformer, an iterative, partially autoregressive model for sequence generation based on insertion operations. Unlike typical autoregressive models which rely on a fixed, often left-to-right ordering of the output, our approach accommodates arbitrary orderings by allowing for tokens to be inserted anywhere in the sequence during decoding. This flexibility confers a number of advantages: for instance, not only can our model be trained to follow specific orderings such as left-to-right generation or a binary tree traversal, but it can also be trained to maximize entropy over all valid insertions for robustness. In addition, our model seamlessly accommodates both fully autoregressive generation (one insertion at a time) and partially autoregressive generation (simultaneous insertions at multiple locations). We validate our approach by analyzing its performance on the WMT 2014 English-German machine translation task under various settings for training and decoding. We find that the Insertion Transformer outperforms many prior non-autoregressive approaches to translation at comparable or better levels of parallelism, and successfully recovers the performance of the original Transformer while requiring only logarithmically many iterations during decoding.

An Analysis of the Insertion Transformer for Sequence Generation

The Insertion Transformer presents a novel approach to sequence generation utilizing insertion operations for flexibility in ordering sequences. Traditional autoregressive models typically follow a fixed left-to-right generation process, but the proposed model allows insertions at arbitrary positions, enabling a more flexible sequence creation process. This model contributes to sequence generation by providing both fully and partially autoregressive capabilities, efficiently bridging the gap between traditional autoregressive and non-autoregressive models.

Model Framework

The Insertion Transformer diverges from the standard left-to-right sequence models by introducing a generative process that iteratively builds the sequence using insertions. A sequence is generated by transforming an empty canvas through iterative insertions until a termination condition is satisfied. This approach accommodates both serial and parallel decoding strategies, enhancing the model's adaptability and efficiency in generating sequences, particularly those of varying complexity.

Technical Implementation

The model modifies the Transformer architecture by adjusting the decoder to produce distributions over possible insertions, rather than predicting the subsequent token in a fixed order. This approach involves generating slot representations via concatenation and conditioning insertions on both content and location within the current hypothesis sequence. The inclusion of an unmasked self-attention mechanism allows the model to consider the entire context at each generation step. The implementation supports different configurations, including direct joint or factorized content-location distributions, enhancing the model's capacity for varied generation tasks.

Training Strategy

A core aspect of the Insertion Transformer is its flexible training strategy, which accommodates left-to-right, binary tree, and uniform losses, each supporting distinct sequence generation orders. The binary tree ordering, particularly, enables logarithmic complexity in generating sequences by focusing on parallelism, aligning with balanced binary trees. This allows tokens to be inserted simultaneously across slots, significantly reducing the total number of generation steps without sacrificing model performance.

Empirical Findings

The Insertion Transformer has demonstrated strong empirical results on tasks such as the WMT 2014 English-German translation benchmark. Compared to existing non-autoregressive models, the Insertion Transformer achieves comparable or superior performance while maintaining high levels of parallelism and requiring logarithmically fewer iterations. With knowledge distillation, the model's performance further improves, indicating the method's robustness in leveraging teacher models to refine output quality.

Implications and Future Directions

The approach suggested by the Insertion Transformer implies significant practical improvements for sequence generation in machine learning, particularly in tasks requiring efficiency and flexibility in structure and ordering. The partially autoregressive framework may present new paradigms for machine learning applications beyond language translation, such as structured prediction tasks where natural order is less relevant or where parallel token generation could provide substantial overhead reductions.

Looking forward, the field may explore further how insertion-based frameworks perform across diverse datasets, especially those requiring completion or infilling capabilities. Moreover, innovations in model architecture to mitigate the computational overhead of recomputing states might enhance the framework's scalability. Future studies might delve into optimizing insertion operations to handle more complex structured outputs efficiently.

In summary, the Insertion Transformer offers a fresh perspective for enhancing sequence generation tasks, marrying the benefits of autoregressive precision and non-autoregressive efficiency. The flexibility and applicability of this approach hold promise for expanding the capabilities of machine learning systems in addressing increasingly sophisticated tasks.