Middle-Out Decoding (1810.11735v1)

Abstract: Despite being virtually ubiquitous, sequence-to-sequence models are challenged by their lack of diversity and inability to be externally controlled. In this paper, we speculate that a fundamental shortcoming of sequence generation models is that the decoding is done strictly from left-to-right, meaning that outputs values generated earlier have a profound effect on those generated later. To address this issue, we propose a novel middle-out decoder architecture that begins from an initial middle-word and simultaneously expands the sequence in both directions. To facilitate information flow and maintain consistent decoding, we introduce a dual self-attention mechanism that allows us to model complex dependencies between the outputs. We illustrate the performance of our model on the task of video captioning, as well as a synthetic sequence de-noising task. Our middle-out decoder achieves significant improvements on de-noising and competitive performance in the task of video captioning, while quantifiably improving the caption diversity. Furthermore, we perform a qualitative analysis that demonstrates our ability to effectively control the generation process of our decoder.

• The paper introduces a novel middle-out decoding approach that enhances sequence diversity and controllability compared to traditional left-to-right methods.
• The architecture employs dual LSTM decoders and a dual self-attention mechanism to generate sequences bidirectionally from a predicted middle word.
• Experimental results demonstrate a 75% error reduction in denoising and competitive video captioning performance, underscoring its practical impact.

An Overview of Middle-Out Decoding for Sequence Generation

The paper "Middle-Out Decoding" by Shikib Mehri and Leonid Sigal introduces a novel approach to addressing the challenges of diversity and controllability in sequence generation models. Traditional sequence-to-sequence models, which typically employ a left-to-right decoding strategy, often suffer from limitations in generating diverse outputs and lack mechanisms for external control. The authors propose a middle-out decoder architecture that generates sequences starting from an important middle word and expands simultaneously in both directions. This methodology is notably complemented by a dual self-attention mechanism designed to handle dependencies between outputs and enhance the coherence of the generated sequences.

Key Contributions and Methodology

The authors identify the fundamental constraint in traditional left-to-right decoding, which amplifies the influence of early-generated tokens on later ones, leading to reduced diversity and controllability challenges. In contrast, the middle-out decoding starts at a classifier-predicted middle word and extends the sequence to the left and right concurrently. This allows for more control over the sequence, particularly in scenarios where specific words or values need emphasis, such as focusing on a verb for video captions.

The proposed architecture consists of two LSTM-based decoders operating in opposite directions, initialized from the same middle-token. The dual self-attention mechanism plays a crucial role here, attending to both the outputs and hidden states of these dual decoders, thus maintaining information flow and coherence. Specifically, the technique allows for interaction between non-adjacent time steps in the decoders, which is crucial for modeling complex dependencies across the sequence. This dual approach stands as a generalization that can be integrated into diverse task-specific architectures requiring sequence generation.

Experimental Evaluation

The experimental validation consists of two primary tasks: a synthetic sequence de-noising task and video captioning, utilizing the MSVD dataset. For the synthetic task, the middle-out decoder demonstrated significant improvements, with a notable 75% reduction in mean squared error compared to the baseline, showcasing its superior ability to manage long dependencies.

In the field of video captioning, the middle-out architecture showed competitive performance, achieving METEOR scores comparable to state-of-the-art models when evaluated on known metrics including BLEU, ROUGE, and CIDEr-D. Notably, in scenarios where an oracle provided the middle word, the middle-out model exhibited a significant enhancement, validating its strength in controlled sequence generation.

Implications and Future Directions

The middle-out decoding approach introduced in this work has profound implications for sequence generation tasks requiring control and diversity, such as language translation, video captioning, and potentially even real-time dialog systems. The dual self-attention mechanism enhances the model's capacity to manage dependencies across sequences, a crucial aspect for sophisticated natural language applications.

Future research could explore integrating more advanced classifiers for the middle word prediction, potentially employing deep learning techniques like transformer-based models to further improve the initial word selection. Additionally, investigating the application of middle-out decoding in other domains or adapting the architecture for different network structures such as transformers might yield further advancements in controllability and diversity.

In conclusion, this paper's introduction of middle-out decoding offers a promising new direction for enhancing the flexibility and expressiveness of sequence generation models, marking a significant step towards more adaptive and controllable AI systems.