Semi-Autoregressive Text Editing for Low-Latency Generation
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Solution Overview
Problem
Existing sequence-to-sequence models for text-to-text transduction are inefficient in monolingual settings with high input-output overlap, leading to high latency and reduced flexibility in generating target texts.
Innovation Solution
A semi-autoregressive text-editing model (EdiT5) decomposes text generation into tagging and insertion tasks, using a non-autoregressive encoder for token selection and reordering, and an autoregressive decoder for inserting missing tokens, leveraging T5's span in-filling pre-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If seq2seq models generate target texts completely from scratch, then the model can handle any text-to-text transduction task, but inference latency becomes unnecessarily high and computational resources are wasted
Solution Approach 1:
The patent segments the text generation process into two distinct phases: (1) a non-autoregressive encoder phase that processes the entire source text and generates intermediate representations in parallel, and (2) an autoregressive decoder phase that generates only the necessary output tokens. This segmentation allows the model to leverage parallel processing for the bulk of the work while maintaining the ability to handle diverse tasks.
Solution Approach 2:
The patent applies partial action by having the encoder generate intermediate representations for all source tokens, but only requiring the decoder to generate the minimal necessary output tokens. This avoids the excessive action of generating complete target texts from scratch, reducing computational waste while maintaining task versatility.
2Productivity
If a limited set of pre-defined edit operations is used, then sample efficiency increases and search space is reduced, but the model's flexibility to reconstruct arbitrary output texts is limited
Solution Approach 1:
The patent makes the edit operations dynamic by allowing the model to learn and apply arbitrary edit operations during training, rather than being constrained to a fixed set of pre-defined operations. The encoder dynamically determines which tokens to keep, delete, or modify based on the specific input-output pair, enabling both sample efficiency and high flexibility.
Solution Approach 2:
The patent changes the parameters of the edit operations by using learned attention mechanisms and soft masking to control token selection and transformation. Instead of hard-coded operation rules, the model learns continuous parameters that guide the editing process, allowing it to adapt to different task requirements while maintaining efficiency.
3Adaptability or versatility
If standard seq2seq models are used for text editing, then the framework is generic and applicable to any text-to-text task, but the quality of output texts suffers due to wasted computation on overlapping content
Solution Approach 1:
The patent extracts and preserves the overlapping source tokens that would otherwise be redundantly regenerated by standard seq2seq models. The encoder identifies tokens that appear in both source and target texts and extracts them directly into the output, eliminating wasted computation and improving output quality while maintaining framework generality.
Solution Approach 2:
The patent performs preliminary action by having the encoder pre-process the entire source text and identify which tokens should be preserved before the decoding phase begins. This preliminary identification of overlapping content prevents redundant generation and improves output quality from the start, while the overall framework remains generic and applicable to various text-to-text tasks.
Data Source
AI summary
Provided are improved machine learning-based text editing models. Specifically, example implementations include a flexible semi-auto-regressive text-editing approach for generation, designed to derive the maximum benefit from non-auto-regressive text-editing and autoregressive decoding. In contrast to conventional sequence-to-sequence (seq2seq) models, the proposed approach is fast at inference time, while being capable of modeling flexible input-output transformations.