Neural Word Alignment for Formatting Tag Projection
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Solution Overview
Problem
Existing neural machine translation systems struggle with accurately transferring text annotations, such as formatting and semantic tags, from source to target languages due to limitations in word alignment techniques, particularly the Transformer architecture's difficulty in interpreting attention activations as word alignments.
Innovation Solution
A computer-implemented word alignment method that incorporates an auxiliary loss function to encourage contiguous attention matrices and replaces heuristic symmetrization with activation optimization. This method trains two alignment models that translate in opposite directions, inferring a symmetrized attention matrix that jointly optimizes the likelihood of correct output words under both models in both languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical word alignment methods (GIZA++, FastAlign) are used, then word alignment can be inferred, but the methods make assumptions of probabilistic independence that prevent them from fully modeling the context-sensitive nature of human language and produce biased contiguous alignments
Solution Approach 1:
The patent replaces statistical alignment methods (GIZA++, FastAlign) with a neural network-based attention mechanism. The neural model uses soft attention to dynamically weigh the importance of different source words when generating each target word, allowing the system to capture context-sensitive relationships without relying on probabilistic independence assumptions or hardcoded contiguity biases.
Solution Approach 2:
The patent introduces dynamic attention weights that adapt based on the specific context of each word pair. Instead of using static probabilistic models or fixed contiguity assumptions, the attention mechanism dynamically adjusts the alignment probabilities during translation, allowing the system to flexibly model context-sensitive relationships between source and target words.
2Measurement precision
If Transformer architecture is used for neural machine translation, then translation quality is improved, but interpreting attention activations as word alignments becomes difficult
Solution Approach 1:
The patent performs alignment inference as a preliminary step before final translation output. By using the attention weights from the trained Transformer model to generate alignment pairs, and then using these alignments to guide tag projection, the system makes the alignment information explicitly available for downstream tasks without requiring changes to the core translation architecture.
Solution Approach 2:
The patent introduces an intermediate alignment layer that sits between the Transformer's attention mechanism and the tag projection task. This intermediate layer uses the attention weights to generate soft alignments, which are then converted to hard alignments for tag transfer. This intermediary step makes the implicit attention activations explicitly interpretable as word alignments.
3Quantity of substance
If heuristic symmetrization methods are used to combine forward and backward alignments, then alignment completeness is improved, but alignment accuracy deteriorates due to propagation of IBM model errors
Solution Approach 1:
The patent inverts the traditional approach by training the neural model in both forward and backward directions, then using bidirectional attention optimization to jointly optimize the alignments. Instead of taking alignments from one direction and symmetrizing them with heuristics, the system optimizes both directions simultaneously, allowing errors to cancel out and improving overall alignment accuracy.
Solution Approach 2:
The patent implements bidirectional feedback loops where the forward and backward models inform each other's alignment decisions. The attention weights from both directions are used to compute a combined alignment that leverages information from both translation directions, creating a feedback mechanism that improves accuracy by cross-validating alignment decisions.
Data Source
AI summary
In an embodiment, the disclosure provides a programmed computer system implemented via client-server Software as a Service (SaaS) techniques that allows for machine translation of digital content. When translating digital content, linguists must translate more than just the text on the page. Formatting, for example, is a commonly used and important aspect of online content that is typically managed with tags, such as for bold and for italics. When linguists work, they must ensure these tags are placed accurately as part of the translation. Projecting tags accurately depends on successfully accomplishing the challenging task of word alignment. Unfortunately, if word alignment is inaccurate, it makes placing formatting tags very difficult. In an embodiment, the present disclosure provides a method of not only translating text, but also efficiently and accurately projecting tags from input text in one language to output text in another language.