Hybrid Direct and Noisy-Channel Scoring for Sequence Transduction
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Solution Overview
Problem
Conventional sequence transduction systems face challenges in accurately mapping input sequences to target sequences due to 'explaining away' effects and reliance on limited paired training data, often ignoring input data in favor of high-probability output sequences, especially in domains with abundant unpaired target sequences.
Innovation Solution
The sequence transduction system employs a combination of direct and noisy-channel models to score hypotheses, using a direct model for probability calculations and a noisy-channel model to mitigate 'explaining away' effects, and leverages both paired and unpaired data for training, incorporating a source model to ensure the output is well-formed and meaningful.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a direct scoring model is used to score hypotheses based on probability of target prefix given input prefix, then the model can leverage abundant unpaired target sequences for training, but the model suffers from 'explaining away' effects where it ignores input data in favor of high-probability output sequences
Solution Approach 1:
The patent combines a direct scoring model with a noisy-channel scoring model into a hybrid system. The direct model leverages unpaired target sequences for training while the noisy-channel model mitigates explaining away effects by scoring based on probability of input prefix given target prefix. The two models work together to achieve both adaptability to unpaired data and scoring accuracy.
Solution Approach 2:
The noisy-channel model acts as an intermediary that corrects the deficiencies of the direct model. It introduces a reverse probability calculation (input given target) that serves as a counterbalance to the direct forward probability (target given input), thereby mediating the explaining away problem while preserving the ability to use unpaired data.
2Reliability
If conventional sequence transduction systems rely on paired training data only, then training data quality is high, but the system performance degrades in domains with abundant unpaired target sequences
Solution Approach 1:
The patent creates a universal training framework that can handle both paired and unpaired target sequences. The direct scoring model is designed to be multi-functional, capable of learning from paired data (with input-target alignments) and unpaired data (with only target sequences), thereby improving productivity in domains where unpaired data is abundant while maintaining reliability from paired data.
3Measurement precision
If the system extends the set of current hypotheses at each iteration, then the search space increases to find better target sequences, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by maintaining a limited set of current hypotheses rather than exploring all possible hypotheses. At each iteration, the system extends only the current hypotheses in the maintained set, scoring them with both direct and noisy-channel models, and selects the best ones to continue. This partial exploration balances hypothesis scoring accuracy with computational complexity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a target sequence from an input sequence. In one aspect, a method comprises maintaining a set of current hypotheses, wherein each current hypothesis comprises an input prefix and an output prefix. For each possible combination of input and output prefix length, the method extends any current hypothesis that could reach the possible combination to generate respective extended hypotheses for each such current hypothesis; determines a respective direct score for each extended hypothesis using a direct model; determines a first number of highest-scoring hypotheses according to the direct scores; rescores the first number of highest-scoring hypotheses using a noisy channel model to generate a reduced number of hypotheses; and adds the reduced number of hypotheses to the set of current hypotheses.