Recurrent Conditional Random Fields for Sequence Labeling
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Solution Overview
Problem
Existing language understanding systems face challenges in accurately assigning semantic labels to words in sequences, as traditional conditional random fields (CRFs) require seeing the entire sequence before labeling, while recurrent neural networks (RNNs) suffer from label bias and lack sequence-level discrimination.
Innovation Solution
The implementation of a recurrent conditional random field (R-CRF) that combines RNNs and CRFs, where the RNN generates activation layer data used by the CRF to assign semantic labels, allowing for online labeling and incorporating sequence-level objective functions and backpropagation for weight adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional conditional random fields (CRFs) are used to assign semantic labels, then sequence-level discrimination is improved, but processing speed deteriorates because the entire sequence must be seen before labeling
Solution Approach 1:
The patent segments the labeling process into individual word-level operations using an RNN tagger that processes words sequentially as they are seen, rather than requiring the entire sequence. Each word is labeled independently based on local context and previous labels, enabling online processing while maintaining sequence-level discrimination through the recurrent structure that incorporates label dependencies.
2Productivity
If recurrent neural networks (RNNs) are used to assign semantic labels, then processing speed is improved by enabling online labeling, but label bias increases and sequence-level discrimination is lost
Solution Approach 1:
The patent implements feedback mechanisms where the RNN tagger incorporates previous label assignments into the current labeling decision through its recurrent structure. The label bias is corrected by using the recurrent connections to propagate sequence-level constraints, allowing each word to be labeled online while still considering the overall sequence context through feedback from previously assigned labels.
3Measurement precision
If a combination of RNN and CRF is implemented, then both feature learning and sequence-level discrimination are improved, but system complexity increases
Solution Approach 1:
The patent merges the RNN's feature learning capability with the CRF's sequence-level discrimination by integrating them into a unified R-CRF architecture. The RNN portion extracts features from input words and previous labels, while the CRF portion models label dependencies across the sequence. This combination is implemented as a single recurrent structure where the CRF transition matrices are learned through backpropagation, avoiding the need for separate RNN and CRF components and reducing overall system complexity.
Data Source
AI summary
Recurrent conditional random field (R-CRF) embodiments are described. In one embodiment, the R-CFR receives feature values corresponding to a sequence of words. Semantic labels for words in the sequence of words are then generated and each label is assigned to the appropriate one of the words in the sequence of words. The R-CRF used to accomplish these tasks includes a recurrent neural network (RNN) portion and a conditional random field (CRF) portion. The RNN portion receives feature values associated with a word in the sequence of words and outputs RNN activation layer activations data that is indicative of a semantic label. The CRF portion inputs the RNN activation layer activations data output from the RNN for one or more words in the sequence of words and outputs label data that is indicative of a separate semantic label that is to be assigned to each of the words.