Label-Attentive Encoder-Decoder Parser for Interpretable NLP
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Solution Overview
Problem
Existing natural language processing (NLP) models, particularly self-attention models, face challenges in providing coherent and interpretable attention weights, making it difficult to understand which parts of the input are relevant for determining specific elements of the output, especially in tasks like syntactic category assignment in sentences.
Innovation Solution
The introduction of a label attention model with attention heads corresponding to specific syntactic categories, where each head learns label-specific views of the input sentence using query, key, and value vectors, allowing for more interpretable attention information by maintaining separability of information from different heads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple self-attention heads are used to process different parts of the input, then the model's ability to capture different aspects of the input is improved, but the coherence and interpretability of attention weights deteriorates
Solution Approach 1:
The patent segments the attention mechanism by creating separate attention heads for each syntactic category (noun phrase, verb phrase, adjective phrase, adverb phrase). Each attention head independently processes the input sentence and generates attention weights specific to its category, preventing the mixing of different syntactic information that occurs in standard multi-headed self-attention. This segmentation maintains coherence within each category while still capturing diverse syntactic aspects through multiple specialized heads.
2Ease of operation
If standard self-attention mechanism is used, then the model can process the input sentence, but the interpretability of which parts of input are attended to deteriorates
Solution Approach 1:
The patent applies local quality by making each attention head specialized for a specific syntactic category. Instead of using a generic self-attention mechanism that treats all input elements uniformly, each attention head develops category-specific attention patterns and weight distributions. This specialization makes the attention weights more interpretable because the attention weights from a noun phrase attention head specifically indicate which words are relevant for noun phrase identification, rather than mixing signals from multiple syntactic categories.
Data Source
AI summary
Systems and methods for parsing natural language sentences using an artificial neural network (ANN) are described. Embodiments of the described systems and methods may generate a plurality of word representation matrices for an input sentence, wherein each of the word representation matrices is based on an input matrix of word vectors, a query vector, a matrix of key vectors, and a matrix of value vectors, and wherein a number of the word representation matrices is based on a number of syntactic categories, compress each of the plurality of word representation matrices to produce a plurality of compressed word representation matrices, concatenate the plurality of compressed word representation matrices to produce an output matrix of word vectors, and identify at least one word from the input sentence corresponding to a syntactic category based on the output matrix of word vectors.