Entity Salience Embeddings for Short Text Response Ranking
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Solution Overview
Problem
Existing speech recognition systems struggle to accurately predict entity salience in extremely short documents, such as responses from virtual assistants, where traditional signals like position and frequency are less effective.
Innovation Solution
A bi-encoder entity salience model is employed to generate context and entity embeddings, using a similarity component to determine salience scores, and combined with techniques like pre-trained language models and semi-supervised training to improve performance, especially in domains with limited labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signals (position and frequency) are used to predict entity salience, then the system is simple and fast, but the prediction accuracy deteriorates in extremely short documents
Solution Approach 1:
The patent transforms the entity salience prediction problem from using traditional positional and frequency parameters to using semantic embedding parameters. The bi-encoder model encodes entities and documents into vector embeddings, then computes similarity between them to determine salience. This parameter transformation enables accurate prediction in short documents where traditional signals fail.
Solution Approach 2:
The patent replaces the mechanical/counting-based approach (tracking entity positions and frequencies) with a semantic representation approach using neural network embeddings. Instead of mechanically counting entity occurrences or tracking positions, the system uses learned vector representations that capture semantic meaning, enabling more accurate salience prediction in contexts where traditional mechanical signals are insufficient.
2Measurement precision
If more labeled data is collected to train the model, then the prediction accuracy improves, but the time and resources required for data collection and labeling increase
Solution Approach 1:
The patent applies preliminary action by using pre-trained language models (such as BERT) that have already been trained on large corpora. These pre-trained models capture general linguistic knowledge and can be fine-tuned on small domain-specific datasets. This preliminary training phase eliminates the need to collect and label large amounts of data from scratch, significantly reducing data collection and labeling time while maintaining high prediction accuracy.
Data Source
AI summary
Systems and methods are described for predicting entity salience in segments of text, including short segments of text, where salience may refer to the importance or centrality of the entity to the content of the text. The system may use an entity salience model that can encode the context (e.g., the segment of text) into a passage embedding and an entity into an entity embedding. In some cases, the entity embedding may be determined using the entity as well as a natural language description of the entity from, for example, a knowledge graph or other resource. The entity salience model may calculate an entity score for each entity represented in the text based on, for example, a similarity between that entity's entity embedding and the passage embedding. The entity salience score maybe used to, for example, identify additional content with which to augment a response to a user input.