Cross-lingual Retrieval via Event Representation
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Solution Overview
Problem
Current techniques for cross-lingual information retrieval and information extraction face challenges in effectively handling queries in one language while documents relevant to the query are in another language, often relying on imperfect machine translation or data sparsity, which limits the accuracy and efficiency of retrieving and extracting relevant information across languages.
Innovation Solution
A cross-lingual information retrieval module is trained using a dataset of annotated documents in one language and their translations into another, allowing for the generation of a document-event map that associates new documents in the second language with events, leveraging neural networks to improve the ranking and retrieval of relevant documents without relying on one-best translations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine translation is used to translate queries and documents into a common language, then cross-lingual information retrieval becomes possible, but translation errors and data sparsity reduce retrieval accuracy
Solution Approach 1:
The patent introduces an event representation as an intermediary that mediates between queries in one language and documents in another language. Instead of directly translating between languages, the system maps both queries and documents to language-independent event representations, which capture the semantic meaning of events occurring in the text. This intermediary representation avoids the errors inherent in direct machine translation while enabling cross-lingual retrieval.
Solution Approach 2:
The patent segments the retrieval process into distinct components: query processing, document processing, and ranking. Each component handles specific aspects of the cross-lingual retrieval task independently. The query is processed to identify event representations, documents are processed to extract event representations, and then a ranking component scores the relevance of documents to queries based on their event representations. This segmentation allows each component to be optimized independently and handles language differences at appropriate stages.
2Measurement precision
If annotated training data in multiple languages is collected to improve retrieval accuracy, then cross-lingual performance improves, but data sparsity and annotation costs increase
Solution Approach 1:
The patent performs preliminary processing of training data by creating event representations from annotated documents in source languages before training the retrieval model. The system pre-processes the training corpus to extract event representations and their associations with documents, creating a structured representation that captures the essential semantic information. This preliminary action reduces the complexity of the training data and enables the model to learn from fewer, more informative examples.
Solution Approach 2:
The patent changes the parameter space by transforming the retrieval task from operating directly on text in multiple languages to operating on event representations. By changing the representation parameters from language-specific text to language-independent event structures, the system can generalize across languages with fewer training examples. The event representation parameters capture cross-lingual semantic similarities that reduce the need for large amounts of annotated data in each language.
3Adaptability or versatility
If neural networks are trained on translated annotated documents, then the model learns cross-lingual patterns, but training complexity and computational resources increase
Solution Approach 1:
The patent extracts event representations from annotated documents as a separate, reusable component before training the neural network. Instead of training the model on raw translated text, the system extracts structured event representations that capture the essential semantic information. This extraction separates the language-independent event modeling from the language-specific translation, reducing training complexity while preserving cross-lingual capabilities.
Data Source
AI summary
A machine accesses a query in a first natural language. The machine identifies an event corresponding to the query. The machine computes, using a cross-lingual information retrieval module, a ranked list of documents in a second natural language that are related to the event. At least a portion of documents in the ranked list are selected from a collection of documents in the second natural language that are not annotated with events. The cross-lingual information retrieval module is trained using a dataset comprising annotated documents in the first natural language and translations of the annotated documents into the second natural language. Each annotated document is annotated with one or more events. The machine provides an output representing at least a portion of the ranked list of documents in the second natural language. The second natural language is different from the first natural language.