Natural Language Query Completion Using Corpus-Indexed Language Models
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Solution Overview
Problem
Traditional autocomplete systems for natural language questions rely on memorized queries and fail to ensure that suggested questions will retrieve search results, leading to frustrated users as they often suggest queries without available documents, violating the implied promise of search functionality.
Innovation Solution
The system employs multiple indexes, including a question term index and short text indexes derived from a corpus of documents, to generate question completion suggestions by selecting relevant indexes based on the user's input and using language models to provide real-time suggestions that are likely to yield search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional autocomplete systems use memorized queries and trie data structures, then query completion speed is improved, but the ability to suggest relevant questions that retrieve search results deteriorates
Solution Approach 1:
The system segments the autocomplete functionality into two distinct components: a trie-based fast completion system for speed and a language model-based relevance system for accuracy. The trie data structure handles character-by-character matching and provides immediate suggestions, while the language model independently evaluates whether those suggestions will actually retrieve relevant search results. This segmentation allows each component to optimize for its specific function without compromising the other.
Solution Approach 2:
The language model acts as an intermediary between the trie-based autocomplete system and the search results. It receives candidate completions from the trie structure and filters/ranks them based on predicted relevance to actual search results. This intermediary layer ensures that only high-probability relevant suggestions are presented to users, bridging the gap between fast completion and reliable relevance.
2Measurement precision
If autocomplete systems rely on memorized queries from past transactions, then completion accuracy is improved, but adaptability to new or unseen query patterns deteriorates
Solution Approach 1:
The system performs preliminary action by pre-training the language model on extensive corpora of natural language questions and search results before deployment. This pre-training equips the model with general knowledge about question structures, terminology, and relationships between queries and results. When encountering new or unseen query patterns, the model can leverage this pre-acquired knowledge to make informed predictions rather than relying solely on memorized examples.
Solution Approach 2:
The system changes parameters by transitioning from exact string matching in traditional autocomplete to probabilistic predictions based on language models. Instead of requiring precise matches against stored queries, the language model evaluates likelihoods based on learned patterns, allowing it to adapt to new query formulations while maintaining accuracy. This parameter change enables the system to handle variations in wording, grammar, and expression that memorized systems cannot accommodate.
3Ease of operation
If the system provides comprehensive question suggestions, then user convenience is improved, but the likelihood of suggesting queries without available documents increases
Solution Approach 1:
The system implements feedback by using actual search result data to train and evaluate the language model. The model learns from the relationship between questions and their corresponding search results, receiving feedback on which question patterns lead to successful retrievals. This feedback loop ensures that suggested completions are not just linguistically plausible but actually connected to available documents, maintaining reliability while providing comprehensive suggestions.
Data Source
AI summary
Representative embodiments disclose mechanisms to complete partial natural language questions. Users enter a partial question. The system comprises a plurality of indexes, one index comprising common phrases associated with natural language questions and other indexes comprising short text entries associated with documents, such as document titles. The partial question is used to search one or more of the indexes. The search yields a ranked list of results. The top k entries of the list are selected and one or more language models are created from the top k entries. Each language model comprises n-grams from the top k entries from an index and an associated probability for each n-gram. A question completion generator creates question completion suggestions by matching n-grams with the partial question, removing ungrammatical candidate suggestions, and filtering the remaining suggestions per a filtering criteria. The top N results are returned as suggestions to complete the question.