Question Disambiguation via Round-Trip Prediction
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Solution Overview
Problem
Open-domain Question Answering systems face challenges in handling ambiguous questions due to users' lack of knowledge about relevant topics, leading to difficulties in finding correct answers across a large collection of diverse passages.
Innovation Solution
A model architecture that aggregates and combines evidence from multiple passages to predict question-answer pairs, utilizing a novel round-trip prediction approach to find additional interpretations and filter out incorrect pairs, thereby disambiguating the output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large collection of diverse passages is searched to answer open-domain questions, then the system can potentially find correct answers, but the complexity of processing and the time required increase significantly
Solution Approach 1:
The patent segments the question answering process into distinct modules: an ambiguity detection module that identifies ambiguous questions, a passage retrieval module that fetches relevant passages, and an answer generation module that produces answers. This segmentation allows each module to specialize in specific tasks, improving overall system efficiency and reliability while managing complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary ambiguity detection module that acts as a mediator between the user's ambiguous question and the passage retrieval system. This intermediary component analyzes the question, identifies ambiguity, and guides the subsequent passage retrieval and answer generation processes, thereby improving answer accuracy without proportionally increasing system complexity.
2Adaptability or versatility
If users formulate questions without knowledge of relevant topics, then the system must handle highly ambiguous questions, but this leads to difficulty in finding correct answers
Solution Approach 1:
The patent applies preliminary action by detecting and analyzing question ambiguity before proceeding to passage retrieval and answer generation. The ambiguity detection module performs preliminary analysis of the question structure and semantics, identifying potential ambiguity issues early in the process, which allows the system to adjust its subsequent processing accordingly and improve interpretation accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where the system analyzes the detected ambiguity and adjusts its passage retrieval and answer generation strategies accordingly. The ambiguity detection results feed back into the subsequent processing stages, allowing the system to refine its interpretation and improve answer precision for ambiguous questions.
Data Source
AI summary
Techniques for predicting an answer to a question using a machine learning model are described. In some examples, the model predicts one or more answers to the question by: predicting at least two answers to the question using a first component of the question-answer model from a set of passages, generating, using a second component of the question-answer model, at least one question for each of the predicted at least two answers, and performing roundtrip predictions until each generated question only has one answer.