Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization (1804.07097v2)

Abstract: Traditional information retrieval (such as that offered by web search engines) impedes users with information overload from extensive result pages and the need to manually locate the desired information therein. Conversely, question-answering systems change how humans interact with information systems: users can now ask specific questions and obtain a tailored answer - both conveniently in natural language. Despite obvious benefits, their use is often limited to an academic context, largely because of expensive domain customizations, which means that the performance in domain-specific applications often fails to meet expectations. This paper proposes cost-efficient remedies: (i) we leverage metadata through a filtering mechanism, which increases the precision of document retrieval, and (ii) we develop a novel fuse-and-oversample approach for transfer learning in order to improve the performance of answer extraction. Here knowledge is inductively transferred from a related, yet different, tasks to the domain-specific application, while accounting for potential differences in the sample sizes across both tasks. The resulting performance is demonstrated with actual use cases from a finance company and the film industry, where fewer than 400 question-answer pairs had to be annotated in order to yield significant performance gains. As a direct implication to management, this presents a promising path to better leveraging of knowledge stored in information systems.

• The paper introduces a dual approach combining metadata filtering with a fuse-and-oversample transfer learning method to enable efficient domain customization using fewer than 400 annotated pairs.
• It demonstrates significant performance gains by boosting recall@1 from 0.48 to 0.88 and enhancing the exact match ratio by up to 17% over traditional models.
• The approach offers practical benefits by reducing manual customization costs, paving the way for scalable QA system deployment across various industries.

Insights into Domain Customization for Question-Answering Systems via Transfer Learning

In the field of information retrieval, conventional search engines often inundate users with expansive result lists, thereby necessitating manual filtering to locate pertinent information. This paper by Kratzwald and Feuerriegel explores an alternative through question-answering (QA) systems that facilitate direct querying and tailored responses in natural language. Despite evidence of superior usability, these systems are constrained outside academic settings due to costly domain-specific customizations. The authors propose strategies to mitigate this through metadata filtering coupled with transfer learning, offering a compelling approach to enhance domain customization efficiency.

Transfer Learning and Metadata Filtering

The crux of the paper lies in its dual approach: leveraging metadata for improved document retrieval and the development of a "fuse-and-oversample" method for transfer learning to enhance answer extraction accuracy. The authors introduce an innovative technique where transfer learning is employed to transfer knowledge from general open-domain applications to domain-specific use cases, accounting for potential variations in sample sizes. This methodology demonstrated marked improvement in QA performance, as exemplified within financial and film industry contexts. Notably, the customization was achieved with fewer than 400 annotated question-answer pairs, underscoring the approach's cost-efficiency.

Strong Numerical Results

The paper provides compelling numerical outcomes, particularly in the effectiveness of metadata filtering and transfer learning. For instance, integrating metadata filtering saw the recall@$1$ metric rise from 0.48 to 0.88, reflecting a significant increase in document retrieval precision. For answer extraction, the application of the fuse-and-oversample approach elevated the exact match ratio, showcasing improvements up to 17.0% over traditional, non-customized models. These results are particularly salient for practitioners seeking robust QA systems adaptable across various domains with minimal manual intervention.

Implications and Future Directions

From a theoretical perspective, this work introduces a paradigm shift in QA system design, emphasizing the fusion of domain-specific needs with general-purpose AI frameworks through transfer learning. The practical implications are substantial; by reducing the resources required for domain-specific customization, organizations can deploy QA systems more widely, potentially reshaping knowledge management and information system interfaces.

The authors suggest future research should aim to overcome limitations associated with the restricted application to English and the necessity for answers to be extracted from single documents. Additionally, expansion into multi-lingual and more complex multi-document scenarios could further bolster the applicability of QA systems.

Overall, the paper by Kratzwald and Feuerriegel illustrates an effective framework for enhancing QA systems, with implications for both practice and theory in AI and information systems. The strategic use of metadata and transfer learning offers a pathway to significantly improve QA technology's practical viability and user experience.