Adaptive Document Retrieval for Deep Question Answering

State-of-the-art systems in deep question answering proceed as follows: (1) an initial document retrieval selects relevant documents, which (2) are then processed by a neural network in order to extract the final answer. Yet the exact interplay between both components is poorly understood, especially concerning the number of candidate documents that should be retrieved. We show that choosing a static number of documents -- as used in prior research -- suffers from a noise-information trade-off and yields suboptimal results. As a remedy, we propose an adaptive document retrieval model. This learns the optimal candidate number for document retrieval, conditional on the size of the corpus and the query. We report extensive experimental results showing that our adaptive approach outperforms state-of-the-art methods on multiple benchmark datasets, as well as in the context of corpora with variable sizes.Comment: EMNLP 201

RankQA: Neural Question Answering with Answer Re-Ranking

The conventional paradigm in neural question answering (QA) for narrative content is limited to a two-stage process: first, relevant text passages are retrieved and, subsequently, a neural network for machine comprehension extracts the likeliest answer. However, both stages are largely isolated in the status quo and, hence, information from the two phases is never properly fused. In contrast, this work proposes RankQA: RankQA extends the conventional two-stage process in neural QA with a third stage that performs an additional answer re-ranking. The re-ranking leverages different features that are directly extracted from the QA pipeline, i.e., a combination of retrieval and comprehension features. While our intentionally simple design allows for an efficient, data-sparse estimation, it nevertheless outperforms more complex QA systems by a significant margin: in fact, RankQA achieves state-of-the-art performance on 3 out of 4 benchmark datasets. Furthermore, its performance is especially superior in settings where the size of the corpus is dynamic. Here the answer re-ranking provides an effective remedy against the underlying noise-information trade-off due to a variable corpus size. As a consequence, RankQA represents a novel, powerful, and thus challenging baseline for future research in content-based QA.Comment: Accepted at ACL 2019; GitHub: https://github.com/bernhard2202/rankq

Domain Adaptation in Neural Question Answering

Question Answering (QA) is the task of automatically deriving answers from natural language questions. It fundamentally redefines how humans interact with information systems by replacing keyword search or technical queries with interactions in natural language. The recent advancement of deep learning and neural networks has lead to significant performance gains in QA. Yet, the performance of QA systems often drops significantly when they are subject to a domain shift, i.e., questions used to train the system differ from user questions after deployment. This represents a severe problem in many practical setups, where certain guarantees on the performance are required. Furthermore, it limits the application of QA to areas for which we have access to large amounts of training data; which is primarily open-domain questions in English language. In this thesis, we present several solutions to tackle the problem of domain shift in QA. On the one hand, we present two methods designed for a usage before the deployment of a QA system. This includes a method for transfer learning when having only access to a small labeled amount of training data, as well as a method for a cost-effective annotation of new datasets. On the other hand, we present a method for domain customization after deployment. Here, the QA system continuously learns to adapt to the new domain directly from user interactions and is capable to overcome an initially low performance over time. In addition, we present robust architectures for QA systems that help in addressing domain shift as a foundation of this thesis. Finally, we show how our methods can be extended to the different but related task of knowledge base completion

Learning from On-Line User Feedback in Neural Question Answering on the Web

Question answering promises a means of efficiently searching web-based content repositories such as Wikipedia. However, the systems of this type most prevalent today merely conduct their learning once in an offline training phase while, afterwards, all parameters remain static. Thus, the possibility of improvement over time is precluded. As a consequence of this shortcoming, question answering is not currently taking advantage of the wealth of feedback mechanisms that have become prominent on the web (e. g., buttons for liking, voting, or sharing). This is the first work that introduces a question-answering system for (web-based) content repositories with an on-line mechanism for user feedback. Our efforts have resulted in QApedia - a framework for on-line improvement based on shallow user feedback. In detail, we develop a simple feedback mechanism that allows users to express whether a question was answered satisfactorily or whether a different answer is needed. Even for this simple mechanism, the implementation represents a daunting undertaking due to the complex, multi-staged operations that underlie state-of-the-art systems for neural questions answering. Another challenge with regard to web-based use is that feedback is limited (and possibly even noisy), as the true labels remain unknown. We thus address these challenges through a novel combination of neural question answering and a dynamic process based on distant supervision, asynchronous updates, and an automatic validation of feedback credibility in order to mine high-quality training samples from the web for the purpose of achieving continuous improvement over time. Our QApedia framework is the first question-answering system that continuously refines its capabilities by improving its now dynamic content repository and thus the underlying answer extraction. QApedia not only achieves state-of-the-art results over several benchmarking datasets, but we further show that it successfully manages to learn from shallow user feedback, even when the feedback is noisy or adversarial. Altogether, our extensive experimental evaluation, with more than 2,500 hours of computational experiments, demonstrates that a feedback mechanism as simple as a binary vote (which is widespread on the web) can considerably improve performance when combined with an efficient framework for continuous learning