Efficient and Robust Question Answering from Minimal Context over Documents (1805.08092v1)

Abstract: Neural models for question answering (QA) over documents have achieved significant performance improvements. Although effective, these models do not scale to large corpora due to their complex modeling of interactions between the document and the question. Moreover, recent work has shown that such models are sensitive to adversarial inputs. In this paper, we study the minimal context required to answer the question, and find that most questions in existing datasets can be answered with a small set of sentences. Inspired by this observation, we propose a simple sentence selector to select the minimal set of sentences to feed into the QA model. Our overall system achieves significant reductions in training (up to 15 times) and inference times (up to 13 times), with accuracy comparable to or better than the state-of-the-art on SQuAD, NewsQA, TriviaQA and SQuAD-Open. Furthermore, our experimental results and analyses show that our approach is more robust to adversarial inputs.

Overview of Efficient and Robust Question Answering from Minimal Context Over Documents

The paper "Efficient and Robust Question Answering from Minimal Context Over Documents" introduces a novel method aimed at enhancing the efficiency and robustness of neural models used for question answering (QA) over large text corpora. The authors identify the primary limitation of traditional QA models as their dependency on extensive interaction modeling between the document and the question, which is computationally intensive and susceptible to adversarial inputs.

Methods and Experimentation

The central premise of this research is the identification and utilization of minimal context required for effective QA. The authors propose a sentence selection mechanism that extracts a reduced set of sentences from the document that are most relevant to the question posed. This subset is then utilized for training and inference, significantly ameliorating computational demands.

In terms of performance, the approach demonstrates substantial improvements in efficiency—specifically, reductions in training time by up to 15 times and inference time by up to 13 times compared to previous state-of-the-art methods. When applied to well-known datasets such as SQuAD, NewsQA, TriviaQA, and SQuAD-Open, the proposed method maintains accuracy levels comparable to or surpassing current benchmarks.

Implications and Future Directions

The implications of these findings are noteworthy both in practical and theoretical contexts. Practically, this approach allows for the deployment of QA systems over large corpora with reduced computational overhead, making them feasible for real-world applications where resource constraints are a concern. Theoretically, this method challenges the assumption that broad context is necessary for precise QA, suggesting that sparse context representation may suffice, and potentially leading to new paradigms in text interpretation.

Another significant aspect of the paper is the increased robustness of the model to adversarial inputs, addressing a critical vulnerability of existing models. This robustness increases the reliability and dependability of QA systems in diverse settings, especially where data integrity cannot be guaranteed.

Looking forward, the approach raises several avenues for further research. Exploration of more sophisticated methods for context minimization and sentence selection could further enhance performance metrics. Furthermore, investigating the utility of this approach across other types of neural network models, and adapting it to varied formats of input data beyond text, could broaden the scope of its applications.

In summary, the paper presents a compelling enhancement in the efficiency and reliability of question answering systems by focusing on minimal context interaction, paving the way for more sustainable computational practices in AI and machine learning.