Large-scale Simple Question Answering with Memory Networks (1506.02075v1)

Abstract: Training large-scale question answering systems is complicated because training sources usually cover a small portion of the range of possible questions. This paper studies the impact of multitask and transfer learning for simple question answering; a setting for which the reasoning required to answer is quite easy, as long as one can retrieve the correct evidence given a question, which can be difficult in large-scale conditions. To this end, we introduce a new dataset of 100k questions that we use in conjunction with existing benchmarks. We conduct our study within the framework of Memory Networks (Weston et al., 2015) because this perspective allows us to eventually scale up to more complex reasoning, and show that Memory Networks can be successfully trained to achieve excellent performance.

• The paper introduces the SimpleQuestions dataset, a large-scale benchmark of over 108k question-fact pairs for evaluating simple QA systems.
• It leverages a memory network architecture that efficiently processes and generalizes facts from Freebase and Reverb for robust question answering.
• The study demonstrates effective transfer learning by achieving 68% accuracy on Reverb without retraining, setting a new performance benchmark.

Large-scale Simple Question Answering with Memory Networks

Introduction

The paper "Large-scale Simple Question Answering with Memory Networks" by Bordes et al. addresses the challenge of building scalable Open-domain Question Answering (QA) systems. The study utilizes Memory Networks (MemNNs) to enhance performance in simple QA tasks, which require retrieving a single supporting fact from large Knowledge Bases (KBs). The authors introduce a new dataset, "SimpleQuestions", which consists of 100k questions paired with their corresponding facts from Freebase and demonstrate how MemNNs can be effectively employed for simple QA.

Contributions

The paper offers two primary contributions:

1. Introduction of the SimpleQuestions Dataset To address the lack of large-scale QA benchmarks, the authors introduce SimpleQuestions, a dataset containing 108,442 question-fact pairs. This dataset provides a more extensive range of question templates and syntactical variations compared to existing benchmarks focusing on a smaller scope.
2. Memory Networks for Simple QA Implementing MemNNs for simple QA tasks, the paper highlights their ability to handle large-scale data effectively. Through the multitask and transfer learning frameworks, MemNNs are shown to achieve state-of-the-art results on standard QA benchmarks such as WebQuestions, SimpleQuestions, and, interestingly, transfer learning on Reverb without retraining.

Methodology

Memory Network Architecture

MemNNs comprise four principal components: Input map (I), Generalization (G), Output map (O), and Response (R). For simple QA, the workflow involves three key steps:

• Storing Freebase: Parsing and storing Freebase facts in memory using the Input module.
• Training: Training the MemNN to answer questions using the Input, Output, and Response modules.
• Connecting Reverb: Adding new facts from Reverb to the memory post-training, leveraging the Input and Generalization modules to ensure compatibility.

Implementation Details

1. Preprocessing Freebase Facts:
   • Grouping facts to handle list-type questions by consolidating multiple objects linked by the same relationship.
   • Removing mediator nodes to enable direct linkage between entities, thus simplifying QA tasks.
2. Preprocessing Questions: Transforming questions into bag-of-ngrams representations.
3. Generalization with Reverb: Linking Reverb facts to Freebase entities, converting both datasets into compatible representations for memory storage and retrieval without retraining.
4. Multitask and Transfer Learning: Employing a multitask learning approach where the model is trained on multiple data sources, including the new dataset, synthetic questions, and paraphrases. This robust training regimen ensures comprehensive coverage of possible question templates and enhances generalization.

Results

The experiments reveal several significant findings:

• Performance on Benchmarks: Achieving an F1-score of 42.2% on WebQuestions, surpassing previous models using embedding spaces and semantic parsing. For SimpleQuestions, MemNNs achieved an accuracy of 63.9%.
• Transfer Learning: Impressively, the MemNNs achieved 68% accuracy on Reverb QA without training specifically on that dataset, showcasing the model's capability for effective transfer learning.
• Importance of Data Sources: The inclusion of diverse training data sources enhanced model robustness, with the usage of paraphrase data proving particularly beneficial for datasets like WebQuestions with higher syntactical variability.

Implications and Future Directions

The findings highlight the practical implications of using MemNNs for large-scale QA systems, emphasizing:

• Scalability of MemNNs: The ability of MemNNs to handle extensive KBs and perform well on varied QA tasks demonstrates their suitability for real-world applications requiring large-scale data handling.
• Simplification of QA Tasks: By structuring KBs to eliminate mediator nodes and grouping related facts, the retrieval process is streamlined, making it feasible to solve complex QA tasks with simpler models.

Looking forward, future research could:

• Explore More Complex Reasoning: Extend MemNNs to handle multi-hop reasoning tasks, leveraging the existing framework to build on more complex inference schemes.
• Enhance QA Models: Further investigate the impact of paraphrase data and weak supervision techniques, optimizing MemNNs for broader linguistic coverage and efficiency.

In conclusion, the paper by Bordes et al. provides substantial advancements in QA systems through the introduction of extensive datasets and the application of Memory Networks, setting a strong foundation for future developments in AI-driven QA research.