Revealing the Importance of Semantic Retrieval for Machine Reading at Scale (1909.08041v1)

Abstract: Machine Reading at Scale (MRS) is a challenging task in which a system is given an input query and is asked to produce a precise output by "reading" information from a large knowledge base. The task has gained popularity with its natural combination of information retrieval (IR) and machine comprehension (MC). Advancements in representation learning have led to separated progress in both IR and MC; however, very few studies have examined the relationship and combined design of retrieval and comprehension at different levels of granularity, for development of MRS systems. In this work, we give general guidelines on system design for MRS by proposing a simple yet effective pipeline system with special consideration on hierarchical semantic retrieval at both paragraph and sentence level, and their potential effects on the downstream task. The system is evaluated on both fact verification and open-domain multihop QA, achieving state-of-the-art results on the leaderboard test sets of both FEVER and HOTPOTQA. To further demonstrate the importance of semantic retrieval, we present ablation and analysis studies to quantify the contribution of neural retrieval modules at both paragraph-level and sentence-level, and illustrate that intermediate semantic retrieval modules are vital for not only effectively filtering upstream information and thus saving downstream computation, but also for shaping upstream data distribution and providing better data for downstream modeling. Code/data made publicly available at: https://github.com/easonnie/semanticRetrievalMRS

• The paper introduces a hierarchical semantic retrieval strategy that enhances downstream comprehension by integrating IR and MC at multiple granularity levels.
• The approach is validated on FEVER and HotpotQA, achieving a FEVER score of 67.26% and significant improvements in answer and joint exact-match metrics.
• The paper shows that refined semantic retrieval not only conserves computational resources but also sets a new performance benchmark for large-scale text processing.

An Evaluation of Semantic Retrieval's Role in Machine Reading at Scale

The paper "Revealing the Importance of Semantic Retrieval for Machine Reading at Scale" presents an exploration into the integration of information retrieval (IR) and machine comprehension (MC) within the framework of Machine Reading at Scale (MRS). By proposing a holistic design that incorporates hierarchical semantic retrieval, the authors Yixin Nie, Songhe Wang, and Mohit Bansal aim to refine understanding of its crucial impact on downstream comprehension tasks.

The research addresses the gap in existing work concerning the overlooked potential of optimizing IR and MC tasks at variable granularity levels. The primary objective is to develop an effective MRS system by applying semantic retrieval techniques hierarchically at the paragraph and sentence levels. The authors evaluate their proposed system through two widely recognized tasks in the domain: fact verification and open-domain multi-hop question answering (QA), specifically using the FEVER and HotpotQA datasets. The research introduces a pipeline system that they claim achieves superior performance against existing benchmarks.

The core contribution lies in demonstrating the symbiotic relationship between upstream semantic retrieval and downstream comprehension. The paper undertakes a comprehensive analysis through both ablation studies and detailed evaluations to quantify the significance of paragraph-level and sentence-level retrievals. These experiments show that their hierarchical approach not only improves computational efficiency by effectively filtering relevant information but also provides more contextually appropriate data for subsequent comprehension tasks.

Numerical results emphasize the impact, with the system achieving a notable FEVER score of 67.26% and advancing the answer-exact-match and joint exact-match metrics on HotpotQA by substantial margins. These improvements validate the necessity of precise semantic retrieval in augmenting the upper bound of downstream task performance, thereby improving the overall system’s efficacy.

The authors argue that an effective MRS design should not merely aggregate information, but strategically select supporting data to enhance the downstream task's recall and precision balance. This approach effectively demonstrates that semantic retrieval modules that integrate multiple granularity levels offer performance gains attributed to improved data distribution and quality for training and prediction phases.

Implications of this research extend to practical applications, where efficient processing of large-scale textual corpora is paramount. By optimizing the retrieval components, computational resources are conserved, reducing overhead in data processing tasks. Theoretical implications suggest potential avenues for future research in the joint optimization of IR and MC, as deeper exploration into their interplay could bring about further breakthroughs in AI comprehension capabilities.

The authors provide a public release of their code and organized dataset, inviting further exploration and validation of their findings. Such contributions open potential pathways for refining current models, offering researchers equipped with these insights refined strategies for enhancing machine reading capabilities on the large scale.

In summary, this paper systematically elucidates the integral role of precise semantic retrieval strategies within MRS frameworks, offering both empirical evidence and theoretical insights to guide future research in the domain.