One of the key AI tools for textual corpora exploration is natural language
question-answering (QA). Unlike keyword-based search engines, QA algorithms
receive and process natural language questions and produce precise answers to
these questions, rather than long lists of documents that need to be manually
scanned by the users. State-of-the-art QA algorithms based on DNNs were
successfully employed in various domains. However, QA in the genealogical
domain is still underexplored, while researchers in this field (and other
fields in humanities and social sciences) can highly benefit from the ability
to ask questions in natural language, receive concrete answers and gain
insights hidden within large corpora. While some research has been recently
conducted for factual QA in the genealogical domain, to the best of our
knowledge, there is no previous research on the more challenging task of
numerical aggregation QA (i.e., answering questions combining aggregation
functions, e.g., count, average, max). Numerical aggregation QA is critical for
distant reading and analysis for researchers (and the general public)
interested in investigating cultural heritage domains. Therefore, in this
study, we present a new end-to-end methodology for numerical aggregation QA for
genealogical trees that includes: 1) an automatic method for training dataset
generation; 2) a transformer-based table selection method, and 3) an optimized
transformer-based numerical aggregation QA model. The findings indicate that
the proposed architecture, GLOBE, outperforms the state-of-the-art models and
pipelines by achieving 87% accuracy for this task compared to only 21% by
current state-of-the-art models. This study may have practical implications for
genealogical information centers and museums, making genealogical data research
easy and scalable for experts as well as the general public.

研究提出了一种新的端到端数值聚合 QA 方法，名为 GLOBE，用于解决对家谱树进行数值聚合问题。结果表明，GLOBE 在这一任务上的准确率达到了 87%，远超过当前最先进模型的 21%。这项研究对家谱信息中心和博物馆具有实际意义，使得专家和普通公众可以轻松进行家谱数据研究。

环球之间：基于深度神经网络的异质家族知识图谱的数值聚合问答

Around the GLOBE: Numerical Aggregation Question-Answering on  Heterogeneous Genealogical Knowledge Graphs with Deep Neural Networks

Open Information Extraction (OpenIE) facilitates the open-domain discovery of
textual facts. However, the prevailing solutions evaluate OpenIE models on
in-domain test sets aside from the training corpus, which certainly violates
the initial task principle of domain-independence. In this paper, we propose to
advance OpenIE towards a more realistic scenario: generalizing over unseen
target domains with different data distributions from the source training
domains, termed Generalized OpenIE. For this purpose, we first introduce GLOBE,
a large-scale human-annotated multi-domain OpenIE benchmark, to examine the
robustness of recent OpenIE models to domain shifts, and the relative
performance degradation of up to 70% implies the challenges of generalized
OpenIE. Then, we propose DragonIE, which explores a minimalist graph expression
of textual fact: directed acyclic graph, to improve the OpenIE generalization.
Extensive experiments demonstrate that DragonIE beats the previous methods in
both in-domain and out-of-domain settings by as much as 6.0% in F1 score
absolutely, but there is still ample room for improvement.

本文介绍了一种名为 DragonIE 的 OpenIE 模型，它使用有向无环图的文本事实的极简表示来提高 Generalized OpenIE 的一般性能，并在 In-domain 和 out-of-domain 设置中表现出比以前的方法高 6.0％的 F1 分数的提升，但仍有大量改进的空间。