Query graph construction aims to construct the correct executable SPARQL on
the KG to answer natural language questions. Although recent methods have
achieved good results using neural network-based query graph ranking, they
suffer from three new challenges when handling more complex questions: 1)
complicated SPARQL syntax, 2) huge search space, and 3) locally ambiguous query
graphs. In this paper, we provide a new solution. As a preparation, we extend
the query graph by treating each SPARQL clause as a subgraph consisting of
vertices and edges and define a unified graph grammar called AQG to describe
the structure of query graphs. Based on these concepts, we propose a novel
end-to-end model that performs hierarchical autoregressive decoding to generate
query graphs. The high-level decoding generates an AQG as a constraint to prune
the search space and reduce the locally ambiguous query graph. The bottom-level
decoding accomplishes the query graph construction by selecting appropriate
instances from the preprepared candidates to fill the slots in the AQG. The
experimental results show that our method greatly improves the SOTA performance
on complex KGQA benchmarks. Equipped with pre-trained models, the performance
of our method is further improved, achieving SOTA for all three datasets used.

本文提出了一种基于 AQG（一种统一的图形文法）的端到端模型，由高级解码生成用于约束搜索空间的 AQG 和底级解码构造查询图，旨在优化查询图构建过程，提高在复杂 KGQA 基准测试中的表现。

基于层次化查询图生成的知识图谱复杂问题回答：概述和填写

Outlining and Filling: Hierarchical Query Graph Generation for Answering  Complex Questions over Knowledge Graphs

Automatic question generation is defined as the task of automating the
creation of question given a various of textual data. Research in automatic
question generator (AQG) has been conducted for more than 10 years, mainly
focused on factoid question. In all these studies, the state-of-the-art is
attained using sequence-to-sequence approach. However, AQG system for
Indonesian has not ever been researched intensely. In this work we construct an
Indonesian automatic question generator, adapting the architecture from some
previous works. In summary, we used sequence-to-sequence approach using BiGRU,
BiLSTM, and Transformer with additional linguistic features, copy mechanism,
and coverage mechanism. Since there is no public large dan popular Indonesian
dataset for question generation, we translated SQuAD v2.0 factoid question
answering dataset, with additional Indonesian TyDiQA dev set for testing. The
system achieved BLEU1, BLEU2, BLEU3, BLEU4, and ROUGE-L score at 38,35, 20,96,
10,68, 5,78, and 43,4 for SQuAD, and 39.9, 20.78, 10.26, 6.31, 44.13 for
TyDiQA, respectively. The system performed well when the expected answers are
named entities and are syntactically close with the context explaining them.
Additionally, from native Indonesian perspective, the best questions generated
by our best models on their best cases are acceptable and reasonably useful.

本研究构建了一个基于印尼语的自动问题生成器，使用 BiGRU、BiLSTM 和 Transformer 结构，加入语言特征、复制机制和覆盖机制，使用 SQuAD v2.0 数据集并成功实现了自动化生成问题，表现出较好的性能。

印度尼西亚自动提问生成器的序列到序列学习

Sequence-to-Sequence Learning for Indonesian Automatic Question  Generator

Automatic question generation (AQG) has broad applicability in domains such
as tutoring systems, conversational agents, healthcare literacy, and
information retrieval. Existing efforts at AQG have been limited to short
answer lengths of up to two or three sentences. However, several real-world
applications require question generation from answers that span several
sentences. Therefore, we propose a novel evaluation benchmark to assess the
performance of existing AQG systems for long-text answers. We leverage the
large-scale open-source Google Natural Questions dataset to create the
aforementioned long-answer AQG benchmark. We empirically demonstrate that the
performance of existing AQG methods significantly degrades as the length of the
answer increases. Transformer-based methods outperform other existing AQG
methods on long answers in terms of automatic as well as human evaluation.
However, we still observe degradation in the performance of our best performing
models with increasing sentence length, suggesting that long answer QA is a
challenging benchmark task for future research.

提出了一种新的评估基准用于评估现有的自动生成问题系统的性能，特别是长文本答案下的自动生成问题系统。研究表明，随着答案长度的增加，现有 AQG 方法的性能显著下降，变压器模型在长答案方面的表现优于其他 AQG 方法，但仍存在性能下降的情况，这表明长答案 QA 是未来研究的具有挑战性的基准任务。