Hybrid question answering (HybridQA) over the financial report contains both
textual and tabular data, and requires the model to select the appropriate
evidence for the numerical reasoning task. Existing methods based on
encoder-decoder framework employ a expression tree-based decoder to solve
numerical reasoning problems. However, encoders rely more on Machine Reading
Comprehension (MRC) methods, which take table serialization and text splicing
as input, damaging the granularity relationship between table and text as well
as the spatial structure information of table itself. In order to solve these
problems, the paper proposes a Multi-View Graph (MVG) Encoder to take the
relations among the granularity into account and capture the relations from
multiple view. By utilizing MVGE as a module, we constuct Tabular View,
Relation View and Numerical View which aim to retain the original
characteristics of the hybrid data. We validate our model on the publicly
available table-text hybrid QA benchmark (TAT-QA) and outperform the
state-of-the-art model.

本文提出了一种使用多视图图形编码器来对文本和表格数据进行混合问答 (Hybrid question answering) 的方法，以解决现有方法中文本和表格数据之间粒度关系和空间结构信息的问题，并在公共数据集 TAT-QA 上取得了最优性能。

多视图图表示学习用于回答混合数值推理问题

Multi-View Graph Representation Learning for Answering Hybrid Numerical  Reasoning Question

Numerical reasoning over hybrid data containing both textual and tabular
content (e.g., financial reports) has recently attracted much attention in the
NLP community. However, existing question answering (QA) benchmarks over hybrid
data only include a single flat table in each document and thus lack examples
of multi-step numerical reasoning across multiple hierarchical tables. To
facilitate data analytical progress, we construct a new large-scale benchmark,
MultiHiertt, with QA pairs over Multi Hierarchical Tabular and Textual data.
MultiHiertt is built from a wealth of financial reports and has the following
unique characteristics: 1) each document contain multiple tables and longer
unstructured texts; 2) most of tables contained are hierarchical; 3) the
reasoning process required for each question is more complex and challenging
than existing benchmarks; and 4) fine-grained annotations of reasoning
processes and supporting facts are provided to reveal complex numerical
reasoning. We further introduce a novel QA model termed MT2Net, which first
applies facts retrieving to extract relevant supporting facts from both tables
and text and then uses a reasoning module to perform symbolic reasoning over
retrieved facts. We conduct comprehensive experiments on various baselines. The
experimental results show that MultiHiertt presents a strong challenge for
existing baselines whose results lag far behind the performance of human
experts. The dataset and code are publicly available at
this https URL.

构建一个新的 QA benchmark (MultiHiertt)，使用金融报告构建。包含多个表以及更长的非结构化文本，大部分表格是分层的；提供了复杂的数量推理的细粒度注释；提出了一个新的 QA 模型 MT2Net，它首先应用事实检索来提取相关事实，然后使用推理模块对检索到的事实进行符号推理。与现有基线相比，实验结果表明 MultiHiertt 对现有基线提出了严峻的挑战。

MultiHiertt: 对多层次表格和文本数据的数值推理

MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data

Automatically generating financial report from a piece of news is quite a
challenging task. Apparently, the difficulty of this task lies in the lack of
sufficient background knowledge to effectively generate long financial report.
To address this issue, this paper proposes the conditional variational
autoencoders (CVAE) based approach which distills external knowledge from a
corpus of news-report data. Particularly, we choose Bi-GRU as the encoder and
decoder component of CVAE, and learn the latent variable distribution from
input news. A higher level latent variable distribution is learnt from a corpus
set of news-report data, respectively extr acted for each input news, to
provide background knowledge to previously learnt latent variable distribution.
Then, a teacher-student network is employed to distill knowledge to refine
theoutput of the decoder component. To evaluate the model performance of the
proposed approach, extensive experiments are preformed on a public dataset and
two widely adopted evaluation criteria, i.e., BLEU and ROUGE, are chosen in the
experiment. The promising experimental results demonstrate that the proposed
approach is superior to the rest compared methods.

通过条件变分自编码器和师生网络技术，从新闻数据中提取金融报告，并在公共数据集上取得了优于其他方法的结果。