Tabular question answering (TQA) presents a challenging setting for neural
systems by requiring joint reasoning of natural language with large amounts of
semi-structured data. Unlike humans who use programmatic tools like filters to
transform data before processing, language models in TQA process tables
directly, resulting in information loss as table size increases. In this paper
we propose ToolWriter to generate query specific programs and detect when to
apply them to transform tables and align them with the TQA model's
capabilities. Focusing ToolWriter to generate row-filtering tools improves the
state-of-the-art for WikiTableQuestions and WikiSQL with the most performance
gained on long tables. By investigating headroom, our work highlights the
broader potential for programmatic tools combined with neural components to
manipulate large amounts of structured data.

本文提出了 ToolWriter 算法，针对 tabular question answering 中信息丢失问题，生成查询特定的程序并检测何时将它们应用于转换表格以与 TQA 模型的能力对齐。使用 ToolWriter 生成的行过滤器工具，提高了 WikiTableQuestions 和 WikiSQL 数据集的性能。同时揭示了结构化数据中潜在的程序化工具与神经元件相结合的潜力。

生成、转换、回答：面向表格数据的问题特定工具综合

Generate, Transform, Answer: Question Specific Tool Synthesis for Tabular Data

Recently, there has been a rising surge of momentum for deep representation
learning in hyperbolic spaces due to theirhigh capacity of modeling data like
knowledge graphs or synonym hierarchies, possessing hierarchical structure. We
refer to the model as hyperbolic deep neural network in this paper. Such a
hyperbolic neural architecture potentially leads to drastically compact model
withmuch more physical interpretability than its counterpart in Euclidean
space. To stimulate future research, this paper presents acoherent and
comprehensive review of the literature around the neural components in the
construction of hyperbolic deep neuralnetworks, as well as the generalization
of the leading deep approaches to the Hyperbolic space. It also presents
current applicationsaround various machine learning tasks on several publicly
available datasets, together with insightful observations and identifying
openquestions and promising future directions.

本文介绍了超半径神经网络的模型以及各个组成部分，探究了其在超几何空间的深度学习方法、机器学习任务上的应用，提出了未来研究方向。

双曲深度神经网络：一份调查

Hyperbolic Deep Neural Networks: A Survey

Entity linking is a standard component in modern retrieval system that is
often performed by third-party toolkits. Despite the plethora of open source
options, it is difficult to find a single system that has a modular
architecture where certain components may be replaced, does not depend on
external sources, can easily be updated to newer Wikipedia versions, and, most
important of all, has state-of-the-art performance. The REL system presented in
this paper aims to fill that gap. Building on state-of-the-art neural
components from natural language processing research, it is provided as a
Python package as well as a web API. We also report on an experimental
comparison against both well-established systems and the current
state-of-the-art on standard entity linking benchmarks.

本文提出了一种名为 REL 的 Python 包和网络 API，它是一种具有模块化架构、使用最先进的自然语言处理研究的神经组件、不依赖外部来源、可轻松更新到较新的维基百科版本并具有最先进的性能的单一系统来填补现有工具的缺口。作者还在标准的实体链接基准测试上进行了实验比较。

REL：基于巨人肩膀上的实体链接器

REL: An Entity Linker Standing on the Shoulders of Giants

We investigate modeling coreference resolution under a fixed memory
constraint by extending an incremental clustering algorithm to utilize
contextualized encoders and neural components. Given a new sentence, our
end-to-end algorithm proposes and scores each mention span against explicit
entity representations created from the earlier document context (if any).
These spans are then used to update the entity's representations before being
forgotten; we only retain a fixed set of salient entities throughout the
document. In this work, we successfully convert a high-performing model (Joshi
et al., 2020), asymptotically reducing its memory usage to constant space with
only a 0.3% relative loss in F1 on OntoNotes 5.0.

使用上下文编码器和神经组件扩展增量聚类算法来模拟在固定内存约束下的关联消解，并对高性能模型进行了优化，以在 OntoNotes5.0 数据集上实现恒定空间的相对 F1 损失仅为 0.3%。