Temporal question answering (QA) involves time constraints, with phrases such
as "... in 2019" or "... before COVID". In the former, time is an explicit
condition, in the latter it is implicit. State-of-the-art methods have
limitations along three dimensions. First, with neural inference, time
constraints are merely soft-matched, giving room to invalid or inexplicable
answers. Second, questions with implicit time are poorly supported. Third,
answers come from a single source: either a knowledge base (KB) or a text
corpus. We propose a temporal QA system that addresses these shortcomings.
First, it enforces temporal constraints for faithful answering with tangible
evidence. Second, it properly handles implicit questions. Third, it operates
over heterogeneous sources, covering KB, text and web tables in a unified
manner. The method has three stages: (i) understanding the question and its
temporal conditions, (ii) retrieving evidence from all sources, and (iii)
faithfully answering the question. As implicit questions are sparse in prior
benchmarks, we introduce a principled method for generating diverse questions.
Experiments show superior performance over a suite of baselines.

在此研究中，我们提出了针对时态问答系统的新方法，通过可信证据强制执行时间约束条件、正确处理隐含问题，并统一处理知识库、文本和网页表格等异构数据源。实验结果表明，该方法在多个基准测试中展现出卓越的性能。

异构源上的忠实时态问答

Faithful Temporal Question Answering over Heterogeneous Sources

Books are typically segmented into chapters and sections, representing
coherent subnarratives and topics. We investigate the task of predicting
chapter boundaries, as a proxy for the general task of segmenting long texts.
We build a Project Gutenberg chapter segmentation data set of 9,126 English
novels, using a hybrid approach combining neural inference and rule matching to
recognize chapter title headers in books, achieving an F1-score of 0.77 on this
task. Using this annotated data as ground truth after removing structural cues,
we present cut-based and neural methods for chapter segmentation, achieving an
F1-score of 0.453 on the challenging task of exact break prediction over
book-length documents. Finally, we reveal interesting historical trends in the
chapter structure of novels.

使用神经推理和规则匹配的混合方法，预测长篇文本的章节边界，通过剔除结构线索并利用预测数据集，我们发现切割和神经方法可以精确地分割小说的章节，并揭示了小说章节结构的有趣历史趋势。

章节捕手：小说中的文本分割

Chapter Captor: Text Segmentation in Novels

A news article's title, content and link structure often reveal its political
ideology. However, most existing works on automatic political ideology
detection only leverage textual cues. Drawing inspiration from recent advances
in neural inference, we propose a novel attention based multi-view model to
leverage cues from all of the above views to identify the ideology evinced by a
news article. Our model draws on advances in representation learning in natural
language processing and network science to capture cues from both textual
content and the network structure of news articles. We empirically evaluate our
model against a battery of baselines and show that our model outperforms state
of the art by 10 percentage points F1 score.

本文提出了一种基于关注机制的多角度模型，利用文本、标题、内容以及链接结构等多种视角识别新闻文章所展现出的政治意识形态，实验证明该模型在自然语言处理的表示学习和网络科学中的进展方面，比现有的基线模型在 F1 得分上提高了 10 个百分点。