Ontonotes has served as the most important benchmark for coreference
resolution. However, for ease of annotation, several long documents in
Ontonotes were split into smaller parts. In this work, we build a corpus of
coreference-annotated documents of significantly longer length than what is
currently available. We do so by providing an accurate, manually-curated,
merging of annotations from documents that were split into multiple parts in
the original Ontonotes annotation process. The resulting corpus, which we call
LongtoNotes contains documents in multiple genres of the English language with
varying lengths, the longest of which are up to 8x the length of documents in
Ontonotes, and 2x those in Litbank. We evaluate state-of-the-art neural
coreference systems on this new corpus, analyze the relationships between model
architectures/hyperparameters and document length on performance and efficiency
of the models, and demonstrate areas of improvement in long-document
coreference modeling revealed by our new corpus. Our data and code is available
at: this https URL.

本研究建立了一个新的语料库 LongtoNotes，其中包含了英语文本中多种类型，长度不同的文档，并在其中评估了最新的神经共指系统，分析了模型结构 / 超参数与文档长度对性能和效率的影响，还提出了长文档共指建模中的改进问题。

Longtonotes: 更长的 OntoNotes 指代链

Longtonotes: OntoNotes with Longer Coreference Chains

Dual encoders perform retrieval by encoding documents and queries into dense
lowdimensional vectors, scoring each document by its inner product with the
query. We investigate the capacity of this architecture relative to sparse
bag-of-words models and attentional neural networks. Using both theoretical and
empirical analysis, we establish connections between the encoding dimension,
the margin between gold and lower-ranked documents, and the document length,
suggesting limitations in the capacity of fixed-length encodings to support
precise retrieval of long documents. Building on these insights, we propose a
simple neural model that combines the efficiency of dual encoders with some of
the expressiveness of more costly attentional architectures, and explore
sparse-dense hybrids to capitalize on the precision of sparse retrieval. These
models outperform strong alternatives in large-scale retrieval.

该研究探讨了双编码器的检索能力，并将其与稀疏词袋模型和注意力神经网络进行了比较。研究发现固定长度编码的能力存在限制，具体表现为编码维数，金标和排名较低文件之间的边际，以及文档长度。在此基础上，该研究提出了一种结合了双编码器的高效性和注意力结构的表达能力的简单神经模型，并探索了稀疏 - 密集混合模型以提高检索精度，这些模型在大规模检索中胜过强有力的替代方案。

文本检索中的稀疏、密集和注意力表示

Sparse, Dense, and Attentional Representations for Text Retrieval

Among the critical challenges around the COVID-19 pandemic is dealing with
the potentially detrimental effects on people's mental health. Designing
appropriate interventions and identifying the concerns of those most at risk
requires methods that can extract worries, concerns and emotional responses
from text data. We examine gender differences and the effect of document length
on worries about the ongoing COVID-19 situation. Our findings suggest that i)
short texts do not offer as adequate insights into psychological processes as
longer texts. We further find ii) marked gender differences in topics
concerning emotional responses. Women worried more about their loved ones and
severe health concerns while men were more occupied with effects on the economy
and society. This paper adds to the understanding of general gender differences
in language found elsewhere, and shows that the current unique circumstances
likely amplified these effects. We close this paper with a call for more
high-quality datasets due to the limitations of Tweet-sized data.

对新冠病毒所带来的心理健康问题进行情感分析时，文本长度和性别差异会影响分析结果，女性更关注家人和健康问题，而男性更关注经济和社会问题。