Multivariate time-series (MTS) forecasting is a challenging task in many
real-world non-stationary dynamic scenarios. In addition to intra-series
temporal signals, the inter-series dependency also plays a crucial role in
shaping future trends. How to enable the model's awareness of dependency
information has raised substantial research attention. Previous approaches have
either presupposed dependency constraints based on domain knowledge or imposed
them using real-time feature similarity. However, MTS data often exhibit both
enduring long-term static relationships and transient short-term interactions,
which mutually influence their evolving states. It is necessary to recognize
and incorporate the complementary dependencies for more accurate MTS
prediction. The frequency information in time series reflects the evolutionary
rules behind complex temporal dynamics, and different frequency components can
be used to well construct long-term and short-term interactive dependency
structures between variables. To this end, we propose FCDNet, a concise yet
effective framework for multivariate time-series forecasting. Specifically,
FCDNet overcomes the above limitations by applying two light-weight dependency
constructors to help extract long- and short-term dependency information
adaptively from multi-level frequency patterns. With the growth of input
variables, the number of trainable parameters in FCDNet only increases
linearly, which is conducive to the model's scalability and avoids
over-fitting. Additionally, adopting a frequency-based perspective can
effectively mitigate the influence of noise within MTS data, which helps
capture more genuine dependencies. The experimental results on six real-world
datasets from multiple fields show that FCDNet significantly exceeds strong
baselines, with an average improvement of 6.82% on MAE, 4.98% on RMSE, and
4.91% on MAPE.

FCDNet 是一个简洁而有效的框架，通过两个轻量级依赖构造器，从多层频率模式中自适应地提取长期和短期依赖信息，从而克服了先前方法所面临的限制，并显著超过强基线，平均改进了 MAE 的 6.82%、RMSE 的 4.98% 和 MAPE 的 4.91%。

FCDNet: 频率引导的补充依赖建模用于多变量时间序列预测

FCDNet: Frequency-Guided Complementary Dependency Modeling for  Multivariate Time-Series Forecasting

This paper describes our approach to the CRAC 2022 Shared Task on
Multilingual Coreference Resolution. Our model is based on a state-of-the-art
end-to-end coreference resolution system. Apart from joined multilingual
training, we improved our results with mention head prediction. We also tried
to integrate dependency information into our model. Our system ended up in
$3^{rd}$ place. Moreover, we reached the best performance on two datasets out
of 13.

本文介绍了我们在 CRAC 2022 共享任务中用于多语言指代消解的方法，我们使用了最先进的端到端指代消解系统并进行了多语言联合训练，同时加入了头部预测和依赖信息集成，最终系统取得了第三名并且在其中两个数据集上表现最佳。

端到端多语言指代消解与提及头预测

End-to-end Multilingual Coreference Resolution with Mention Head Prediction

Work on summarization has explored both reinforcement learning (RL)
optimization using ROUGE as a reward and syntax-aware models, such as models
those input is enriched with part-of-speech (POS)-tags and dependency
information. However, it is not clear what is the respective impact of these
approaches beyond the standard ROUGE evaluation metric. Especially, RL-based
for summarization is becoming more and more popular. In this paper, we provide
a detailed comparison of these two approaches and of their combination along
several dimensions that relate to the perceived quality of the generated
summaries: number of repeated words, distribution of part-of-speech tags,
impact of sentence length, relevance and grammaticality. Using the standard
Gigaword sentence summarization task, we compare an RL self-critical sequence
training (SCST) method with syntax-aware models that leverage POS tags and
Dependency information. We show that on all qualitative evaluations, the
combined model gives the best results, but also that only training with RL and
without any syntactic information already gives nearly as good results as
syntax-aware models with less parameters and faster training convergence.

本文比较了强化学习（RL）和基于句法的模型（如基于词性标注和依存信息的模型）两种文摘方法的优劣，并探究了它们在生成摘要时的影响，得出了使用两种方法的联合模型在质量评估方面表现最佳，但仅使用强化学习的训练无需更少的参数和更快的训练收敛即可得到与基于句法模型相近的好结果。