Event Detection (ED) is the task of identifying and classifying trigger words
of event mentions in text. Despite considerable research efforts in recent
years for English text, the task of ED in other languages has been
significantly less explored. Switching to non-English languages, important
research questions for ED include how well existing ED models perform on
different languages, how challenging ED is in other languages, and how well ED
knowledge and annotation can be transferred across languages. To answer those
questions, it is crucial to obtain multilingual ED datasets that provide
consistent event annotation for multiple languages. There exist some
multilingual ED datasets; however, they tend to cover a handful of languages
and mainly focus on popular ones. Many languages are not covered in existing
multilingual ED datasets. In addition, the current datasets are often small and
not accessible to the public. To overcome those shortcomings, we introduce a
new large-scale multilingual dataset for ED (called MINION) that consistently
annotates events for 8 different languages; 5 of them have not been supported
by existing multilingual datasets. We also perform extensive experiments and
analysis to demonstrate the challenges and transferability of ED across
languages in MINION that in all call for more research effort in this area.

本研究介绍了一个新的大规模多语言数据集 MINION，用于事件检测 (Event Detection)，提供了 8 种不同语言的事件注释，其中有 5 种语言并未在现有的多语言数据集中支持。该数据集的实验和分析表明，事件检测在不同语言中存在挑战和可转移性，需要更多的研究努力。

MINION: 用于多语言事件检测的大规模且多样化的数据集

MINION: a Large-Scale and Diverse Dataset for Multilingual Event Detection

The Oxford Nanopore Technologies's MinION is the first portable DNA
sequencing device. It is capable of producing long reads, over 100 kBp were
reported. However, it has significantly higher error rate than other methods.
In this study, we present MinCall, an end2end basecaller model for the MinION.
The model is based on deep learning and uses convolutional neural networks
(CNN) in its implementation. For extra performance, it uses cutting edge deep
learning techniques and architectures, batch normalization and Connectionist
Temporal Classification (CTC) loss. The best performing deep learning model
achieves 91.4% median match rate on E. Coli dataset using R9 pore chemistry and
1D reads.

研究使用深度学习和卷积神经网络来开发基于 MinION 便携式 DNA 测序系统的 basecaller 模型，最佳模型在使用 R9 孔径化学和 1D 读取的 E.Coli 数据集上实现了 91.4% 的匹配率。

MinCall - MinION 端到端卷积深度学习基序呼叫器

MinCall - MinION end2end convolutional deep learning basecaller

Motivation: The MinION device by Oxford Nanopore is the first portable
sequencing device. MinION is able to produce very long reads (reads over
100~kBp were reported), however it suffers from high sequencing error rate. In
this paper, we show that the error rate can be reduced by improving the base
calling process.
Results: We present the first open-source DNA base caller for the MinION
sequencing platform by Oxford Nanopore. By employing carefully crafted
recurrent neural networks, our tool improves the base calling accuracy compared
to the default base caller supplied by the manufacturer. This advance may
further enhance applicability of MinION for genome sequencing and various
clinical applications.
Availability: DeepNano can be downloaded at
this http URL
Contact: boza@fmph.uniba.sk

本文介绍了 DeepNano，一种用于改进 MinION 测序平台 DNA 碱基呼叫的工具，使用精心设计的递归神经网络，与制造商提供的默认碱基呼叫者相比，我们的工具提高了碱基呼叫的准确性，这一进步可能进一步提高 MinION 在基因组测序和各种临床应用方面的适用性。