In this work, we present the first corpus for German Adverse Drug Reaction
(ADR) detection in patient-generated content. The data consists of 4,169 binary
annotated documents from a German patient forum, where users talk about health
issues and get advice from medical doctors. As is common in social media data
in this domain, the class labels of the corpus are very imbalanced. This and a
high topic imbalance make it a very challenging dataset, since often, the same
symptom can have several causes and is not always related to a medication
intake. We aim to encourage further multi-lingual efforts in the domain of ADR
detection and provide preliminary experiments for binary classification using
different methods of zero- and few-shot learning based on a multi-lingual
model. When fine-tuning XLM-RoBERTa first on English patient forum data and
then on the new German data, we achieve an F1-score of 37.52 for the positive
class. We make the dataset and models publicly available for the community.

本文首次提出了一个针对德国患者提供的 Adverse Drug Reaction 侦测语料库，包含了德语患者论坛中 4169 个二进制标注文件，并呈现了这个社交媒体数据的课程和主题的不平衡性，通过使用 XLM-RoBERTa 在英语和德语患者论坛数据之前进行优化，最终实现了一个 37.52 的二元分类 F1 - 得分。

从患者角度探讨检测德语不良药物反应的跨语言方法

Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective

Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively
new direction in speech research. The approach benefits by performing model
training without using lexicon and alignments. However, this poses a new
problem of requiring more data compared to conventional DNN-HMM systems. In
this work, we attempt to use data from 10 BABEL languages to build a
multi-lingual seq2seq model as a prior model, and then port them towards 4
other BABEL languages using transfer learning approach. We also explore
different architectures for improving the prior multilingual seq2seq model. The
paper also discusses the effect of integrating a recurrent neural network
language model (RNNLM) with a seq2seq model during decoding. Experimental
results show that the transfer learning approach from the multilingual model
shows substantial gains over monolingual models across all 4 BABEL languages.
Incorporating an RNNLM also brings significant improvements in terms of %WER,
and achieves recognition performance comparable to the models trained with
twice more training data.

本文提出使用多语言数据建立先前模型，结合迁移学习的方法，通过在 10 个 BABEL 语言中训练 seq2seq 模型来改进音频识别，同时在解码时加入循环神经网络语言模型 (RNNLM) 来进一步提高模型的表现，实验结果表明，将先前的多语言模型应用于 4 种其他 BABEL 语言可以有效提高识别的准确度，并且加入 RNNLM 辅助也可以带来显著的性能提升。