Acronyms are abbreviated units of a phrase constructed by using initial components of the phrase in a text. Automatic extraction of acronyms from a text can help various Natural Language Processing tasks like machine translation, information retrieval, and text summarisation. This paper discusses an ensemble approach for the task of Acronym Extraction, which utilises two different methods to extract acronyms and their corresponding long forms. The first method utilises a multilingual contextual language model and fine-tunes the model to perform the task. The second method relies on a convolutional neural network architecture to extract acronyms and append them to the output of the previous method. We also augment the official training dataset with additional training samples extracted from several open-access journals to help improve the task performance. Our dataset analysis also highlights the noise within the current task dataset. Our approach achieves the following macro-F1 scores on test data released with the task: Danish (0.74), English-Legal (0.72), English-Scientific (0.73), French (0.63), Persian (0.57), Spanish (0.65), Vietnamese (0.65). We release our code and models publicly.
翻译:缩略语是使用文本中短语的初始组成部分构建的短语缩略语单位。 从文本中自动提取缩略语可以帮助各种自然语言处理任务,如机器翻译、信息检索和文本摘要。本文讨论Acronym Expliton的任务的混合方法,该方法使用两种不同的方法提取缩略语及其相应的长式。第一种方法使用多种语言背景语言模型,并精细描述执行这项任务的模式。第二种方法依靠一个革命神经网络结构提取缩略语并将其附加到先前方法的产出中。我们还利用从几个开放的期刊提取的额外培训样本来增加官方培训数据集,以帮助改进任务性能。我们的数据集分析还突出当前任务数据集中的噪音。我们的方法在随任务释放的测试数据上取得了以下宏观-F1分数:丹麦语(0.74)、英语-法律(0.72)、英语-科学(0.73)、法语(0.63)、波斯语(0.57)、西班牙语(0.65)、越南语(0.65)。我们公开发布我们的代码和模型。