There is an ongoing debate on whether neural networks can grasp the quasi-regularities in languages like humans. In a typical quasi-regularity task, English past tense inflections, the neural network model has long been criticized that it learns only to generalize the most frequent pattern, but not the regular pattern, thus can not learn the abstract categories of regular and irregular and is dissimilar to human performance. In this work, we train a set of transformer models with different settings to examine their behavior on this task. The models achieved high accuracy on unseen regular verbs and some accuracy on unseen irregular verbs. The models' performance on the regulars is heavily affected by type frequency and ratio but not token frequency and ratio, and vice versa for the irregulars. The different behaviors on the regulars and irregulars suggest that the models have some degree of symbolic learning on the regularity of the verbs. In addition, the models are weakly correlated with human behavior on nonce verbs. Although the transformer model exhibits some level of learning on the abstract category of verb regularity, its performance does not fit human data well, suggesting that it might not be a good cognitive model.
Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding
Salesforce AI Research（赛富时人工智能研究院）；National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Unsupervised Inflection Generation Using Neural Language Modeling
Human Language Technologies Research Center, University of Bucharest（布加勒斯特大学人类语言技术研究中心）；GumGum（GumGum公司）；University of Bucharest, Faculty of Mathematics and Computer Science（布加勒斯特大学数学与计算机科学学院）
Evaluating Multilingual Sentence Embeddings for Translation Error Detection:An English--Greek Contrastive Study
Ionian University（爱奥尼亚大学）；Laboratory on Digital Libraries and Electronic Publishing（数字图书馆与电子出版实验室）；Centre for Literary Translation and Book Policy（文学翻译与图书政策中心）
Emergent morpho-phonological representations in self-supervised speech models
Department of Neurological Surgery, University of California, San Francisco（加州大学旧金山分校神经外科系）；Department of Linguistics, University of Southern California（南加州大学语言学系）；Center for Computational Language Sciences, University of Southern California（南加州大学计算语言科学中心）
Inflecting when there's no majority: Limitations of encoder-decoder neural networks as cognitive models for German plurals
Institute for Language, Cognition and Computation（语言、认知与计算研究所）；School of Informatics（信息学院）；University of Edinburgh（爱丁堡大学）
人工神经网络能像人类说话者那样学会表示屈折形态并概括为新词吗？Kirov和Cotterell（2018）认为答案是肯定的：现代编码器-解码器（ED）架构在改变英语动词时学习类似人类的行为，例如将常规的过去时态形式-（e）d扩展到新单词。然而，他们的工作并没有解决Marcus et al.（1995）提出的批评：神经模型可能会学习扩展不是常规的，而是最常见的类-因此在德国数字变形等任务上失败，其中不常见的后缀如-s仍然可以有效地推广。为了研究这个问题，我们首先收集了一个新的数据集从德语（生产和评级的复数形式的新名词），旨在避免信息来源不可用的ED模型。说话人数据显示出高变异性，两个后缀表明“规则”的行为，出现更频繁的语音非典型输入。编码器-解码器模型确实概括了最频繁产生的复数类，但没有显示出这些其他复数标记的类似人类的可变性或“规则”扩展。我们的结论是，现代神经模型可能仍在努力解决少数群体的泛化问题。
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
University of Melbourne（墨尔本大学）；University of Cambridge（剑桥大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Higher School of Economics（高等经济学院）；Moscow State University（莫斯科国立大学）；Karelian Research Centre（卡累利阿研究中心）；Indiana University（印第安纳大学）；Google AI（谷歌人工智能）；University of British Columbia（不列颠哥伦比亚大学）；Facebook AI Research（Facebook人工智能研究院）；Carnegie Mellon University（卡内基梅隆大学）；University of Louisville（路易斯维尔大学）；University of York（约克大学）；ETH Zürich（苏黎世联邦理工学院）；University of Colorado Boulder（科罗拉多大学博尔德分校）
Internal and external dynamics in language: Evidence from verb regularity in a historical corpus of English
Institute for Complex Systems (ISC-CNR)（国家研究委员会复杂系统研究所（ISC-CNR））；Sapienza University of Rome（罗马大学（Sapienza））；Institute for Scientific Interchange (ISI)（科学交流研究所（ISI））
École de Technologie Supérieure（高等技术学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Amazon（亚马逊）；Facebook（脸书）；Google（谷歌）；Microsoft（微软）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；National Research University Higher School of Economics (HSE University)（国立研究型大学高等经济学院（HSE大学））；Indiana University Bloomington（印第安纳大学伯明顿分校）；Carnegie Mellon University（卡内基梅隆大学）
Classifying Dominant Temporal Orientation without Pretrained Text Embeddings: A Novel Morphosyntactic Inventory Vector Approach
Interdisciplinary Center for Innovative Theory and Empirics, Columbia University（哥伦比亚大学创新理论与实证跨学科中心）；Department of Sociology, Columbia University（哥伦比亚大学社会学系）
