Recent NLP architectures have illustrated in various ways how semantic change can be captured across time and domains. However, in terms of evaluation there is a lack of benchmarks to compare the performance of these systems against each other. We present the results of the first shared task on unsupervised lexical semantic change detection (LSCD) in German based on the evaluation framework proposed by Schlechtweg et al. (2019).
University of Stuttgart（斯图加特大学）；University of Helsinki（赫尔辛基大学）；University of Geneva（日内瓦大学）；The Alan Turing Institute（艾伦·图灵研究所）；University of Cambridge（剑桥大学）；University of Gothenburg（哥德堡大学）
Lexical Semantic Change detection, i.e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics. Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress. We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish. 33 teams submitted 186 systems,…
University of St.Gallen（圣加仑大学）；University of Passau（帕绍大学）；University of Kassel（卡塞尔大学）
In this paper, we present a novel annotation approach to capture claims and premises of arguments and their relations in student-written persuasive peer reviews on business models in German language. We propose an annotation scheme based on annotation guidelines that allows to model claims and premises as well as support and attack relations for capturing the structure of argumentative discourse in student-written peer reviews. We conduct an annotation study with three annotators on 50 persuasive essays to evaluate our annotation scheme. The obtained inter-rater agreement of $α=0.57$ for argum…
How Do Lexical Senses Correspond Between Spoken German and German Sign Language?
Institute for Computational Linguistics, University of Heidelberg（计算语言学研究所，海德堡大学）；Department of Computing Science, University of Aberdeen（计算机科学系，阿伯丁大学）
The usefulness of a statistical approach suggested by Church et al. (1991) is evaluated for the extraction of verb-noun (V-N) collocations from German text corpora. Some problematic issues of that method arising from properties of the German language are discussed and various modifications of the method are considered that might improve extraction results for German. The precision and recall of all variant methods is evaluated for V-N collocations containing support verbs, and the consequences for further work on the extraction of collocations from German corpora are discussed. With a sufficie…
Alice: A Large-Scale German Benchmark for Rubric-Based Multi-Dimensional Automatic Short Answer Scoring
DIPF | Leibniz Institute for Research and Information in Education（DIPF | 莱布尼茨教育与信息研究所）；IPN | Leibniz Institute for Science and Mathematics Education（IPN | 莱布尼茨科学与数学教育研究所）；Umeå University（于默奥大学）；Goethe University Frankfurt（法兰克福大学）
Essays as a form of assessment test student knowledge on a deeper level than short answer and multiple-choice questions. However, the manual evaluation of essays is time- and labor-consuming. Automatic clustering of essays, or their fragments, prior to manual evaluation presents a possible solution to reducing the effort required in the evaluation process. Such clustering presents numerous challenges due to the variability and ambiguity of natural language. In this paper, we introduce two datasets of undergraduate student essays in Finnish, manually annotated for salient arguments on the sente…
We propose a framework that extends synchronic polysemy annotation to diachronic changes in lexical meaning, to counteract the lack of resources for evaluating computational models of lexical semantic change. Our framework exploits an intuitive notion of semantic relatedness, and distinguishes between innovative and reductive meaning changes with high inter-annotator agreement. The resulting test set for German comprises ratings from five annotators for the relatedness of 1,320 use pairs across 22 target words.
University of Stuttgart（斯图加特大学）；Leibniz Institute for the German Language（莱布尼茨德语研究所）
While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models. In this paper, we propose a shift of focus from change detection to change discovery, i.e., discovering novel word senses over time from the full corpus vocabulary. By heavily fine-tuning a type-based and a token-based approach on recently published German data, we demonstrate that both models can successfully be applied to discover new words undergoing meaning change. Furthermore, we provide an almost fully automated fra…
Inria Saclay（法国国家信息与自动化研究所萨克雷研究中心）；Humboldt Universität zu Berlin（柏林洪堡大学）；Universität Heidelberg（海德堡大学）
In recent years, new developments in the area of lexicography have altered not only the management, processing and publishing of lexicographical data, but also created new types of products such as electronic dictionaries and thesauri. These expand the range of possible uses of lexical data and support users with more flexibility, for instance in assisting human translation. In this article, we give a short and easy-to-understand introduction to the problematic nature of the storage, display and interpretation of lexical data. We then describe the main methods and specifications used to build …
University of St.Gallen（圣加仑大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Passau（帕绍大学）；University of Kassel（卡塞尔大学）
We present an annotation approach to capturing emotional and cognitive empathy in student-written peer reviews on business models in German. We propose an annotation scheme that allows us to model emotional and cognitive empathy scores based on three types of review components. Also, we conducted an annotation study with three annotators based on 92 student essays to evaluate our annotation scheme. The obtained inter-rater agreement of α=0.79 for the components and the multi-π=0.41 for the empathy scores indicate that the proposed annotation scheme successfully guides annotators to a substanti…
University of Toronto（多伦多大学）；Carnegie Mellon University（卡内基梅隆大学）
We adopt an evolutionary view on language change in which cognitive factors (in addition to social ones) affect the fitness of words and their success in the linguistic ecosystem. Specifically, we propose a variety of psycholinguistic factors -- semantic, distributional, and phonological -- that we hypothesize are predictive of lexical decline, in which words greatly decrease in frequency over time. Using historical data across three languages (English, French, and German), we find that most of our proposed factors show a significant difference in the expected direction between each curated se…
Technical University of Darmstadt（达姆施塔特工业大学）；Johannes Gutenberg-University Mainz（美因茨约翰内斯·古腾堡大学）
This work investigates the use of interactively updated label suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data. We develop guidelines to conduct a controlled annotation study with social science students and find that suggestions from a model trained on a small, expert-annotated dataset already lead to a substantial improvement - in terms of inter-annotator agreement(+.14 Fleiss' $κ$) and annotation quality - compared to students that do not receive any label suggestions. We further find that label suggestion…
University of West Bohemia（西波希米亚大学）；NTIS – New Technologies for the Information Society（NTIS——信息社会新技术研究中心）
In this paper, we describe our method for the detection of lexical semantic change, i.e., word sense changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: \textit{Unsupervised Lexical Semantic Change Detection.} We ranked $1^{st}$ in Sub-task 1: binary change detection, and $4^{th}$ in Sub-task 2: ranked change detection. Our method is fully unsupervised and language independent. It consists of preparing a semantic vector space for…
We introduce a neural network-based system of Word Sense Disambiguation (WSD) for German that is based on SenseFitting, a novel method for optimizing WSD. We outperform knowledge-based WSD methods by up to 25% F1-score and produce a new state-of-the-art on the German sense-annotated dataset WebCAGe. Our method uses three feature vectors consisting of a) sense, b) gloss, and c) relational vectors to represent target senses and to compare them with the vector centroids of sample contexts. Utilizing widely available word embeddings and lexical resources, we are able to compensate for the lower re…
This paper describes EmbLexChange, a system introduced by the "Life-Language" team for SemEval-2020 Task 1, on unsupervised detection of lexical-semantic changes. EmbLexChange is defined as the divergence between the embedding based profiles of word w (calculated with respect to a set of reference words) in the source and the target domains (source and target domains can be simply two time frames t1 and t2). The underlying assumption is that the lexical-semantic change of word w would affect its co-occurring words and subsequently alters the neighborhoods in the embedding spaces. We show that …
This article considers the task of automatically inducing role-semantic annotations in the FrameNet paradigm for new languages. We propose a general framework that is based on annotation projection, phrased as a graph optimization problem. It is relatively inexpensive and has the potential to reduce the human effort involved in creating role-semantic resources. Within this framework, we present projection models that exploit lexical and syntactic information. We provide an experimental evaluation on an English-German parallel corpus which demonstrates the feasibility of inducing high-precision…
Indiana University（印第安纳大学）；Rutgers University（罗格斯大学）；University of Pittsburgh（匹兹堡大学）；University of Edinburgh（爱丁堡大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）
Typologically diverse languages offer systems of lexical and grammatical aspect that allow speakers to focus on facets of event structure in ways that comport with the specific communicative setting and discourse constraints they face. In this paper, we look specifically at captions of images across Arabic, Chinese, Farsi, German, Russian, and Turkish and describe a computational model for predicting lexical aspects. Despite the heterogeneity of these languages, and the salient invocation of distinctive linguistic resources across their caption corpora, speakers of these languages show surpris…
Ottawa-Carleton Institute for Computer Science（渥太华-卡尔顿计算机科学研究所）；School of Information Technology and Engineering（信息技术与工程学院）；University of Ottawa（渥太华大学）
WordNet proved that it is possible to construct a large-scale electronic lexical database on the principles of lexical semantics. It has been accepted and used extensively by computational linguists ever since it was released. Inspired by WordNet's success, we propose as an alternative a similar resource, based on the 1987 Penguin edition of Roget's Thesaurus of English Words and Phrases. Peter Mark Roget published his first Thesaurus over 150 years ago. Countless writers, orators and students of the English language have used it. Computational linguists have employed Roget's for almost 50 yea…
The evaluation of question answering models compares ground-truth annotations with model predictions. However, as of today, this comparison is mostly lexical-based and therefore misses out on answers that have no lexical overlap but are still semantically similar, thus treating correct answers as false. This underestimation of the true performance of models hinders user acceptance in applications and complicates a fair comparison of different models. Therefore, there is a need for an evaluation metric that is based on semantics instead of pure string similarity. In this short paper, we present…
This paper describes "TLT-school" a corpus of speech utterances collected in schools of northern Italy for assessing the performance of students learning both English and German. The corpus was recorded in the years 2017 and 2018 from students aged between nine and sixteen years, attending primary, middle and high school. All utterances have been scored, in terms of some predefined proficiency indicators, by human experts. In addition, most of utterances recorded in 2017 have been manually transcribed carefully. Guidelines and procedures used for manual transcriptions of utterances will be des…
Large Language Models for Dysfluency Detection in Stuttered Speech
Technische Hochschule Nürnberg Georg Simon Ohm（纽伦堡乔治·西蒙·欧姆应用技术大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Intel Labs（英特尔实验室）
本文将多标签口吃不流利检测建模为语言建模问题，向 LLM 输入 ASR 假设候选与音频编码器声学表征并微调，在英语、德语数据集上取得有竞争力的结果。
