Search Results for author:
Found 67 papers, 9 papers with code
LeSpell - A Multi-Lingual Benchmark Corpus of Spelling Errors to Develop Spellchecking Methods for Learner Language
Spellchecking text written by language learners is especially challenging because errors made by learners differ both quantitatively and qualitatively from errors made by already proficient learners.
Improving Generalization of Hate Speech Detection Systems to Novel Target Groups via Domain Adaptation
Despite recent advances in machine learning based hate speech detection, classifiers still struggle with generalizing knowledge to out-of-domain data samples.
Implicit Phenomena in Short-answer Scoring Data
We therefore quantify to what extent implicit language phenomena occur in short answer datasets and examine the influence they have on automatic scoring performance.
A Crash Course on Ethics for Natural Language Processing
It is generally agreed upon in the natural language processing (NLP) community that ethics should be integrated into any curriculum.
Analyzing the Real Vulnerability of Hate Speech Detection Systems against Targeted Intentional Noise
Hate speech detection systems have been shown to be vulnerable against obfuscation attacks, where a potential hater tries to circumvent detection by deliberately introducing noise in their posts.
‘Meet me at the ribary’ – Acceptability of spelling variants in free-text answers to listening comprehension prompts
When listening comprehension is tested as a free-text production task, a challenge for scoring the answers is the resulting wide range of spelling variants.
Similarity-Based Content Scoring - How to Make S-BERT Keep Up With BERT
The dominating paradigm for content scoring is to learn an instance-based model, i. e. to use lexical features derived from the learner answers themselves.
C-Test Collector: A Proficiency Testing Application to Collect Training Data for C-Tests
We present the C-Test Collector, a web-based tool that allows language learners to test their proficiency level using c-tests.
VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes
This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021.
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa.
Text or Image? What is More Important in Cross-Domain Generalization Capabilities of Hate Meme Detection Models?
This paper delves into the formidable challenge of cross-domain generalization in multimodal hate meme detection, presenting compelling findings.
HateProof: Are Hateful Meme Detection Systems really Robust?
Empirically, we find a noticeable performance drop of as high as 10% in the macro-F1 score for certain attacks.
Robustness of end-to-end Automatic Speech Recognition Models -- A Case Study using Mozilla DeepSpeech
When evaluating the performance of automatic speech recognition models, usually word error rate within a certain dataset is used.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Effects of Layer Freezing on Transferring a Speech Recognition System to Under-resourced Languages
In this paper, we investigate the effect of layer freezing on the effectiveness of model transfer in the area of automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Don't take ``nswvtnvakgxpm'' for an answer --The surprising vulnerability of automatic content scoring systems to adversarial input
Automatic content scoring systems are widely used on short answer tasks to save human effort.
Chinese Content Scoring: Open-Access Datasets and Features on Different Segmentation Levels
As a review of prior work for Chinese content scoring shows a lack of open-access data in the field, we present two short-answer data sets for Chinese.
Decomposing and Comparing Meaning Relations: Paraphrasing, Textual Entailment, Contradiction, and Specificity
We use the typology to annotate a corpus of 520 sentence pairs in English and we demonstrate that unlike previous typologies, SHARel can be applied to all relations of interest with a high inter-annotator agreement.
A Legal Approach to Hate Speech -- Operationalizing the EU's Legal Framework against the Expression of Hatred as an NLP Task
We propose a 'legal approach' to hate speech detection by operationalization of the decision as to whether a post is subject to criminal law into an NLP task.
Divide and Extract -- Disentangling Clause Splitting and Proposition Extraction
The resulting proposition evaluation dataset allows us to independently compare the performance of proposition extraction systems on simple and complex clauses.
Annotating and analyzing the interactions between meaning relations
Here we present a corpus annotated with these relations and the analysis of these results.
ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter
On the Spanish data our system is ranked 25th out of 39.
LTL-UDE at SemEval-2019 Task 6: BERT and Two-Vote Classification for Categorizing Offensiveness
We present results for Subtask A and C of SemEval 2019 Shared Task 6.
From legal to technical concept: Towards an automated classification of German political Twitter postings as criminal offenses
In this article, we analyze which Twitter posts could actually be deemed offenses under German criminal law.
Cross-Lingual Content Scoring
We investigate the feasibility of cross-lingual content scoring, a scenario where training and test data in an automatic scoring task are from two different languages.
Agree or Disagree: Predicting Judgments on Nuanced Assertions
Being able to predict whether people agree or disagree with an assertion (i. e. an explicit, self-contained statement) has several applications ranging from predicting how many people will like or dislike a social media post to classifying posts based on whether they are in accordance with a particular point of view.
The Influence of Spelling Errors on Content Scoring Performance
Spelling errors occur frequently in educational settings, but their influence on automatic scoring is largely unknown.
Fine-grained essay scoring of a complex writing task for native speakers
Automatic essay scoring is nowadays successfully used even in high-stakes tests, but this is mainly limited to holistic scoring of learner essays.
Investigating neural architectures for short answer scoring
Neural approaches to automated essay scoring have recently shown state-of-the-art performance.
Same same, but different: Compositionality of paraphrase granularity levels
Paraphrases exist on different granularity levels, the most frequently used one being the sentential level.
Do LSTMs really work so well for PoS tagging? -- A replication study
A recent study by Plank et al. (2016) found that LSTM-based PoS taggers considerably improve over the current state-of-the-art when evaluated on the corpora of the Universal Dependencies project that use a coarse-grained tagset.
Assigning Fine-grained PoS Tags based on High-precision Coarse-grained Tagging
We propose a new approach to PoS tagging where in a first step, we assign a coarse-grained tag corresponding to the main syntactic category.
Predicting proficiency levels in learner writings by transferring a linguistic complexity model from expert-written coursebooks
The lack of a sufficient amount of data tailored for a task is a well-recognized problem for many statistical NLP methods.
FlexTag: A Highly Flexible PoS Tagging Framework
We present FlexTag, a highly flexible PoS tagging framework.
Predicting the Difficulty of Language Proficiency Tests
Language proficiency tests are used to evaluate and compare the progress of language learners.
