Andrea Horbach


2024

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Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)
Ekaterina Kochmar | Marie Bexte | Jill Burstein | Andrea Horbach | Ronja Laarmann-Quante | Anaïs Tack | Victoria Yaneva | Zheng Yuan
Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)

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Scoring with Confidence? – Exploring High-confidence Scoring for Saving Manual Grading Effort
Marie Bexte | Andrea Horbach | Lena Schützler | Oliver Christ | Torsten Zesch
Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)

A possible way to save manual grading effort in short answer scoring is to automatically score answers for which the classifier is highly confident. We explore the feasibility of this approach in a high-stakes exam setting, evaluating three different similarity-based scoring methods, where the similarity score is a direct proxy for model confidence. The decision on an appropriate level of confidence should ideally be made before scoring a new prompt. We thus probe to what extent confidence thresholds are consistent across different datasets and prompts. We find that high-confidence thresholds vary on a prompt-to-prompt basis, and that the overall potential of increased performance at a reasonable cost of additional manual effort is limited.

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Fairness in Automated Essay Scoring: A Comparative Analysis of Algorithms on German Learner Essays from Secondary Education
Nils-Jonathan Schaller | Yuning Ding | Andrea Horbach | Jennifer Meyer | Thorben Jansen
Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)

Pursuing educational equity, particularly in writing instruction, requires that all students receive fair (i.e., accurate and unbiased) assessment and feedback on their texts. Automated Essay Scoring (AES) algorithms have so far focused on optimizing the mean accuracy of their scores and paid less attention to fair scores for all subgroups, although research shows that students receive unfair scores on their essays in relation to demographic variables, which in turn are related to their writing competence. We add to the literature arguing that AES should also optimize for fairness by presenting insights on the fairness of scoring algorithms on a corpus of learner texts in the German language and introduce the novelty of examining fairness on psychological and demographic differences in addition to demographic differences. We compare shallow learning, deep learning, and large language models with full and skewed subsets of training data to investigate what is needed for fair scoring. The results show that training on a skewed subset of higher and lower cognitive ability students shows no bias but very low accuracy for students outside the training set. Our results highlight the need for specific training data on all relevant user groups, not only for demographic background variables but also for cognitive abilities as psychological student characteristics.

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Transfer Learning of Argument Mining in Student Essays
Yuning Ding | Julian Lohmann | Nils-Jonathan Schaller | Thorben Jansen | Andrea Horbach
Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)

This paper explores the transferability of a cross-prompt argument mining model trained on argumentative essays authored by native English-speaking learners (EN-L1) across educational contexts and languages. Specifically, the adaptability of a multilingual transformer model is assessed through its application to comparable argumentative essays authored by English-as-a-foreign-language learners (EN-L2) for context transfer, and a dataset composed of essays written by native German learners (DE) for both language and task transfer. To separate language effects from educational context effects, we also perform experiments on a machine-translated version of the German dataset (DE-MT). Our findings demonstrate that, even under zero-shot conditions, a model trained on native English speakers exhibits satisfactory performance on the EN-L2/DE datasets. Machine translation does not substantially enhance this performance, suggesting that distinct writing styles across educational contexts impact performance more than language differences.

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The BEA 2024 Shared Task on the Multilingual Lexical Simplification Pipeline
Matthew Shardlow | Fernando Alva-Manchego | Riza Batista-Navarro | Stefan Bott | Saul Calderon Ramirez | Rémi Cardon | Thomas François | Akio Hayakawa | Andrea Horbach | Anna Hülsing | Yusuke Ide | Joseph Marvin Imperial | Adam Nohejl | Kai North | Laura Occhipinti | Nelson Peréz Rojas | Nishat Raihan | Tharindu Ranasinghe | Martin Solis Salazar | Sanja Štajner | Marcos Zampieri | Horacio Saggion
Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)

We report the findings of the 2024 Multilingual Lexical Simplification Pipeline shared task. We released a new dataset comprising 5,927 instances of lexical complexity prediction and lexical simplification on common contexts across 10 languages, split into trial (300) and test (5,627). 10 teams participated across 2 tracks and 10 languages with 233 runs evaluated across all systems. Five teams participated in all languages for the lexical complexity prediction task and 4 teams participated in all languages for the lexical simplification task. Teams employed a range of strategies, making use of open and closed source large language models for lexical simplification, as well as feature-based approaches for lexical complexity prediction. The highest scoring team on the combined multilingual data was able to obtain a Pearson’s correlation of 0.6241 and an ACC@1@Top1 of 0.3772, both demonstrating that there is still room for improvement on two difficult sub-tasks of the lexical simplification pipeline.

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Rainbow - A Benchmark for Systematic Testing of How Sensitive Visio-Linguistic Models are to Color Naming
Marie Bexte | Andrea Horbach | Torsten Zesch
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

With the recent emergence of powerful visio-linguistic models comes the question of how fine-grained their multi-modal understanding is. This has lead to the release of several probing datasets. Results point towards models having trouble with prepositions and verbs, but being relatively robust when it comes to color.To gauge how deep this understanding goes, we compile a comprehensive probing dataset to systematically test multi-modal alignment around color. We demonstrate how human perception influences descriptions of color and pay special attention to the extent to which this is reflected within the predictions of a visio-linguistic model. Probing a set of models with diverse properties with our benchmark confirms the superiority of models that do not rely on pre-extracted image features, and demonstrates that augmentation with too much noisy pre-training data can produce an inferior model. While the benchmark remains challenging for all models we test, the overall result pattern suggests well-founded alignment of color terms with hues. Analyses do however reveal uncertainty regarding the boundaries between neighboring color terms.

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An Extensible Massively Multilingual Lexical Simplification Pipeline Dataset using the MultiLS Framework
Matthew Shardlow | Fernando Alva-Manchego | Riza Batista-Navarro | Stefan Bott | Saul Calderon Ramirez | Rémi Cardon | Thomas François | Akio Hayakawa | Andrea Horbach | Anna Hülsing | Yusuke Ide | Joseph Marvin Imperial | Adam Nohejl | Kai North | Laura Occhipinti | Nelson Peréz Rojas | Nishat Raihan | Tharindu Ranasinghe | Martin Solis Salazar | Marcos Zampieri | Horacio Saggion
Proceedings of the 3rd Workshop on Tools and Resources for People with REAding DIfficulties (READI) @ LREC-COLING 2024

We present preliminary findings on the MultiLS dataset, developed in support of the 2024 Multilingual Lexical Simplification Pipeline (MLSP) Shared Task. This dataset currently comprises of 300 instances of lexical complexity prediction and lexical simplification across 10 languages. In this paper, we (1) describe the annotation protocol in support of the contribution of future datasets and (2) present summary statistics on the existing data that we have gathered. Multilingual lexical simplification can be used to support low-ability readers to engage with otherwise difficult texts in their native, often low-resourced, languages.

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DARIUS: A Comprehensive Learner Corpus for Argument Mining in German-Language Essays
Nils-Jonathan Schaller | Andrea Horbach | Lars Ingver Höft | Yuning Ding | Jan Luca Bahr | Jennifer Meyer | Thorben Jansen
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

In this paper, we present the DARIUS (Digital Argumentation Instruction for Science) corpus for argumentation quality on 4589 essays written by 1839 German secondary school students. The corpus is annotated according to a fine-grained annotation scheme, ranging from a broader perspective like content zones, to more granular features like argumentation coverage/reach and argumentative discourse units like claims and warrants. The features have inter-annotator agreements up to 0.83 Krippendorff’s α. The corpus and dataset are publicly available for further research in argument mining.

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EVil-Probe - a Composite Benchmark for Extensive Visio-Linguistic Probing
Marie Bexte | Andrea Horbach | Torsten Zesch
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Research probing the language comprehension of visio-linguistic models has gained traction due to their remarkable performance on various tasks. We introduce EViL-Probe, a composite benchmark that processes existing probing datasets into a unified format and reorganizes them based on the linguistic categories they probe. On top of the commonly used negative probes, this benchmark introduces positive probes to more rigorously test the robustness of models. Since the language side alone may introduce a bias models could exploit in solving the probes, we estimate the difficulty of the individual subsets with a language-only baseline. Using the benchmark to probe a set of state-of-the-art visio-linguistic models sheds light on how sensitive they are to the different linguistic categories. Results show that the benchmark is challenging for all models we probe, as their performance is around the chance baseline for many of the categories. The only category all models are able to handle relatively well are nouns. Additionally, models that use a Vision Transformer to process the images are also somewhat robust against probes targeting color and image type. Among these models, our enrichment of EViL-Probe with positive probes helps further discriminate performance, showing BLIP to be the overall best-performing model.

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When Argumentation Meets Cohesion: Enhancing Automatic Feedback in Student Writing
Yuning Ding | Omid Kashefi | Swapna Somasundaran | Andrea Horbach
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

In this paper, we investigate the role of arguments in the automatic scoring of cohesion in argumentative essays. The feature analysis reveals that in argumentative essays, the lexical cohesion between claims is more important to the overall cohesion, while the evidence is expected to be diverse and divergent. Our results show that combining features related to argument segments and cohesion features improves the performance of the automatic cohesion scoring model trained on a transformer. The cohesion score is also learned more accurately in a multi-task learning process by adding the automatic segmentation of argumentative elements as an auxiliary task. Our findings contribute to both the understanding of cohesion in argumentative writing and the development of automatic feedback.

2023

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Similarity-Based Content Scoring - A more Classroom-Suitable Alternative to Instance-Based Scoring?
Marie Bexte | Andrea Horbach | Torsten Zesch
Findings of the Association for Computational Linguistics: ACL 2023

Automatically scoring student answers is an important task that is usually solved using instance-based supervised learning. Recently, similarity-based scoring has been proposed as an alternative approach yielding similar perfor- mance. It has hypothetical advantages such as a lower need for annotated training data and better zero-shot performance, both of which are properties that would be highly beneficial when applying content scoring in a realistic classroom setting. In this paper we take a closer look at these alleged advantages by comparing different instance-based and similarity-based methods on multiple data sets in a number of learning curve experiments. We find that both the demand on data and cross-prompt performance is similar, thus not confirming the former two suggested advantages. The by default more straightforward possibility to give feedback based on a similarity-based approach may thus tip the scales in favor of it, although future work is needed to explore this advantage in practice.

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Score It All Together: A Multi-Task Learning Study on Automatic Scoring of Argumentative Essays
Yuning Ding | Marie Bexte | Andrea Horbach
Findings of the Association for Computational Linguistics: ACL 2023

When scoring argumentative essays in an educational context, not only the presence or absence of certain argumentative elements but also their quality is important. On the recently published student essay dataset PERSUADE, we first show that the automatic scoring of argument quality benefits from additional information about context, writing prompt and argument type. We then explore the different combinations of three tasks: automated span detection, type and quality prediction. Results show that a multi-task learning approach combining the three tasks outperforms sequential approaches that first learn to segment and then predict the quality/type of a segment.

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CATALPA_EduNLP at PragTag-2023
Yuning Ding | Marie Bexte | Andrea Horbach
Proceedings of the 10th Workshop on Argument Mining

This paper describes our contribution to the PragTag-2023 Shared Task. We describe and compare different approaches based on sentence classification, sentence similarity, and sequence tagging. We find that a BERT-based sentence labeling approach integrating positional information outperforms both sequence tagging and SBERT-based sentence classification. We further provide analyses highlighting the potential of combining different approaches.

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Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
Ekaterina Kochmar | Jill Burstein | Andrea Horbach | Ronja Laarmann-Quante | Nitin Madnani | Anaïs Tack | Victoria Yaneva | Zheng Yuan | Torsten Zesch
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)

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Sequence Tagging in EFL Email Texts as Feedback for Language Learners
Yuning Ding | Ruth Trüb | Johanna Fleckenstein | Stefan Keller | Andrea Horbach
Proceedings of the 12th Workshop on NLP for Computer Assisted Language Learning

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Manual and Automatic Identification of Similar Arguments in EFL Learner Essays
Ahmed Mousa | Ronja Laarmann-Quante | Andrea Horbach
Proceedings of the 12th Workshop on NLP for Computer Assisted Language Learning

2022

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LeSpell - A Multi-Lingual Benchmark Corpus of Spelling Errors to Develop Spellchecking Methods for Learner Language
Marie Bexte | Ronja Laarmann-Quante | Andrea Horbach | Torsten Zesch
Proceedings of the Thirteenth Language Resources and Evaluation Conference

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. We introduce LeSpell, a multi-lingual (English, German, Italian, and Czech) evaluation data set of spelling mistakes in context that we compiled from seven underlying learner corpora. Our experiments show that existing spellcheckers do not work well with learner data. Thus, we introduce a highly customizable spellchecking component for the DKPro architecture, which improves performance in many settings.

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Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
Ekaterina Kochmar | Jill Burstein | Andrea Horbach | Ronja Laarmann-Quante | Nitin Madnani | Anaïs Tack | Victoria Yaneva | Zheng Yuan | Torsten Zesch
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

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Similarity-Based Content Scoring - How to Make S-BERT Keep Up With BERT
Marie Bexte | Andrea Horbach | Torsten Zesch
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

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. An alternative approach that receives much less attention is however to learn a similarity-based model. We introduce an architecture that efficiently learns a similarity model and find that results on the standard ASAP dataset are on par with a BERT-based classification approach.

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Don’t Drop the Topic - The Role of the Prompt in Argument Identification in Student Writing
Yuning Ding | Marie Bexte | Andrea Horbach
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

In this paper, we explore the role of topic information in student essays from an argument mining perspective. We cluster a recently released corpus through topic modeling into prompts and train argument identification models on different data settings. Results show that, given the same amount of training data, prompt-specific training performs better than cross-prompt training. However, the advantage can be overcome by introducing large amounts of cross-prompt training data.

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‘Meet me at the ribary’ – Acceptability of spelling variants in free-text answers to listening comprehension prompts
Ronja Laarmann-Quante | Leska Schwarz | Andrea Horbach | Torsten Zesch
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

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. When judging whether a variant is acceptable or not, human raters perform a complex holistic decision. In this paper, we present a corpus study in which we analyze human acceptability decisions in a high stakes test for German. We show that for human experts, spelling variants are harder to score consistently than other answer variants. Furthermore, we examine how the decision can be operationalized using features that could be applied by an automatic scoring system. We show that simple measures like edit distance and phonetic similarity between a given answer and the target answer can model the human acceptability decisions with the same inter-annotator agreement as humans, and discuss implications of the remaining inconsistencies.

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Bringing Automatic Scoring into the Classroom - Measuring the Impact of Automated Analytic Feedback on Student Writing Performance
Andrea Horbach | Ronja Laarmann-Quante | Lucas Liebenow | Thorben Jansen | Stefan Keller | Jennifer Meyer | Torsten Zesch | Johanna Fleckenstein
Proceedings of the 11th Workshop on NLP for Computer Assisted Language Learning

2021

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Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications
Jill Burstein | Andrea Horbach | Ekaterina Kochmar | Ronja Laarmann-Quante | Claudia Leacock | Nitin Madnani | Ildikó Pilán | Helen Yannakoudakis | Torsten Zesch
Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications

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C-Test Collector: A Proficiency Testing Application to Collect Training Data for C-Tests
Christian Haring | Rene Lehmann | Andrea Horbach | Torsten Zesch
Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications

We present the C-Test Collector, a web-based tool that allows language learners to test their proficiency level using c-tests. Our tool collects anonymized data on test performance, which allows teachers to gain insights into common error patterns. At the same time, it allows NLP researchers to collect training data for being able to generate c-test variants at the desired difficulty level.

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Implicit Phenomena in Short-answer Scoring Data
Marie Bexte | Andrea Horbach | Torsten Zesch
Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language

Short-answer scoring is the task of assessing the correctness of a short text given as response to a question that can come from a variety of educational scenarios. As only content, not form, is important, the exact wording including the explicitness of an answer should not matter. However, many state-of-the-art scoring models heavily rely on lexical information, be it word embeddings in a neural network or n-grams in an SVM. Thus, the exact wording of an answer might very well make a difference. We therefore quantify to what extent implicit language phenomena occur in short answer datasets and examine the influence they have on automatic scoring performance. We find that the level of implicitness depends on the individual question, and that some phenomena are very frequent. Resolving implicit wording to explicit formulations indeed tends to improve automatic scoring performance.

2020

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Chinese Content Scoring: Open-Access Datasets and Features on Different Segmentation Levels
Yuning Ding | Andrea Horbach | Torsten Zesch
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

In this paper, we analyse the challenges of Chinese content scoring in comparison to English. 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. The Chinese Educational Short Answers data set (CESA) contains 1800 student answers for five science-related questions. As a second data set, we collected ASAP-ZH with 942 answers by re-using three existing prompts from the ASAP data set. We adapt a state-of-the-art content scoring system for Chinese and evaluate it in several settings on these data sets. Results show that features on lower segmentation levels such as character n-grams tend to have better performance than features on token level.

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Linguistic Appropriateness and Pedagogic Usefulness of Reading Comprehension Questions
Andrea Horbach | Itziar Aldabe | Marie Bexte | Oier Lopez de Lacalle | Montse Maritxalar
Proceedings of the Twelfth Language Resources and Evaluation Conference

Automatic generation of reading comprehension questions is a topic receiving growing interest in the NLP community, but there is currently no consensus on evaluation metrics and many approaches focus on linguistic quality only while ignoring the pedagogic value and appropriateness of questions. This paper overcomes such weaknesses by a new evaluation scheme where questions from the questionnaire are structured in a hierarchical way to avoid confronting human annotators with evaluation measures that do not make sense for a certain question. We show through an annotation study that our scheme can be applied, but that expert annotators with some level of expertise are needed. We also created and evaluated two new evaluation data sets from the biology domain for Basque and German, composed of questions written by people with an educational background, which will be publicly released. Results show that manually generated questions are in general both of higher linguistic as well as pedagogic quality and that among the human generated questions, teacher-generated ones tend to be most useful.

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Don’t take “nswvtnvakgxpm” for an answer –The surprising vulnerability of automatic content scoring systems to adversarial input
Yuning Ding | Brian Riordan | Andrea Horbach | Aoife Cahill | Torsten Zesch
Proceedings of the 28th International Conference on Computational Linguistics

Automatic content scoring systems are widely used on short answer tasks to save human effort. However, the use of these systems can invite cheating strategies, such as students writing irrelevant answers in the hopes of gaining at least partial credit. We generate adversarial answers for benchmark content scoring datasets based on different methods of increasing sophistication and show that even simple methods lead to a surprising decrease in content scoring performance. As an extreme example, up to 60% of adversarial answers generated from random shuffling of words in real answers are accepted by a state-of-the-art scoring system. In addition to analyzing the vulnerabilities of content scoring systems, we examine countermeasures such as adversarial training and show that these measures improve system robustness against adversarial answers considerably but do not suffice to completely solve the problem.

2018

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Cross-Lingual Content Scoring
Andrea Horbach | Sebastian Stennmanns | Torsten Zesch
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

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. Cross-lingual scoring can contribute to educational equality by allowing answers in multiple languages. Training a model in one language and applying it to another language might also help to overcome data sparsity issues by re-using trained models from other languages. As there is no suitable dataset available for this new task, we create a comparable bi-lingual corpus by extending the English ASAP dataset with German answers. Our experiments with cross-lingual scoring based on machine-translating either training or test data show a considerable drop in scoring quality.

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ESCRITO - An NLP-Enhanced Educational Scoring Toolkit
Torsten Zesch | Andrea Horbach
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Semi-Supervised Clustering for Short Answer Scoring
Andrea Horbach | Manfred Pinkal
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Investigating neural architectures for short answer scoring
Brian Riordan | Andrea Horbach | Aoife Cahill | Torsten Zesch | Chong Min Lee
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Neural approaches to automated essay scoring have recently shown state-of-the-art performance. The automated essay scoring task typically involves a broad notion of writing quality that encompasses content, grammar, organization, and conventions. This differs from the short answer content scoring task, which focuses on content accuracy. The inputs to neural essay scoring models – ngrams and embeddings – are arguably well-suited to evaluate content in short answer scoring tasks. We investigate how several basic neural approaches similar to those used for automated essay scoring perform on short answer scoring. We show that neural architectures can outperform a strong non-neural baseline, but performance and optimal parameter settings vary across the more diverse types of prompts typical of short answer scoring.

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Fine-grained essay scoring of a complex writing task for native speakers
Andrea Horbach | Dirk Scholten-Akoun | Yuning Ding | Torsten Zesch
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Automatic essay scoring is nowadays successfully used even in high-stakes tests, but this is mainly limited to holistic scoring of learner essays. We present a new dataset of essays written by highly proficient German native speakers that is scored using a fine-grained rubric with the goal to provide detailed feedback. Our experiments with two state-of-the-art scoring systems (a neural and a SVM-based one) show a large drop in performance compared to existing datasets. This demonstrates the need for such datasets that allow to guide research on more elaborate essay scoring methods.

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The Influence of Spelling Errors on Content Scoring Performance
Andrea Horbach | Yuning Ding | Torsten Zesch
Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017)

Spelling errors occur frequently in educational settings, but their influence on automatic scoring is largely unknown. We therefore investigate the influence of spelling errors on content scoring performance using the example of the ASAP corpus. We conduct an annotation study on the nature of spelling errors in the ASAP dataset and utilize these finding in machine learning experiments that measure the influence of spelling errors on automatic content scoring. Our main finding is that scoring methods using both token and character n-gram features are robust against spelling errors up to the error frequency in ASAP.

2016

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Investigating Active Learning for Short-Answer Scoring
Andrea Horbach | Alexis Palmer
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications

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UdS-(retrain|distributional|surface): Improving POS Tagging for OOV Words in German CMC and Web Data
Jakob Prange | Andrea Horbach | Stefan Thater
Proceedings of the 10th Web as Corpus Workshop

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Improving POS Tagging of German Learner Language in a Reading Comprehension Scenario
Lena Keiper | Andrea Horbach | Stefan Thater
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present a novel method to automatically improve the accurracy of part-of-speech taggers on learner language. The key idea underlying our approach is to exploit the structure of a typical language learner task and automatically induce POS information for out-of-vocabulary (OOV) words. To evaluate the effectiveness of our approach, we add manual POS and normalization information to an existing language learner corpus. Our evaluation shows an increase in accurracy from 72.4% to 81.5% on OOV words.

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A Corpus of Literal and Idiomatic Uses of German Infinitive-Verb Compounds
Andrea Horbach | Andrea Hensler | Sabine Krome | Jakob Prange | Werner Scholze-Stubenrecht | Diana Steffen | Stefan Thater | Christian Wellner | Manfred Pinkal
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present an annotation study on a representative dataset of literal and idiomatic uses of German infinitive-verb compounds in newspaper and journal texts. Infinitive-verb compounds form a challenge for writers of German, because spelling regulations are different for literal and idiomatic uses. Through the participation of expert lexicographers we were able to obtain a high-quality corpus resource which offers itself as a testbed for automatic idiomaticity detection and coarse-grained word-sense disambiguation. We trained a classifier on the corpus which was able to distinguish literal and idiomatic uses with an accuracy of 85 %.

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Unsupervised Ranked Cross-Lingual Lexical Substitution for Low-Resource Languages
Stefan Ecker | Andrea Horbach | Stefan Thater
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We propose an unsupervised system for a variant of cross-lingual lexical substitution (CLLS) to be used in a reading scenario in computer-assisted language learning (CALL), in which single-word translations provided by a dictionary are ranked according to their appropriateness in context. In contrast to most alternative systems, ours does not rely on either parallel corpora or machine translation systems, making it suitable for low-resource languages as the language to be learned. This is achieved by a graph-based scoring mechanism which can deal with ambiguous translations of context words provided by a dictionary. Due to this decoupling from the source language, we need monolingual corpus resources only for the target language, i.e. the language of the translation candidates. We evaluate our approach for the language pair Norwegian Nynorsk-English on an exploratory manually annotated gold standard and report promising results. When running our system on the original SemEval CLLS task, we rank 6th out of 18 (including 2 baselines and our 2 system variants) in the best evaluation.

2015

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Using Shallow Syntactic Features to Measure Influences of L1 and Proficiency Level in EFL Writings
Andrea Horbach | Jonathan Poitz | Alexis Palmer
Proceedings of the fourth workshop on NLP for computer-assisted language learning

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Annotating Entailment Relations for Shortanswer Questions
Simon Ostermann | Andrea Horbach | Manfred Pinkal
Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications

2014

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Finding a Tradeoff between Accuracy and Rater’s Workload in Grading Clustered Short Answers
Andrea Horbach | Alexis Palmer | Magdalena Wolska
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

n this paper we investigate the potential of answer clustering for semi-automatic scoring of short answer questions for German as a foreign language. We use surface features like word and character n-grams to cluster answers to listening comprehension exercises per question and simulate having human graders only label one answer per cluster and then propagating this label to all other members of the cluster. We investigate various ways to select this single item to be labeled and find that choosing the item closest to the centroid of a cluster leads to improved (simulated) grading accuracy over random item selection. Averaged over all questions, we can reduce a teacher’s workload to labeling only 40% of all different answers for a question, while still maintaining a grading accuracy of more than 85%.

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Paraphrase Detection for Short Answer Scoring
Nikolina Koleva | Andrea Horbach | Alexis Palmer | Simon Ostermann | Manfred Pinkal
Proceedings of the third workshop on NLP for computer-assisted language learning

2013

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Using the text to evaluate short answers for reading comprehension exercises
Andrea Horbach | Alexis Palmer | Manfred Pinkal
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity