Sigrid Klerke


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Lexical Resources for Low-Resource PoS Tagging in Neural Times
Barbara Plank | Sigrid Klerke
Proceedings of the 22nd Nordic Conference on Computational Linguistics

More and more evidence is appearing that integrating symbolic lexical knowledge into neural models aids learning. This contrasts the widely-held belief that neural networks largely learn their own feature representations. For example, recent work has shows benefits of integrating lexicons to aid cross-lingual part-of-speech (PoS). However, little is known on how complementary such additional information is, and to what extent improvements depend on the coverage and quality of these external resources. This paper seeks to fill this gap by providing a thorough analysis on the contributions of lexical resources for cross-lingual PoS tagging in neural times.

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At a Glance: The Impact of Gaze Aggregation Views on Syntactic Tagging
Sigrid Klerke | Barbara Plank
Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)

Readers’ eye movements used as part of the training signal have been shown to improve performance in a wide range of Natural Language Processing (NLP) tasks. Previous work uses gaze data either at the type level or at the token level and mostly from a single eye-tracking corpus. In this paper, we analyze type vs token-level integration options with eye tracking data from two corpora to inform two syntactic sequence labeling problems: binary phrase chunking and part-of-speech tagging. We show that using globally-aggregated measures that capture the central tendency or variability of gaze data is more beneficial than proposed local views which retain individual participant information. While gaze data is informative for supervised POS tagging, which complements previous findings on unsupervised POS induction, almost no improvement is obtained for binary phrase chunking, except for a single specific setup. Hence, caution is warranted when using gaze data as signal for NLP, as no single view is robust over tasks, modeling choice and gaze corpus.


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Predicting misreadings from gaze in children with reading difficulties
Joachim Bingel | Maria Barrett | Sigrid Klerke
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

We present the first work on predicting reading mistakes in children with reading difficulties based on eye-tracking data from real-world reading teaching. Our approach employs several linguistic and gaze-based features to inform an ensemble of different classifiers, including multi-task learning models that let us transfer knowledge about individual readers to attain better predictions. Notably, the data we use in this work stems from noisy readings in the wild, outside of controlled lab conditions. Our experiments show that despite the noise and despite the small fraction of misreadings, gaze data improves the performance more than any other feature group and our models achieve good performance. We further show that gaze patterns for misread words do not fully generalize across readers, but that we can transfer some knowledge between readers using multitask learning at least in some cases. Applications of our models include partial automation of reading assessment as well as personalized text simplification.

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Grotoco@SLAM: Second Language Acquisition Modeling with Simple Features, Learners and Task-wise Models
Sigrid Klerke | Héctor Martínez Alonso | Barbara Plank
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

We present our submission to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We focus on evaluating a range of features for the task, including user-derived measures, while examining how far we can get with a simple linear classifier. Our analysis reveals that errors differ per exercise format, which motivates our final and best-performing system: a task-wise (per exercise-format) model.


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Improving sentence compression by learning to predict gaze
Sigrid Klerke | Yoav Goldberg | Anders Søgaard
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Looking hard: Eye tracking for detecting grammaticality of automatically compressed sentences
Sigrid Klerke | Héctor Martínez Alonso | Anders Søgaard
Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015)

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Reading metrics for estimating task efficiency with MT output
Sigrid Klerke | Sheila Castilho | Maria Barrett | Anders Søgaard
Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning


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Copenhagen-Malmö: Tree Approximations of Semantic Parsing Problems
Natalie Schluter | Anders Søgaard | Jakob Elming | Dirk Hovy | Barbara Plank | Héctor Martínez Alonso | Anders Johanssen | Sigrid Klerke
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)


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Down-stream effects of tree-to-dependency conversions
Jakob Elming | Anders Johannsen | Sigrid Klerke | Emanuele Lapponi | Hector Martinez Alonso | Anders Søgaard
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Simple, readable sub-sentences
Sigrid Klerke | Anders Søgaard
51st Annual Meeting of the Association for Computational Linguistics Proceedings of the Student Research Workshop


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EMNLP@CPH: Is frequency all there is to simplicity?
Anders Johannsen | Héctor Martínez | Sigrid Klerke | Anders Søgaard
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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DSim, a Danish Parallel Corpus for Text Simplification
Sigrid Klerke | Anders Søgaard
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We present DSim, a new sentence aligned Danish monolingual parallel corpus extracted from 3701 pairs of news telegrams and corresponding professionally simplified short news articles. The corpus is intended for building automatic text simplification for adult readers. We compare DSim to different examples of monolingual parallel corpora, and we argue that this corpus is a promising basis for future development of automatic data-driven text simplification systems in Danish. The corpus contains both the collection of paired articles and a sentence aligned bitext, and we show that sentence alignment using simple tf*idf weighted cosine similarity scoring is on line with state―of―the―art when evaluated against a hand-aligned sample. The alignment results are compared to state of the art for English sentence alignment. We finally compare the source and simplified sides of the corpus in terms of lexical and syntactic characteristics and readability, and find that the one―to―many sentence aligned corpus is representative of the sentence simplifications observed in the unaligned collection of article pairs.