Beata Beigman Klebanov

Also published as: Beata Beigman Klebanov


2020

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Proceedings of the Second Workshop on Figurative Language Processing
Beata Beigman Klebanov | Ekaterina Shutova | Patricia Lichtenstein | Smaranda Muresan | Chee Wee | Anna Feldman | Debanjan Ghosh
Proceedings of the Second Workshop on Figurative Language Processing

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A Report on the 2020 VUA and TOEFL Metaphor Detection Shared Task
Chee Wee (Ben) Leong | Beata Beigman Klebanov | Chris Hamill | Egon Stemle | Rutuja Ubale | Xianyang Chen
Proceedings of the Second Workshop on Figurative Language Processing

In this paper, we report on the shared task on metaphor identification on VU Amsterdam Metaphor Corpus and on a subset of the TOEFL Native Language Identification Corpus. The shared task was conducted as apart of the ACL 2020 Workshop on Processing Figurative Language.

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Go Figure! Multi-task transformer-based architecture for metaphor detection using idioms: ETS team in 2020 metaphor shared task
Xianyang Chen | Chee Wee (Ben) Leong | Michael Flor | Beata Beigman Klebanov
Proceedings of the Second Workshop on Figurative Language Processing

This paper describes the ETS entry to the 2020 Metaphor Detection shared task. Our contribution consists of a sequence of experiments using BERT, starting with a baseline, strengthening it by spell-correcting the TOEFL corpus, followed by a multi-task learning setting, where one of the tasks is the token-level metaphor classification as per the shared task, while the other is meant to provide additional training that we hypothesized to be relevant to the main task. In one case, out-of-domain data manually annotated for metaphor is used for the auxiliary task; in the other case, in-domain data automatically annotated for idioms is used for the auxiliary task. Both multi-task experiments yield promising results.

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Automated Evaluation of Writing – 50 Years and Counting
Beata Beigman Klebanov | Nitin Madnani
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In this theme paper, we focus on Automated Writing Evaluation (AWE), using Ellis Page’s seminal 1966 paper to frame the presentation. We discuss some of the current frontiers in the field and offer some thoughts on the emergent uses of this technology.

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An Exploratory Study of Argumentative Writing by Young Students: A transformer-based Approach
Debanjan Ghosh | Beata Beigman Klebanov | Yi Song
Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications

We present a computational exploration of argument critique writing by young students. Middle school students were asked to criticize an argument presented in the prompt, focusing on identifying and explaining the reasoning flaws. This task resembles an established college-level argument critique task. Lexical and discourse features that utilize detailed domain knowledge to identify critiques exist for the college task but do not perform well on the young students’ data. Instead, transformer-based architecture (e.g., BERT) fine-tuned on a large corpus of critique essays from the college task performs much better (over 20% improvement in F1 score). Analysis of the performance of various configurations of the system suggests that while children’s writing does not exhibit the standard discourse structure of an argumentative essay, it does share basic local sequential structures with the more mature writers.

2019

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My Turn To Read: An Interleaved E-book Reading Tool for Developing and Struggling Readers
Nitin Madnani | Beata Beigman Klebanov | Anastassia Loukina | Binod Gyawali | Patrick Lange | John Sabatini | Michael Flor
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Literacy is crucial for functioning in modern society. It underpins everything from educational attainment and employment opportunities to health outcomes. We describe My Turn To Read, an app that uses interleaved reading to help developing and struggling readers improve reading skills while reading for meaning and pleasure. We hypothesize that the longer-term impact of the app will be to help users become better, more confident readers with an increased stamina for extended reading. We describe the technology and present preliminary evidence in support of this hypothesis.

2018

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Writing Mentor: Self-Regulated Writing Feedback for Struggling Writers
Nitin Madnani | Jill Burstein | Norbert Elliot | Beata Beigman Klebanov | Diane Napolitano | Slava Andreyev | Maxwell Schwartz
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

Writing Mentor is a free Google Docs add-on designed to provide feedback to struggling writers and help them improve their writing in a self-paced and self-regulated fashion. Writing Mentor uses natural language processing (NLP) methods and resources to generate feedback in terms of features that research into post-secondary struggling writers has classified as developmental (Burstein et al., 2016b). These features span many writing sub-constructs (use of sources, claims, and evidence; topic development; coherence; and knowledge of English conventions). Prelimi- nary analysis indicates that users have a largely positive impression of Writing Mentor in terms of usability and potential impact on their writing.

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Towards Understanding Text Factors in Oral Reading
Anastassia Loukina | Van Rynald T. Liceralde | Beata Beigman Klebanov
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Using a case study, we show that variation in oral reading rate across passages for professional narrators is consistent across readers and much of it can be explained using features of the texts being read. While text complexity is a poor predictor of the reading rate, a substantial share of variability can be explained by timing and story-based factors with performance reaching r=0.75 for unseen passages and narrator.

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A Corpus of Non-Native Written English Annotated for Metaphor
Beata Beigman Klebanov | Chee Wee (Ben) Leong | Michael Flor
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

We present a corpus of 240 argumentative essays written by non-native speakers of English annotated for metaphor. The corpus is made publicly available. We provide benchmark performance of state-of-the-art systems on this new corpus, and explore the relationship between writing proficiency and metaphor use.

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Using exemplar responses for training and evaluating automated speech scoring systems
Anastassia Loukina | Klaus Zechner | James Bruno | Beata Beigman Klebanov
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

Automated scoring engines are usually trained and evaluated against human scores and compared to the benchmark of human-human agreement. In this paper we compare the performance of an automated speech scoring engine using two corpora: a corpus of almost 700,000 randomly sampled spoken responses with scores assigned by one or two raters during operational scoring, and a corpus of 16,500 exemplar responses with scores reviewed by multiple expert raters. We show that the choice of corpus used for model evaluation has a major effect on estimates of system performance with r varying between 0.64 and 0.80. Surprisingly, this is not the case for the choice of corpus for model training: when the training corpus is sufficiently large, the systems trained on different corpora showed almost identical performance when evaluated on the same corpus. We show that this effect is consistent across several learning algorithms. We conclude that evaluating the model on a corpus of exemplar responses if one is available provides additional evidence about system validity; at the same time, investing effort into creating a corpus of exemplar responses for model training is unlikely to lead to a substantial gain in model performance.

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Proceedings of the Workshop on Figurative Language Processing
Beata Beigman Klebanov | Ekaterina Shutova | Patricia Lichtenstein | Smaranda Muresan | Chee Wee
Proceedings of the Workshop on Figurative Language Processing

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Catching Idiomatic Expressions in EFL Essays
Michael Flor | Beata Beigman Klebanov
Proceedings of the Workshop on Figurative Language Processing

This paper presents an exploratory study on large-scale detection of idiomatic expressions in essays written by non-native speakers of English. We describe a computational search procedure for automatic detection of idiom-candidate phrases in essay texts. The study used a corpus of essays written during a standardized examination of English language proficiency. Automatically-flagged candidate expressions were manually annotated for idiomaticity. The study found that idioms are widely used in EFL essays. The study also showed that a search algorithm that accommodates the syntactic and lexical exibility of idioms can increase the recall of idiom instances by 30%, but it also increases the amount of false positives.

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A Report on the 2018 VUA Metaphor Detection Shared Task
Chee Wee (Ben) Leong | Beata Beigman Klebanov | Ekaterina Shutova
Proceedings of the Workshop on Figurative Language Processing

As the community working on computational approaches to figurative language is growing and as methods and data become increasingly diverse, it is important to create widely shared empirical knowledge of the level of system performance in a range of contexts, thus facilitating progress in this area. One way of creating such shared knowledge is through benchmarking multiple systems on a common dataset. We report on the shared task on metaphor identification on the VU Amsterdam Metaphor Corpus conducted at the NAACL 2018 Workshop on Figurative Language Processing.

2017

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Continuous fluency tracking and the challenges of varying text complexity
Beata Beigman Klebanov | Anastassia Loukina | John Sabatini | Tenaha O’Reilly
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

This paper is a preliminary report on using text complexity measurement in the service of a new educational application. We describe a reading intervention where a child takes turns reading a book aloud with a virtual reading partner. Our ultimate goal is to provide meaningful feedback to the parent or the teacher by continuously tracking the child’s improvement in reading fluency. We show that this would not be a simple endeavor, due to an intricate relationship between text complexity from the point of view of comprehension and reading rate.

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Exploring Relationships Between Writing & Broader Outcomes With Automated Writing Evaluation
Jill Burstein | Dan McCaffrey | Beata Beigman Klebanov | Guangming Ling
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Writing is a challenge, especially for at-risk students who may lack the prerequisite writing skills required to persist in U.S. 4-year postsecondary (college) institutions. Educators teaching postsecondary courses requiring writing could benefit from a better understanding of writing achievement and its role in postsecondary success. In this paper, novel exploratory work examined how automated writing evaluation (AWE) can inform our understanding of the relationship between postsecondary writing skill and broader success outcomes. An exploratory study was conducted using test-taker essays from a standardized writing assessment of postsecondary student learning outcomes. Findings showed that for the essays, AWE features were found to be predictors of broader outcomes measures: college success and learning outcomes measures. Study findings illustrate AWE’s potential to support educational analytics – i.e., relationships between writing skill and broader outcomes – taking a step toward moving AWE beyond writing assessment and instructional use cases.

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Detecting Good Arguments in a Non-Topic-Specific Way: An Oxymoron?
Beata Beigman Klebanov | Binod Gyawali | Yi Song
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Automatic identification of good arguments on a controversial topic has applications in civics and education, to name a few. While in the civics context it might be acceptable to create separate models for each topic, in the context of scoring of students’ writing there is a preference for a single model that applies to all responses. Given that good arguments for one topic are likely to be irrelevant for another, is a single model for detecting good arguments a contradiction in terms? We investigate the extent to which it is possible to close the performance gap between topic-specific and across-topics models for identification of good arguments.

2016

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Semantic classifications for detection of verb metaphors
Beata Beigman Klebanov | Chee Wee Leong | E. Dario Gutierrez | Ekaterina Shutova | Michael Flor
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Topicality-Based Indices for Essay Scoring
Beata Beigman Klebanov | Michael Flor | Binod Gyawali
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications

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Enhancing STEM Motivation through Personal and Communal Values: NLP for Assessment of Utility Value in Student Writing
Beata Beigman Klebanov | Jill Burstein | Judith Harackiewicz | Stacy Priniski | Matthew Mulholland
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications

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Proceedings of the Fourth Workshop on Metaphor in NLP
Beata Beigman Klebanov | Ekaterina Shutova | Patricia Lichtenstein
Proceedings of the Fourth Workshop on Metaphor in NLP

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Argumentation: Content, Structure, and Relationship with Essay Quality
Beata Beigman Klebanov | Christian Stab | Jill Burstein | Yi Song | Binod Gyawali | Iryna Gurevych
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

2015

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Proceedings of the Third Workshop on Metaphor in NLP
Ekaterina Shutova | Beata Beigman Klebanov | Patricia Lichtenstein
Proceedings of the Third Workshop on Metaphor in NLP

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Supervised Word-Level Metaphor Detection: Experiments with Concreteness and Reweighting of Examples
Beata Beigman Klebanov | Chee Wee Leong | Michael Flor
Proceedings of the Third Workshop on Metaphor in NLP

2014

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Applying Argumentation Schemes for Essay Scoring
Yi Song | Michael Heilman | Beata Beigman Klebanov | Paul Deane
Proceedings of the First Workshop on Argumentation Mining

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Proceedings of the Second Workshop on Metaphor in NLP
Beata Beigman Klebanov | Ekaterina Shutova | Patricia Lichtenstein
Proceedings of the Second Workshop on Metaphor in NLP

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Different Texts, Same Metaphors: Unigrams and Beyond
Beata Beigman Klebanov | Ben Leong | Michael Heilman | Michael Flor
Proceedings of the Second Workshop on Metaphor in NLP

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ETS Lexical Associations System for the COGALEX-4 Shared Task
Michael Flor | Beata Beigman Klebanov
Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon (CogALex)

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Content Importance Models for Scoring Writing From Sources
Beata Beigman Klebanov | Nitin Madnani | Jill Burstein | Swapna Somasundaran
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Difficult Cases: From Data to Learning, and Back
Beata Beigman Klebanov | Eyal Beigman
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Proceedings of the First Workshop on Metaphor in NLP
Ekaterina Shutova | Beata Beigman Klebanov | Joel Tetreault | Zornitsa Kozareva
Proceedings of the First Workshop on Metaphor in NLP

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Argumentation-Relevant Metaphors in Test-Taker Essays
Beata Beigman Klebanov | Michael Flor
Proceedings of the First Workshop on Metaphor in NLP

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Lexical Tightness and Text Complexity
Michael Flor | Beata Beigman Klebanov | Kathleen M. Sheehan
Proceedings of the Workshop on Natural Language Processing for Improving Textual Accessibility

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Associative Texture Is Lost In Translation
Beata Beigman Klebanov | Michael Flor
Proceedings of the Workshop on Discourse in Machine Translation

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Using Pivot-Based Paraphrasing and Sentiment Profiles to Improve a Subjectivity Lexicon for Essay Data
Beata Beigman Klebanov | Nitin Madnani | Jill Burstein
Transactions of the Association for Computational Linguistics, Volume 1

We demonstrate a method of improving a seed sentiment lexicon developed on essay data by using a pivot-based paraphrasing system for lexical expansion coupled with sentiment profile enrichment using crowdsourcing. Profile enrichment alone yields up to 15% improvement in the accuracy of the seed lexicon on 3-way sentence-level sentiment polarity classification of essay data. Using lexical expansion in addition to sentiment profiles provides a further 7% improvement in performance. Additional experiments show that the proposed method is also effective with other subjectivity lexicons and in a different domain of application (product reviews).

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Word Association Profiles and their Use for Automated Scoring of Essays
Beata Beigman Klebanov | Michael Flor
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Measuring the Use of Factual Information in Test-Taker Essays
Beata Beigman Klebanov | Derrick Higgins
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

2010

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Some Empirical Evidence for Annotation Noise in a Benchmarked Dataset
Beata Beigman Klebanov | Eyal Beigman
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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A Game-Theoretic Model of Metaphorical Bargaining
Beata Beigman Klebanov | Eyal Beigman
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Vocabulary Choice as an Indicator of Perspective
Beata Beigman Klebanov | Eyal Beigman | Daniel Diermeier
Proceedings of the ACL 2010 Conference Short Papers

2009

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Squibs: From Annotator Agreement to Noise Models
Beata Beigman Klebanov | Eyal Beigman
Computational Linguistics, Volume 35, Number 4, December 2009

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Learning with Annotation Noise
Eyal Beigman | Beata Beigman Klebanov
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Discourse Topics and Metaphors
Beata Beigman Klebanov | Eyal Beigman | Daniel Diermeier
Proceedings of the Workshop on Computational Approaches to Linguistic Creativity

2008

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Analyzing Disagreements
Beata Beigman Klebanov | Eyal Beigman | Daniel Diermeier
Coling 2008: Proceedings of the workshop on Human Judgements in Computational Linguistics

2006

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Measuring Semantic Relatedness Using People and WordNet
Beata Beigman Klebanov
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers

2005

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Using Readers to Identify Lexical Cohesive Structures in Texts
Beata Beigman Klebanov
Proceedings of the ACL Student Research Workshop