Search Results for author: Beata Beigman Klebanov

Found 30 papers, 0 papers with code

Automated Evaluation of Writing -- 50 Years and Counting

no code implementations ACL 2020 Beata Beigman Klebanov, Nitin Madnani

In this theme paper, we focus on Automated Writing Evaluation (AWE), using Ellis Page{'}s seminal 1966 paper to frame the presentation.

Automated Writing Evaluation

A Report on the 2020 VUA and TOEFL Metaphor Detection Shared Task

no code implementations WS 2020 Chee Wee (Ben) Leong, Beata Beigman Klebanov, Chris Hamill, Egon Stemle, Rutuja Ubale, Xianyang Chen

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.

Native Language Identification

Writing Mentor: Self-Regulated Writing Feedback for Struggling Writers

no code implementations COLING 2018 Nitin Madnani, Jill Burstein, Norbert Elliot, Beata Beigman Klebanov, Diane Napolitano, Slava Andreyev, Maxwell Schwartz

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.

A Corpus of Non-Native Written English Annotated for Metaphor

no code implementations NAACL 2018 Beata Beigman Klebanov, Chee Wee (Ben) Leong, Michael Flor

We present a corpus of 240 argumentative essays written by non-native speakers of English annotated for metaphor.

Towards Understanding Text Factors in Oral Reading

no code implementations NAACL 2018 Anastassia Loukina, Van Rynald T. Liceralde, Beata Beigman Klebanov

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.

Language Acquisition

Catching Idiomatic Expressions in EFL Essays

no code implementations WS 2018 Michael Flor, Beata Beigman Klebanov

The study used a corpus of essays written during a standardized examination of English language proficiency.

A Report on the 2018 VUA Metaphor Detection Shared Task

no code implementations WS 2018 Chee Wee (Ben) Leong, Beata Beigman Klebanov, Ekaterina Shutova

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.

Benchmarking

Using exemplar responses for training and evaluating automated speech scoring systems

no code implementations WS 2018 Anastassia Loukina, Klaus Zechner, James Bruno, Beata Beigman Klebanov

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.

Exploring Relationships Between Writing \& Broader Outcomes With Automated Writing Evaluation

no code implementations WS 2017 Jill Burstein, Dan McCaffrey, Beata Beigman Klebanov, Guangming Ling

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.

Automated Writing Evaluation

Detecting Good Arguments in a Non-Topic-Specific Way: An Oxymoron?

no code implementations ACL 2017 Beata Beigman Klebanov, Binod Gyawali, Yi Song

Automatic identification of good arguments on a controversial topic has applications in civics and education, to name a few.

Using Pivot-Based Paraphrasing and Sentiment Profiles to Improve a Subjectivity Lexicon for Essay Data

no code implementations TACL 2013 Beata Beigman Klebanov, Nitin Madnani, Jill Burstein

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.

General Classification Sentence +1

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