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.
no code implementations • WS 2020 • Xianyang Chen, Chee Wee (Ben) Leong, Michael Flor, Beata Beigman Klebanov
This paper describes the ETS entry to the 2020 Metaphor Detection shared task.
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.
no code implementations • WS 2020 • Debanjan Ghosh, Beata Beigman Klebanov, Yi Song
We present a computational exploration of argument critique writing by young students.
no code implementations • ACL 2019 • Nitin Madnani, Beata Beigman Klebanov, Anastassia Loukina, Binod Gyawali, Patrick Lange, John Sabatini, Michael Flor
Literacy is crucial for functioning in modern society.
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.
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.
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.
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.
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.
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.
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.
no code implementations • WS 2017 • Beata Beigman Klebanov, Anastassia Loukina, John Sabatini, Tenaha O{'}Reilly
This paper is a preliminary report on using text complexity measurement in the service of a new educational application.
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.
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.