no code implementations • EACL (BEA) 2021 • Victoria Yaneva, Daniel Jurich, Le An Ha, Peter Baldwin
This study examines the relationship between the linguistic characteristics of a test item and the complexity of the response process required to answer it correctly.
no code implementations • NAACL 2022 • Victoria Yaneva, Janet Mee, Le Ha, Polina Harik, Michael Jodoin, Alex Mechaber
This paper presents a corpus of 43, 985 clinical patient notes (PNs) written by 35, 156 examinees during the high-stakes USMLE® Step 2 Clinical Skills examination.
no code implementations • COLING 2020 • Le An Ha, Victoria Yaneva, Polina Harik, Ravi Pandian, Amy Morales, Brian Clauser
This paper brings together approaches from the fields of NLP and psychometric measurement to address the problem of predicting examinee proficiency from responses to short-answer questions (SAQs).
no code implementations • WS 2020 • Kang Xue, Victoria Yaneva, Christopher Runyon, Peter Baldwin
The results indicate that, for our sample, transfer learning can improve the prediction of item difficulty when response time is used as an auxiliary task but not the other way around.
1 code implementation • EMNLP 2018 • Victoria Yaneva, Le An Ha, Richard Evans, Ruslan Mitkov
When processing a text, humans and machines must disambiguate between different uses of the pronoun it, including non-referential, nominal anaphoric or clause anaphoric ones.
no code implementations • LREC 2020 • Victoria Yaneva, Le An Ha, Peter Baldwin, Janet Mee
One of the most resource-intensive problems in the educational testing industry relates to ensuring that newly-developed exam questions can adequately distinguish between students of high and low ability.
no code implementations • RANLP 2019 • Le An Ha, Victoria Yaneva
We present a novel approach to automatic question answering that does not depend on the performance of an information retrieval (IR) system and does not require that the training data come from the same source as the questions.
no code implementations • RANLP 2019 • Victoria Yaneva, Constantin Orasan, Le An Ha, Natalia Ponomareva
NLP approaches to automatic text adaptation often rely on user-need guidelines which are generic and do not account for the differences between various types of target groups.
no code implementations • WS 2019 • Le An Ha, Victoria Yaneva, Peter Baldwin, Janet Mee
To accomplish this, we extract a large number of linguistic features and embedding types, as well as features quantifying the difficulty of the items for an automatic question-answering system.
no code implementations • WS 2018 • Le An Ha, Victoria Yaneva
We frame the evaluation as a prediction task where we aim to {``}predict{''} the human-produced distractors used in large sets of medical questions, i. e. if a distractor generated by our system is good enough it is likely to feature among the list of distractors produced by the human item-writers.
no code implementations • RANLP 2017 • Omid Rohanian, Shiva Taslimipoor, Victoria Yaneva, Le An Ha
In recent years gaze data has been increasingly used to improve and evaluate NLP models due to the fact that it carries information about the cognitive processing of linguistic phenomena.
no code implementations • WS 2017 • Sanja {\v{S}}tajner, Victoria Yaneva, Ruslan Mitkov, Simone Paolo Ponzetto
Eye tracking studies from the past few decades have shaped the way we think of word complexity and cognitive load: words that are long, rare and ambiguous are more difficult to read.
no code implementations • WS 2017 • Victoria Yaneva, Constantin Or{\u{a}}san, Richard Evans, Omid Rohanian
Given the lack of large user-evaluated corpora in disability-related NLP research (e. g. text simplification or readability assessment for people with cognitive disabilities), the question of choosing suitable training data for NLP models is not straightforward.
no code implementations • LREC 2016 • Victoria Yaneva, Irina Temnikova, Ruslan Mitkov
This paper presents an approach for automatic evaluation of the readability of text simplification output for readers with cognitive disabilities.
no code implementations • LREC 2016 • Victoria Yaneva, Irina Temnikova, Ruslan Mitkov
This division of the groups informs researchers on whether particular fixations were elicited from skillful or less-skillful readers and allows a fair between-group comparison for two levels of reading ability.