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 • 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 • ACL 2020 • Omid Rohanian, Marek Rei, Shiva Taslimipoor, Le An Ha
Metaphor is a linguistic device in which a concept is expressed by mentioning another.
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 • Emad Mohamed, Le An Ha
Film age appropriateness classification is an important problem with a significant societal impact that has so far been out of the interest of Natural Language Processing and Machine Learning researchers.
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 • 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 • 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 • 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 2019 • Shiva Taslimipoor, Omid Rohanian, Le An Ha
Recent developments in deep learning have prompted a surge of interest in the application of multitask and transfer learning to NLP problems.
2 code implementations • NAACL 2019 • Omid Rohanian, Shiva Taslimipoor, Samaneh Kouchaki, Le An Ha, Ruslan Mitkov
We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture.
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 • SEMEVAL 2018 • Shiva Taslimipoor, Omid Rohanian, Le An Ha, Gloria Corpas Pastor, Ruslan Mitkov
This paper describes the system submitted to SemEval 2018 shared task 10 {`}Capturing Dicriminative Attributes{'}.
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.