Binod Gyawali


Using Rhetorical Structure Theory to Assess Discourse Coherence for Non-native Spontaneous Speech
Xinhao Wang | Binod Gyawali | James V. Bruno | Hillary R. Molloy | Keelan Evanini | Klaus Zechner
Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

This study aims to model the discourse structure of spontaneous spoken responses within the context of an assessment of English speaking proficiency for non-native speakers. Rhetorical Structure Theory (RST) has been commonly used in the analysis of discourse organization of written texts; however, limited research has been conducted to date on RST annotation and parsing of spoken language, in particular, non-native spontaneous speech. Due to the fact that the measurement of discourse coherence is typically a key metric in human scoring rubrics for assessments of spoken language, we conducted research to obtain RST annotations on non-native spoken responses from a standardized assessment of academic English proficiency. Subsequently, automatic parsers were trained on these annotations to process non-native spontaneous speech. Finally, a set of features were extracted from automatically generated RST trees to evaluate the discourse structure of non-native spontaneous speech, which were then employed to further improve the validity of an automated speech scoring system.

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.


Towards Evaluating Narrative Quality In Student Writing
Swapna Somasundaran | Michael Flor | Martin Chodorow | Hillary Molloy | Binod Gyawali | Laura McCulla
Transactions of the Association for Computational Linguistics, Volume 6

This work lays the foundation for automated assessments of narrative quality in student writing. We first manually score essays for narrative-relevant traits and sub-traits, and measure inter-annotator agreement. We then explore linguistic features that are indicative of good narrative writing and use them to build an automated scoring system. Experiments show that our features are more effective in scoring specific aspects of narrative quality than a state-of-the-art feature set.


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.


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

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)

Evaluating Argumentative and Narrative Essays using Graphs
Swapna Somasundaran | Brian Riordan | Binod Gyawali | Su-Youn Yoon
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This work investigates whether the development of ideas in writing can be captured by graph properties derived from the text. Focusing on student essays, we represent the essay as a graph, and encode a variety of graph properties including PageRank as features for modeling essay scores related to quality of development. We demonstrate that our approach improves on a state-of-the-art system on the task of holistic scoring of persuasive essays and on the task of scoring narrative essays along the development dimension.


Self-Training for Parsing Learner Text
Aoife Cahill | Binod Gyawali | James Bruno
Proceedings of the First Joint Workshop on Statistical Parsing of Morphologically Rich Languages and Syntactic Analysis of Non-Canonical Languages


Native Language Identification: a Simple n-gram Based Approach
Binod Gyawali | Gabriela Ramirez | Thamar Solorio
Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications


Grading the Quality of Medical Evidence
Binod Gyawali | Thamar Solorio | Yassine Benajiba
BioNLP: Proceedings of the 2012 Workshop on Biomedical Natural Language Processing

UABCoRAL: A Preliminary study for Resolving the Scope of Negation
Binod Gyawali | Thamar Solorio
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)