Alona Fyshe


2022

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Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask
Bilal Ghanem | Lauren Lutz Coleman | Julia Rivard Dexter | Spencer von der Ohe | Alona Fyshe
Findings of the Association for Computational Linguistics: ACL 2022

Reading is integral to everyday life, and yet learning to read is a struggle for many young learners. During lessons, teachers can use comprehension questions to increase engagement, test reading skills, and improve retention. Historically such questions were written by skilled teachers, but recently language models have been used to generate comprehension questions. However, many existing Question Generation (QG) systems focus on generating extractive questions from the text, and have no way to control the type of the generated question. In this paper, we study QG for reading comprehension where inferential questions are critical and extractive techniques cannot be used. We propose a two-step model (HTA-WTA) that takes advantage of previous datasets, and can generate questions for a specific targeted comprehension skill. We propose a new reading comprehension dataset that contains questions annotated with story-based reading comprehension skills (SBRCS), allowing for a more complete reader assessment. Across several experiments, our results show that HTA-WTA outperforms multiple strong baselines on this new dataset. We show that the HTA-WTA model tests for strong SCRS by asking deep inferential questions.

2020

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From Language to Language-ish: How Brain-Like is an LSTM’s Representation of Nonsensical Language Stimuli?
Maryam Hashemzadeh | Greta Kaufeld | Martha White | Andrea E. Martin | Alona Fyshe
Findings of the Association for Computational Linguistics: EMNLP 2020

The representations generated by many models of language (word embeddings, recurrent neural networks and transformers) correlate to brain activity recorded while people read. However, these decoding results are usually based on the brain’s reaction to syntactically and semantically sound language stimuli. In this study, we asked: how does an LSTM (long short term memory) language model, trained (by and large) on semantically and syntactically intact language, represent a language sample with degraded semantic or syntactic information? Does the LSTM representation still resemble the brain’s reaction? We found that, even for some kinds of nonsensical language, there is a statistically significant relationship between the brain’s activity and the representations of an LSTM. This indicates that, at least in some instances, LSTMs and the human brain handle nonsensical data similarly.

2018

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Social and Emotional Correlates of Capitalization on Twitter
Sophia Chan | Alona Fyshe
Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media

Social media text is replete with unusual capitalization patterns. We posit that capitalizing a token like THIS performs two expressive functions: it marks a person socially, and marks certain parts of an utterance as more salient than others. Focusing on gender and sentiment, we illustrate using a corpus of tweets that capitalization appears in more negative than positive contexts, and is used more by females compared to males. Yet we find that both genders use capitalization in a similar way when expressing sentiment.

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Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models
Avery Hiebert | Cole Peterson | Alona Fyshe | Nishant Mehta
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

While Long Short-Term Memory networks (LSTMs) and other forms of recurrent neural network have been successfully applied to language modeling on a character level, the hidden state dynamics of these models can be difficult to interpret. We investigate the hidden states of such a model by using the HDBSCAN clustering algorithm to identify points in the text at which the hidden state is similar. Focusing on whitespace characters prior to the beginning of a word reveals interpretable clusters that offer insight into how the LSTM may combine contextual and character-level information to identify parts of speech. We also introduce a method for deriving word vectors from the hidden state representation in order to investigate the word-level knowledge of the model. These word vectors encode meaningful semantic information even for words that appear only once in the training text.

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The Emergence of Semantics in Neural Network Representations of Visual Information
Dhanush Dharmaretnam | Alona Fyshe
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Word vector models learn about semantics through corpora. Convolutional Neural Networks (CNNs) can learn about semantics through images. At the most abstract level, some of the information in these models must be shared, as they model the same real-world phenomena. Here we employ techniques previously used to detect semantic representations in the human brain to detect semantic representations in CNNs. We show the accumulation of semantic information in the layers of the CNN, and discover that, for misclassified images, the correct class can be recovered in intermediate layers of a CNN.

2017

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Ensemble Methods for Native Language Identification
Sophia Chan | Maryam Honari Jahromi | Benjamin Benetti | Aazim Lakhani | Alona Fyshe
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Our team—Uvic-NLP—explored and evaluated a variety of lexical features for Native Language Identification (NLI) within the framework of ensemble methods. Using a subset of the highest performing features, we train Support Vector Machines (SVM) and Fully Connected Neural Networks (FCNN) as base classifiers, and test different methods for combining their outputs. Restricting our scope to the closed essay track in the NLI Shared Task 2017, we find that our best SVM ensemble achieves an F1 score of 0.8730 on the test set.

2016

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Poet Admits // Mute Cypher: Beam Search to find Mutually Enciphering Poetic Texts
Cole Peterson | Alona Fyshe
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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BrainBench: A Brain-Image Test Suite for Distributional Semantic Models
Haoyan Xu | Brian Murphy | Alona Fyshe
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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A Compositional and Interpretable Semantic Space
Alona Fyshe | Leila Wehbe | Partha P. Talukdar | Brian Murphy | Tom M. Mitchell
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Interpretable Semantic Vectors from a Joint Model of Brain- and Text- Based Meaning
Alona Fyshe | Partha P. Talukdar | Brian Murphy | Tom M. Mitchell
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Documents and Dependencies: an Exploration of Vector Space Models for Semantic Composition
Alona Fyshe | Brian Murphy | Partha Talukdar | Tom Mitchell
Proceedings of the Seventeenth Conference on Computational Natural Language Learning

2006

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Term Generalization and Synonym Resolution for Biological Abstracts: Using the Gene Ontology for Subcellular Localization Prediction
Alona Fyshe | Duane Szafron
Proceedings of the HLT-NAACL BioNLP Workshop on Linking Natural Language and Biology