Glorianna Jagfeld


2019

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A Computational Linguistic Study of Personal Recovery in Bipolar Disorder
Glorianna Jagfeld
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Mental health research can benefit increasingly fruitfully from computational linguistics methods, given the abundant availability of language data in the internet and advances of computational tools. This interdisciplinary project will collect and analyse social media data of individuals diagnosed with bipolar disorder with regard to their recovery experiences. Personal recovery - living a satisfying and contributing life along symptoms of severe mental health issues - so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants inWestern countries. Complementary to this evidence, computational linguistic methods allow us to analyse first-person accounts shared online in large quantities, representing unstructured settings and a more heterogeneous, multilingual population, to draw a more complete picture of the aspects and mechanisms of personal recovery in bipolar disorder.

2018

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Comparing Attention-Based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension
Matthias Blohm | Glorianna Jagfeld | Ekta Sood | Xiang Yu | Ngoc Thang Vu
Proceedings of the 22nd Conference on Computational Natural Language Learning

We propose a machine reading comprehension model based on the compare-aggregate framework with two-staged attention that achieves state-of-the-art results on the MovieQA question answering dataset. To investigate the limitations of our model as well as the behavioral difference between convolutional and recurrent neural networks, we generate adversarial examples to confuse the model and compare to human performance. Furthermore, we assess the generalizability of our model by analyzing its differences to human inference, drawing upon insights from cognitive science.

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Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity
Glorianna Jagfeld | Sabrina Jenne | Ngoc Thang Vu
Proceedings of the 11th International Conference on Natural Language Generation

We present a comparison of word-based and character-based sequence-to-sequence models for data-to-text natural language generation, which generate natural language descriptions for structured inputs. On the datasets of two recent generation challenges, our models achieve comparable or better automatic evaluation results than the best challenge submissions. Subsequent detailed statistical and human analyses shed light on the differences between the two input representations and the diversity of the generated texts. In a controlled experiment with synthetic training data generated from templates, we demonstrate the ability of neural models to learn novel combinations of the templates and thereby generalize beyond the linguistic structures they were trained on.

2017

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Evaluating Compound Splitters Extrinsically with Textual Entailment
Glorianna Jagfeld | Patrick Ziering | Lonneke van der Plas
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Traditionally, compound splitters are evaluated intrinsically on gold-standard data or extrinsically on the task of statistical machine translation. We explore a novel way for the extrinsic evaluation of compound splitters, namely recognizing textual entailment. Compound splitting has great potential for this novel task that is both transparent and well-defined. Moreover, we show that it addresses certain aspects that are either ignored in intrinsic evaluations or compensated for by taskinternal mechanisms in statistical machine translation. We show significant improvements using different compound splitting methods on a German textual entailment dataset.

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Encoding Word Confusion Networks with Recurrent Neural Networks for Dialog State Tracking
Glorianna Jagfeld | Ngoc Thang Vu
Proceedings of the Workshop on Speech-Centric Natural Language Processing

This paper presents our novel method to encode word confusion networks, which can represent a rich hypothesis space of automatic speech recognition systems, via recurrent neural networks. We demonstrate the utility of our approach for the task of dialog state tracking in spoken dialog systems that relies on automatic speech recognition output. Encoding confusion networks outperforms encoding the best hypothesis of the automatic speech recognition in a neural system for dialog state tracking on the well-known second Dialog State Tracking Challenge dataset.

2016

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The Grammar of English Deverbal Compounds and their Meaning
Gianina Iordăchioaia | Lonneke van der Plas | Glorianna Jagfeld
Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces (GramLex)

We present an interdisciplinary study on the interaction between the interpretation of noun-noun deverbal compounds (DCs; e.g., task assignment) and the morphosyntactic properties of their deverbal heads in English. Underlying hypotheses from theoretical linguistics are tested with tools and resources from computational linguistics. We start with Grimshaw’s (1990) insight that deverbal nouns are ambiguous between argument-supporting nominal (ASN) readings, which inherit verbal arguments (e.g., the assignment of the tasks), and the less verbal and more lexicalized Result Nominal and Simple Event readings (e.g., a two-page assignment). Following Grimshaw, our hypothesis is that the former will realize object arguments in DCs, while the latter will receive a wider range of interpretations like root compounds headed by non-derived nouns (e.g., chocolate box). Evidence from a large corpus assisted by machine learning techniques confirms this hypothesis, by showing that, besides other features, the realization of internal arguments by deverbal heads outside compounds (i.e., the most distinctive ASN-property in Grimshaw 1990) is a good predictor for an object interpretation of non-heads in DCs.

2015

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Towards a Better Semantic Role Labeling of Complex Predicates
Glorianna Jagfeld | Lonneke van der Plas
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop