Papers by Matthew R. Gormley
Neural Factor Graph Models for Cross-lingual Morphological Tagging (P18-1)
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| Challenge: | Existing approaches to morphological tagging are limited by the assumption that tag sets overlap . a limited amount of data is available for most languages to learn these morphology taggers. |
| Approach: | They propose a method for cross-lingual morphological tagging that relaxes this assumption . they use factorial conditional random fields with neural network potentials to smooth over superficial differences in the surface forms . |
| Outcome: | The proposed model can smooth over superficial differences in the surface forms and generate unseen or rare tag sets. |
Effective Convolutional Attention Network for Multi-label Clinical Document Classification (2021.emnlp-main)
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| Challenge: | a large number of medical encounters need to be coded everyday due to long document sets and large label set. |
| Approach: | They propose a convolutional attention network for multi-label document classification problem . they use convolution-based encoders and convolution networks to aggregate information across documents . |
| Outcome: | The proposed model outperforms prior best model and multilingual Transformer model on a widely used dataset in the medical domain. |
Limitations of Autoregressive Models and Their Alternatives (2021.naacl-main)
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| Challenge: | Standard autoregressive language models only perform polynomial-time computation to compute probability of next symbol. |
| Approach: | authors propose alternative to standard autoregressive language models that use polynomial-time computation to compute probability of next symbol. |
| Outcome: | a large model size can grow superpolynomially in length, allowing it to store precomputed results and verify solutions. |
Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations (2021.findings-emnlp)
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Longxiang Zhang, Renato Negrinho, Arindam Ghosh, Vasudevan Jagannathan, Hamid Reza Hassanzadeh, Thomas Schaaf, Matthew R. Gormley
| Challenge: | Using pretrained transformer models for automatically summarizing doctor-patient conversations presents challenges . limited training data, domain shift, long and noisy transcripts, and high target summary variability are challenges compared to human annotators. |
| Approach: | They propose a method for fine-tuning pretrained transformer models for automatically summarizing doctor-patient conversations directly from transcripts. |
| Outcome: | The proposed method surpasses the performance of an average human annotator and the quality of previous published work for the task. |
He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues (2022.findings-emnlp)
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| Challenge: | Existing work on style transfer has focused on controlling formality, authorial style, and sentiment of text. |
| Approach: | They propose a style transfer task that reframes a dialogue from informal first person to formal third person rephrasing . they use a dataset to annotate dialogues from a text summarization corpus . |
| Outcome: | The proposed task improves the performance of extractive models on a dialogue summarization dataset. |
In-Context Learning with Long-Context Models: An In-Depth Exploration (2025.naacl-long)
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| Challenge: | In-context learning is limited by context length, but it can be used for many tasks. |
| Approach: | They study the behavior of in-context learning at an extreme context length . example retrieval shows excellent performance at low context lengths but has diminished gains . |
| Outcome: | The proposed model can perform many tasks with reasonable accuracy when a few examples are provided in-context. |
Neural Finite-State Transducers: Beyond Rational Relations (N19-1)
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| Challenge: | a finite state transducer defines joint and conditional probability distributions over strings . a weighted finite-state transducers can only model certain functions, known as the rational relations . |
| Approach: | They propose a family of string transduction models defining joint and conditional probability distributions over pairs of strings. |
| Outcome: | The proposed models are more powerful than previous finite-state models with neural features. |
An Empirical Investigation of Beam-Aware Training in Supertagging (2020.findings-emnlp)
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| Challenge: | Structured prediction is often approached by training a local normalized model with maximum likelihood and decoding approximately with beam search. |
| Approach: | They propose a meta-algorithm that captures beam-aware training algorithms and suggests new ones. |
| Outcome: | The proposed algorithm improves performance for both models and the simpler model . it also improves the model which must manage uncertainty during decoding . |
Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces (P19-1)
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| Challenge: | Recent work on bilingual lexicon induction (BLI) relies on an assumption about the isometry of two embedding spaces. |
| Approach: | They propose a semi-supervised approach that relaxes the isometric assumption while leveraging limited aligned bilingual lexicons and a larger set of unaligned word embeddings. |
| Outcome: | The proposed method obtains state-of-the-art results on 15 of 18 language pairs on the MUSE dataset and does particularly well when the embedding spaces don’t appear isometric. |
Training for Gibbs Sampling on Conditional Random Fields with Neural Scoring Factors (2020.emnlp-main)
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| Challenge: | Recent advances in NLP focus on simple approaches to model the output label space . graphical models are often limited to (heuristic) greedy search and its variants . |
| Approach: | They propose an approach for efficiently training and decoding hybrids of graphical and graphical models based on Gibbs sampling. |
| Outcome: | The proposed approach improves on Dutch and Dutch with graphical models . the proposed model improves over a strong baseline on three languages . |
Phonetic and Visual Priors for Decipherment of Informal Romanization (2020.acl-main)
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| Challenge: | Informal romanization is an idiosyncratic process used by humans in informal digital communication to encode non-Latin script languages into Latin character sets found on common keyboards. |
| Approach: | They propose a noisy-channel WFST cascade model for deciphering the original non-Latin script from observed romanized text in an unsupervised fashion. |
| Outcome: | The proposed model improves on romanized Egyptian Arabic and Russian data and is closer to the supervised skyline. |