Papers by Jason Naradowsky
Gender Bias in Coreference Resolution (N18-2)
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| Challenge: | a study of coreference resolution systems that resolve gender differences in pairs is aimed at examining implicit gender biases. |
| Approach: | They propose a Winograd schema-style set of minimal pair sentences that differ only by gender . they evaluate and confirm systematic gender bias in three publicly-available coreference resolution systems . |
| Outcome: | The proposed system resolves a male and neutral pronoun as coreferent with "The surgeon" but does not resolve the female pronounce. |
Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem (2022.findings-acl)
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| Challenge: | Existing methods of offensive text detection perform poorly when asked to detect implicitly offensive statements . a dataset based on SLIGHT provides a framework for implicit offensive text identification . |
| Approach: | They propose a dataset to support the task of implicit offensive text detection in dialogues . they show that reasoning is crucial for understanding this broader class of offensive utterances - SLIGHT . |
| Outcome: | The proposed model achieves 11% accuracy in implicit offensive text detection tasks . the proposed model can be used to identify toxic speech in specific domains . |
Ask an Expert: Leveraging Language Models to Improve Strategic Reasoning in Goal-Oriented Dialogue Models (2023.findings-acl)
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| Challenge: | Existing dialogue models may encounter scenarios which are not well-represented in the training data and produce unnatural, inappropriate, or unhelpful responses. |
| Approach: | They propose a framework in which a model is trained with access to an "expert" they propose to optimize the model to selectively utilize (or ignore) advice given context and dialogue history. |
| Outcome: | The proposed framework improves quality across all expert sizes and with fewer parameters than the dialogue model itself. |
Mind the Gap Between Conversations for Improved Long-Term Dialogue Generation (2023.findings-emnlp)
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| Challenge: | a gap between conversations can be weeks, months or years, and dialogue systems which do not explicitly model time may generate unnatural responses. |
| Approach: | They propose to model the passage of time between conversations by exposing time information to a multi-session dialogue dataset and comparing different representations of time and event progress. |
| Outcome: | The proposed model is based on a real-time dataset showing that it can predict topics and information gained from conversations over a long time span. |
A Structured Variational Autoencoder for Contextual Morphological Inflection (P18-1)
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| Challenge: | morphological inflectors typically trained on fully supervised, type-level data, but how can we improve their performance? et al., 2016: a novel latent-variable model for semi-supervised learning of inflection generation. |
| Approach: | They propose a latent-variable model for semi-supervised learning of inflection generation . they use a wake-sleep algorithm to enable posterior inference over latent variables . |
| Outcome: | The proposed model improves on 23 languages and shows 10% accuracy improvement . the proposed model is based on the wake-sleep algorithm . |
Fiction-Writing Mode: An Effective Control for Human-Machine Collaborative Writing (2023.eacl-main)
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| Challenge: | Large-scale pre-trained language models (PLMs) have demonstrated an exceptional aptitude for generating text with an exceptional degree of fluency and structure. |
| Approach: | They propose to integrate writing skills curricula into human-machine collaborative writing scenarios by adding writing modes as a control for text generation models. |
| Outcome: | The proposed model can be used to generate narrative fiction with a high level of accuracy and similarity with the professionally written target story. |
Who Said What: Formalization and Benchmarks for the Task of Quote Attribution (2024.lrec-main)
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| Challenge: | Existing methods for quote attribution are poorly understood, despite advances in research . previous approaches have used hand-crafted features to identify speaker names . |
| Approach: | They formalize the task of quote attribution and establish a basis for comparison . they compare CEQA and ChatGPT models on available datasets in both English and Chinese . |
| Outcome: | The proposed model outperforms all supervised methods on English and Chinese datasets. |
How Much Do Large Language Models Know about Human Motion? A Case Study in 3D Avatar Control (2025.findings-emnlp)
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| Challenge: | a new study explores the human motion knowledge of Large Language Models (LLMs) using 3D avatar control. |
| Approach: | They use 20 representative motion instructions to interpolate LLMs into avatar animations . they find they are strong at interpreting high-level body movements but struggle with precise body part positioning . |
| Outcome: | The proposed model is strong at interpreting high-level body movements but struggles with precise body part positioning. |