Papers by Julia White
Learning to Decompose and Organize Complex Tasks (2021.naacl-main)
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| Challenge: | Using a novel end-to-end pipeline, we propose a solution that consumes a complex task and induces 'dependency graphs' from unstructured text to represent sub-tasks and their relationships. |
| Approach: | They propose a pipeline that consumes a complex task and induces 'dependency graphs' from unstructured text to represent sub-tasks and their relationships. |
| Outcome: | The proposed pipeline outperforms state-of-the-art graph induction pipelines in a dataset of complex tasks with their sub-task graphs. |
Open-domain clarification question generation without question examples (2021.emnlp-main)
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| Challenge: | Currently, natural language inputs are unclear or ambiguous, causing uncertainty in dialogues. |
| Approach: | They propose a framework for building a visually grounded question-asking model capable of producing polar (yes-no) clarification questions to resolve misunderstandings in dialogue. |
| Outcome: | The proposed model can produce polar (yes-no) clarification questions to resolve misunderstandings in a goal-oriented 20 questions game with synthetic and human answerers. |
Leveraging Explicit Procedural Instructions for Data-Efficient Action Prediction (2023.findings-acl)
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| Challenge: | a paper presents a data-efficient solution to constructing task-oriented dialogue systems . large language models have shown success in modeling such dialogues, but they require large quantities of data . |
| Approach: | They propose a system that leverages explicit instructions from agent guidelines . they propose dialogue-document matching and action-oriented masked language modeling . |
| Outcome: | The proposed system improves accuracy predicting in- and out-of-distribution actions while preserving high performance in settings with low or sparse data. |
Mixed-effects transformers for hierarchical adaptation (2022.emnlp-main)
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| Challenge: | Language differs dramatically from context to context, but prompting can be ineffective when contexts are sparse, out-of-sample, or extra-textual. |
| Approach: | They propose a mixed-effects transformer approach for learning hierarchically-structured prefixes to account for structured variation in language use. |
| Outcome: | The proposed approach can be extended to transformer-based architectures while generalizing well to unseen contexts. |
Calibrate your listeners! Robust communication-based training for pragmatic speakers (2021.findings-emnlp)
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| Challenge: | Prior work has investigated training NLP systems with communication-based objectives . prior work has focused on supervised learning, but is expensive to collect . |
| Approach: | They propose a method that uses a population of neural listeners to regularize speaker training. |
| Outcome: | The proposed method improves on ensemble- and dropout-based listening populations on reference games and generalizes to new games and listeners. |