Papers by Leah Findlater
Rapidly Piloting Real-time Linguistic Assistance for Simultaneous Interpreters with Untrained Bilingual Surrogates (2024.lrec-main)
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Alvin C. Grissom II, Jo Shoemaker, Benjamin Goldman, Ruikang Shi, Craig Stewart, C. Anton Rytting, Leah Findlater, Jordan Boyd-Graber
| Challenge: | Simultaneous interpretation is a cognitively taxing task, and even seasoned professionals benefit from real-time assistance. |
| Approach: | They propose a simultaneous interpretation task that mimics the cognitive load of interpretation with crowdworker surrogates. |
| Outcome: | The proposed task mimics the cognitive load of interpretation with crowdworker surrogates . the evaluation setup provides consistent results between expert and proxy participants . |
Which Evaluations Uncover Sense Representations that Actually Make Sense? (2020.lrec-1)
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| Challenge: | Existing sense representations fail for human-centric tasks like inspecting a language’s sense inventory. |
| Approach: | They propose a coherence evaluation for sense embeddings and a model optimized for finding interpretable sense representations that are more coherent than existing sense embeds. |
| Outcome: | The proposed model is more coherent than existing sense embeddings and offers comparable word similarities with multisense representations while learning more distinguishable, interpretable senses. |
Why Didn’t You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models (P19-1)
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| Challenge: | Informed prior-based methods provide better control than constraints, but constraints yield higher quality topics, but with less control. |
| Approach: | They propose to use constraints and informed prior-based methods to improve user control and topic coherence. |
| Outcome: | The proposed methods improve user control and topic coherence, while constraints yield higher quality topics, but with less control. |
Interactive Refinement of Cross-Lingual Word Embeddings (2020.emnlp-main)
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| Challenge: | Cross-lingual word embeddings transfer knowledge between languages to models trained on resource-rich languages can predict in low-resource languages. |
| Approach: | They propose an interactive system to quickly refine cross-lingual word embeddings for a given classification problem. |
| Outcome: | The proposed system improves on identifying health-related text in four low-resource languages. |