Papers by David Ho
Multimodal Conversation Structure Understanding (2026.eacl-long)
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| Challenge: | a new set of tasks is being developed to parse the structure of conversation . female characters are 1.2 times more likely to be cast as an addressee or side-participant . |
| Approach: | They propose a set of tasks and release an annotated dataset for multimodal conversation structure understanding. |
| Outcome: | The proposed model outperforms the baseline model, but performance drops when character identities are anonymized. |
Task-Oriented Dialogue as Dataflow Synthesis (2020.tacl-1)
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Jacob Andreas, John Bufe, David Burkett, Charles Chen, Josh Clausman, Jean Crawford, Kate Crim, Jordan DeLoach, Leah Dorner, Jason Eisner, Hao Fang, Alan Guo, David Hall, Kristin Hayes, Kellie Hill, Diana Ho, Wendy Iwaszuk, Smriti Jha, Dan Klein, Jayant Krishnamurthy, Theo Lanman, Percy Liang, Christopher H. Lin, Ilya Lintsbakh, Andy McGovern, Aleksandr Nisnevich, Adam Pauls, Dmitrij Petters, Brent Read, Dan Roth, Subhro Roy, Jesse Rusak, Beth Short, Div Slomin, Ben Snyder, Stephon Striplin, Yu Su, Zachary Tellman, Sam Thomson, Andrei Vorobev, Izabela Witoszko, Jason Wolfe, Abby Wray, Yuchen Zhang, Alexander Zotov
| Challenge: | Existing approaches to task-oriented dialogue represent dialogue state as a dataflow graph . microsoft's SMCalFlow dataset features complex dialogues about events, weather, places, and people . |
| Approach: | They propose a dataflow graph-based dialogue agent that maps each user utterance to a program that extends this graph. |
| Outcome: | The proposed framework improves representability and predictability in natural dialogues . it uses dataflow graphs and metacomputation to map user intents to a program . |
ReadPrompt: A Readable Prompting Method for Reliable Knowledge Probing (2023.findings-emnlp)
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| Challenge: | Existing methods to prob pre-trained language models (PLMs) lack readability and credibility. |
| Approach: | They propose a method to identify meaningful sentences to serve as prompts to assess the knowledge encoded within pre-trained language models (PLMs). |
| Outcome: | The proposed method achieves state-of-the-art on the current knowledge probing benchmark. |