Papers by Jayant Krishnamurthy
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 . |
The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding (2023.findings-acl)
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Hao Fang, Anusha Balakrishnan, Harsh Jhamtani, John Bufe, Jean Crawford, Jayant Krishnamurthy, Adam Pauls, Jason Eisner, Jacob Andreas, Dan Klein
| Challenge: | In a task-oriented dialogue system, response generation is a conditional language model, but effective dialogue agents must balance fluent generation with stricter constraints. |
| Approach: | They propose a rule-based content selection model that transduces a dialogue agent’s actions and their results into context-free grammars representing the space of contextually acceptable responses. |
| Outcome: | The proposed architecture outperforms both rule-based and learned approaches in human evaluations of fluency, relevance, and truthfulness. |
Value-Agnostic Conversational Semantic Parsing (2021.acl-long)
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Emmanouil Antonios Platanios, Adam Pauls, Subhro Roy, Yuchen Zhang, Alexander Kyte, Alan Guo, Sam Thomson, Jayant Krishnamurthy, Jason Wolfe, Jacob Andreas, Dan Klein
| Challenge: | Existing models rely on rich representations of dialogue history that include all previously generated components of the output. |
| Approach: | They propose a model that abstracts over values to focus prediction on type- and function-level context. |
| Outcome: | The proposed model outperforms baseline models by 7.3% and 10.6% on SMCalFlow and TreeDST datasets. |