Papers by Aleksandr Nisnevich
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 . |
Experience Grounds Language (2020.emnlp-main)
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Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, Joseph Turian
| Challenge: | aaron carroll: language understanding research is held back by a failure to relate language to the physical world it describes and to social interactions it facilitates. carroll says successful linguistic communication relies on a shared experience of the world. |
| Approach: | They propose to use a broader physical and social context to address communication problems . they argue that the current success of representation learning approaches is limited . |
| Outcome: | a new study suggests that the current success of representation learning requires a parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication. |
Guided K-best Selection for Semantic Parsing Annotation (2022.acl-demo)
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Anton Belyy, Chieh-yang Huang, Jacob Andreas, Emmanouil Antonios Platanios, Sam Thomson, Richard Shin, Subhro Roy, Aleksandr Nisnevich, Charles Chen, Benjamin Van Durme
| Challenge: | a prototype model trained on a small amount of data is not available, leading to limited prediction performance. |
| Approach: | They propose a human-in-the-loop process that generates a set of valid candidates and allows users to quickly traverse the set and filter incorrect parses. |
| Outcome: | The proposed process can be used to efficiently traverse the candidate set and select the correct parse, with minimal modification when necessary. |