Papers by Jason Lin
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 . |
Limitations of Autoregressive Models and Their Alternatives (2021.naacl-main)
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| Challenge: | Standard autoregressive language models only perform polynomial-time computation to compute probability of next symbol. |
| Approach: | authors propose alternative to standard autoregressive language models that use polynomial-time computation to compute probability of next symbol. |
| Outcome: | a large model size can grow superpolynomially in length, allowing it to store precomputed results and verify solutions. |
ScreenQA: Large-Scale Question-Answer Pairs Over Mobile App Screenshots (2025.naacl-long)
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Yu-Chung Hsiao, Fedir Zubach, Gilles Baechler, Srinivas Sunkara, Victor Carbune, Jason Lin, Maria Wang, Yun Zhu, Jindong Chen
| Challenge: | Existing screen datasets focus on low-level structural and component understanding or on a much higher-level composite task such as navigation and task completion for autonomous agents. |
| Approach: | They propose to annotate 86k question-answer pairs over the RICO dataset to benchmark screen content understanding. |
| Outcome: | The proposed dataset covers full answers, short answer phrases, and corresponding UI contents with bounding boxes, enabling four subtasks to address various application scenarios. |
One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction (2026.acl-srw)
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| Challenge: | Existing single-agent strategies sample from one role-conditioned distribution, and multi-agend frameworks use fixed roles with flat majority voting, discarding the diagnostic signal in disagreement. |
| Approach: | They propose a case-adaptive multi-agent panel where an attending-physician agent dynamically assembles a specialist panel tailored to each case’s diagnostic uncertainty. |
| Outcome: | The proposed model outperforms baseline models on diagnostic prediction and brief hospital course generation using MIMIC-IV. |
Constrained Language Models Yield Few-Shot Semantic Parsers (2021.emnlp-main)
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Richard Shin, Christopher Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, Benjamin Van Durme
| Challenge: | Large pretrained language models excel at generating natural language, but they are not efficient for task specific semantic parsing. |
| Approach: | They propose to use large pretrained language models as few-shot semantic parsers . they paraphrase inputs into a controlled sublanguage resembling English . |
| Outcome: | The proposed model can generate surprisingly accurate models on multiple tasks with minimal code and data. |
Neural Particle Smoothing for Sampling from Conditional Sequence Models (N18-1)
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| Challenge: | In structured prediction problems, labeling an input string with a length-T sequence of tags becomes intractable. |
| Approach: | They propose a sequential Monte Carlo method for sampling annotations of an input string from a given probability model. |
| Outcome: | The proposed method improves the quality of the sample. |
Neural Finite-State Transducers: Beyond Rational Relations (N19-1)
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| Challenge: | a finite state transducer defines joint and conditional probability distributions over strings . a weighted finite-state transducers can only model certain functions, known as the rational relations . |
| Approach: | They propose a family of string transduction models defining joint and conditional probability distributions over pairs of strings. |
| Outcome: | The proposed models are more powerful than previous finite-state models with neural features. |
Tab2Text - A framework for deep learning with tabular data (2024.findings-emnlp)
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| Challenge: | Tabular data is a foundational part of social sciences and is used to fit supervised learning models. |
| Approach: | They propose a technique for transforming tabular data to text data to improve deep learning models for tabular datasets. |
| Outcome: | The proposed technique improves performance of deep learning models for tabular data. |