Papers by Subhro Roy
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 . |
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. |
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models (2023.emnlp-main)
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| Challenge: | Existing LLMs cannot generalize to domain-specific parsing tasks in a zero-shot setting. |
| Approach: | They propose a task-oriented parsing method that decomposes parse problem into abstractive and extractive question-answering problems. |
| Outcome: | The proposed method decomposes a parsing problem into abstractive and extractive question-answering (QA) problems. |
InstructExcel: A Benchmark for Natural Language Instruction in Excel (2023.findings-emnlp)
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Justin Payan, Swaroop Mishra, Mukul Singh, Carina Negreanu, Christian Poelitz, Chitta Baral, Subhro Roy, Rasika Chakravarthy, Benjamin Van Durme, Elnaz Nouri
| Challenge: | Large Language Models (LLMs) can solve increasingly complex NLP tasks such as Excel specific tasks. |
| Approach: | They propose a large-scale benchmark to test whether Large Language Models can generate code that solves Excel specific tasks provided via natural language user instructions. |
| Outcome: | The proposed model outperforms existing models and provides a hard benchmark for state of the art models like GPT-4. |
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. |
CogCompNLP: Your Swiss Army Knife for NLP (L18-1)
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Daniel Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling, Dan Roth
| Challenge: | a corpus-reader module supports popular corpora, feature extraction and annotation modules for semantic and syntactic tasks. |
| Approach: | They propose a library that provides modules to address different challenges . they provide a corpus-reader module that supports popular corpora in the NLP community . |
| Outcome: | The proposed library simplifies the process of design and development of NLP applications by providing modules to address different challenges. |
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. |
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation (2022.findings-acl)
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| Challenge: | a low-resource task-oriented semantic parser is limited by privacy requirements for unlabeled natural utterances. |
| Approach: | They propose a setup for low-resource task-oriented semantic parsing based on user interactions . they use structured canonical utterances, then simulating corresponding natural language to improve performance. |
| Outcome: | The proposed setup improves on a low-resource task-oriented semantic parser using utterances collected through user interactions. |