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20182022
most citedPyText: A Seamless Path from NLP research to production

16 citations · 47 across the 9 of their papers we have counts for

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10 papers · 1 filter

cs.CL2022

Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing

Akshat Shrivastava, Shrey Desai, Anchit Gupta +4

Task-oriented semantic parsing models have achieved strong results in recent years, but unfortunately do not strike an appealing balance between model size, runtime latency, and cr…

cs.CL20213 cited

AutoNLU: Detecting, root-causing, and fixing NLU model errors

Pooja Sethi, Denis Savenkov, Forough Arabshahi +6

Improving the quality of Natural Language Understanding (NLU) models, and more specifically, task-oriented semantic parsing models, in production is a cumbersome task. In this work…

cs.CL2021

Assessing Data Efficiency in Task-Oriented Semantic Parsing

Shrey Desai, Akshat Shrivastava, Justin Rill +4

Data efficiency, despite being an attractive characteristic, is often challenging to measure and optimize for in task-oriented semantic parsing; unlike exact match, it can require…

cs.CL2021

Diagnosing Transformers in Task-Oriented Semantic Parsing

Shrey Desai, Ahmed Aly

Modern task-oriented semantic parsing approaches typically use seq2seq transformers to map textual utterances to semantic frames comprised of intents and slots. While these models…

cs.CL202112 cited

Low-Resource Task-Oriented Semantic Parsing via Intrinsic Modeling

Shrey Desai, Akshat Shrivastava, Alexander Zotov +1

Task-oriented semantic parsing models typically have high resource requirements: to support new ontologies (i.e., intents and slots), practitioners crowdsource thousands of samples…

cs.CL20211 cited

Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog

Arun Babu, Akshat Shrivastava, Armen Aghajanyan +3

Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models. In spite of these advantages, widespr…