no code implementations • 14 Apr 2022 • Geunseob Oh, Rahul Goel, Chris Hidey, Shachi Paul, Aditya Gupta, Pararth Shah, Rushin Shah
As the top-level intent largely governs the syntax and semantics of a parse, the intent conditioning allows the model to better control beam search and improves the quality and diversity of top-k outputs.
1 code implementation • EMNLP (NLP4ConvAI) 2021 • David Gaddy, Alex Kouzemtchenko, Pavankumar Reddy Muddireddy, Prateek Kolhar, Rushin Shah
In this paper, we explore how to use a small amount of new data to update a task-oriented semantic parsing model when the desired output for some examples has changed.
no code implementations • 15 Oct 2020 • Vladislav Lialin, Rahul Goel, Andrey Simanovsky, Anna Rumshisky, Rushin Shah
To reduce training time, one can fine-tune the previously trained model on each patch, but naive fine-tuning exhibits catastrophic forgetting - degradation of the model performance on the data not represented in the data patch.
no code implementations • 12 Nov 2019 • Arash Einolghozati, Sonal Gupta, Mrinal Mohit, Rushin Shah
However, evaluating a model's robustness to these changes is harder for language since words are discrete and an automated change (e. g. adding `noise') to a query sometimes changes the meaning and thus labels of a query.
no code implementations • IJCNLP 2019 • Panupong Pasupat, Sonal Gupta, M, Karishma yam, Rushin Shah, Mike Lewis, Luke Zettlemoyer
We propose a semantic parser for parsing compositional utterances into Task Oriented Parse (TOP), a tree representation that has intents and slots as labels of nesting tree nodes.
no code implementations • 15 Feb 2019 • Arash Einolghozati, Panupong Pasupat, Sonal Gupta, Rushin Shah, Mrinal Mohit, Mike Lewis, Luke Zettlemoyer
Semantic parsing using hierarchical representations has recently been proposed for task oriented dialog with promising results [Gupta et al 2018].
2 code implementations • 12 Dec 2018 • Ahmed Aly, Kushal Lakhotia, Shicong Zhao, Mrinal Mohit, Barlas Oguz, Abhinav Arora, Sonal Gupta, Christopher Dewan, Stef Nelson-Lindall, Rushin Shah
We introduce PyText - a deep learning based NLP modeling framework built on PyTorch.
no code implementations • NAACL 2019 • Sebastian Schuster, Sonal Gupta, Rushin Shah, Mike Lewis
We use this data set to evaluate three different cross-lingual transfer methods: (1) translating the training data, (2) using cross-lingual pre-trained embeddings, and (3) a novel method of using a multilingual machine translation encoder as contextual word representations.
no code implementations • EMNLP 2018 • Sonal Gupta, Rushin Shah, Mrinal Mohit, Anuj Kumar, Mike Lewis
Task oriented dialog systems typically first parse user utterances to semantic frames comprised of intents and slots.