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20192022
most citedGenie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands

29 citations · 42 across the 6 of their papers we have counts for

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Showing cs.CLShow all

5 papers · 1 filter

cs.CL20213 cited

Grounding Open-Domain Instructions to Automate Web Support Tasks

Nancy Xu, Sam Masling, Michael Du +4

Grounding natural language instructions on the web to perform previously unseen tasks enables accessibility and automation. We introduce a task and dataset to train AI agents from…

cs.CL2020

Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation

Mehrad Moradshahi, Giovanni Campagna, Sina J. Semnani +2

We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language. Our methodology is t…

cs.CL20201 cited

Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking

Giovanni Campagna, Agata Foryciarz, Mehrad Moradshahi +1

Zero-shot transfer learning for multi-domain dialogue state tracking can allow us to handle new domains without incurring the high cost of data acquisition. This paper proposes new…

cs.CL2019

HUBERT Untangles BERT to Improve Transfer across NLP Tasks

Mehrad Moradshahi, Hamid Palangi, Monica S. Lam +2

We introduce HUBERT which combines the structured-representational power of Tensor-Product Representations (TPRs) and BERT, a pre-trained bidirectional Transformer language model.…

cs.CL201929 cited

Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands

Giovanni Campagna, Silei Xu, Mehrad Moradshahi +2

To understand diverse natural language commands, virtual assistants today are trained with numerous labor-intensive, manually annotated sentences. This paper presents a methodology…