20 citations · 22 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
Domain-matched Pre-training Tasks for Dense Retrieval
Barlas Oğuz, Kushal Lakhotia, Anchit Gupta +8
Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased performance across almost all NLP tasks. A notable exception is information ret…
Muppet: Massive Multi-task Representations with Pre-Finetuning
Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava +3
We propose pre-finetuning, an additional large-scale learning stage between language model pre-training and fine-tuning. Pre-finetuning is massively multi-task learning (around 50…
Sound Natural: Content Rephrasing in Dialog Systems
Arash Einolghozati, Anchit Gupta, Keith Diedrick +1
We introduce a new task of rephrasing for a more natural virtual assistant. Currently, virtual assistants work in the paradigm of intent slot tagging and the slot values are direct…
MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark
Haoran Li, Abhinav Arora, Shuohui Chen +3
Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets. Available datasets su…