10 citations · 21 across the 7 of their papers we have counts for
9 papers · 1 filter
CST5: Data Augmentation for Code-Switched Semantic Parsing
Anmol Agarwal, Jigar Gupta, Rahul Goel +3
Extending semantic parsers to code-switched input has been a challenging problem, primarily due to a lack of supervised training data. In this work, we introduce CST5, a new data a…
Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning
Geunseob Oh, Rahul Goel, Chris Hidey +4
Semantic parsing (SP) is a core component of modern virtual assistants like Google Assistant and Amazon Alexa. While sequence-to-sequence-based auto-regressive (AR) approaches are…
TableFormer: Robust Transformer Modeling for Table-Text Encoding
Jingfeng Yang, Aditya Gupta, Shyam Upadhyay +3
Understanding tables is an important aspect of natural language understanding. Existing models for table understanding require linearization of the table structure, where row or co…
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems
Anish Acharya, Suranjit Adhikari, Sanchit Agarwal +28
Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. Trai…
Update Frequently, Update Fast: Retraining Semantic Parsing Systems in a Fraction of Time
Vladislav Lialin, Rahul Goel, Andrey Simanovsky +2
Currently used semantic parsing systems deployed in voice assistants can require weeks to train. Datasets for these models often receive small and frequent updates, data patches. E…
Towards Universal Dialogue Act Tagging for Task-Oriented Dialogues
Shachi Paul, Rahul Goel, Dilek Hakkani-Tür
Machine learning approaches for building task-oriented dialogue systems require large conversational datasets with labels to train on. We are interested in building task-oriented d…