22 citations · 70 across the 17 of their papers we have counts for
19 papers
FusedRF: Fusing Multiple Radiance Fields
Rahul Goel, Dhawal Sirikonda, Rajvi Shah +1
Radiance Fields (RFs) have shown great potential to represent scenes from casually captured discrete views. Compositing parts or whole of multiple captured scenes could greatly int…
PRESTO: A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs
Rahul Goel, Waleed Ammar, Aditya Gupta +13
Research interest in task-oriented dialogs has increased as systems such as Google Assistant, Alexa and Siri have become ubiquitous in everyday life. However, the impact of academi…
StyleTRF: Stylizing Tensorial Radiance Fields
Rahul Goel, Sirikonda Dhawal, Saurabh Saini +1
Stylized view generation of scenes captured casually using a camera has received much attention recently. The geometry and appearance of the scene are typically captured as neural…
Interactive Segmentation of Radiance Fields
Rahul Goel, Dhawal Sirikonda, Saurabh Saini +1
Radiance Fields (RF) are popular to represent casually-captured scenes for new view synthesis and several applications beyond it. Mixed reality on personal spaces needs understandi…
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue
William Held, Christopher Hidey, Fei Liu +4
Modern virtual assistants use internal semantic parsing engines to convert user utterances to actionable commands. However, prior work has demonstrated that semantic parsing is a d…
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…
Reducing Model Jitter: Stable Re-training of Semantic Parsers in Production Environments
Christopher Hidey, Fei Liu, Rahul Goel
Retraining modern deep learning systems can lead to variations in model performance even when trained using the same data and hyper-parameters by simply using different random seed…
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…
Pre-Trained Language Transformers are Universal Image Classifiers
Rahul Goel, Modar Sulaiman, Kimia Noorbakhsh +4
Facial images disclose many hidden personal traits such as age, gender, race, health, emotion, and psychology. Understanding these traits will help to classify the people in differ…
Do Facial Trait Correlates with Roll Call Voting in Parliament? Using fWHR to Study Performance in Politics
Rahul Goel, Tymofii Brik, Rajesh Sharma
Research has shown that people recognize and select leaders based on their facial appearance. However, considering the correlation between the performance of leaders and their faci…
Misinformation Detection on YouTube Using Video Captions
Raj Jagtap, Abhinav Kumar, Rahul Goel +3
Millions of people use platforms such as YouTube, Facebook, Twitter, and other mass media. Due to the accessibility of these platforms, they are often used to establish a narrative…
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…
HyST: A Hybrid Approach for Flexible and Accurate Dialogue State Tracking
Rahul Goel, Shachi Paul, Dilek Hakkani-Tür
Recent works on end-to-end trainable neural network based approaches have demonstrated state-of-the-art results on dialogue state tracking. The best performing approaches estimate…
Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators
Sanghyun Yi, Rahul Goel, Chandra Khatri +6
Encoder-decoder based neural architectures serve as the basis of state-of-the-art approaches in end-to-end open domain dialog systems. Since most of such systems are trained with a…
Natural Language Generation at Scale: A Case Study for Open Domain Question Answering
Alessandra Cervone, Chandra Khatri, Rahul Goel +4
Current approaches to Natural Language Generation (NLG) for dialog mainly focus on domain-specific, task-oriented applications (e.g. restaurant booking) using limited ontologies (u…
Flexible and Scalable State Tracking Framework for Goal-Oriented Dialogue Systems
Rahul Goel, Shachi Paul, Tagyoung Chung +3
Goal-oriented dialogue systems typically rely on components specifically developed for a single task or domain. This limits such systems in two different ways: If there is an updat…