7 citations · 9 across the 4 of their papers we have counts for
10 papers · 1 filter
Self-training Strategies for Sentiment Analysis: An Empirical Study
Haochen Liu, Sai Krishna Rallabandi, Yijing Wu +2
Sentiment analysis is a crucial task in natural language processing that involves identifying and extracting subjective sentiment from text. Self-training has recently emerged as a…
HeySQuAD: A Spoken Question Answering Dataset
Yijing Wu, SaiKrishna Rallabandi, Ravisutha Srinivasamurthy +3
Spoken question answering (SQA) systems are critical for digital assistants and other real-world use cases, but evaluating their performance is a challenge due to the importance of…
Switch Point biased Self-Training: Re-purposing Pretrained Models for Code-Switching
Parul Chopra, Sai Krishna Rallabandi, Alan W Black +1
Code-switching (CS), a ubiquitous phenomenon due to the ease of communication it offers in multilingual communities still remains an understudied problem in language processing. Th…
Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems
Akshat Gupta, Olivia Deng, Akruti Kushwaha +4
Intent Recognition and Slot Identification are crucial components in spoken language understanding (SLU) systems. In this paper, we present a novel approach towards both these task…
Unsupervised Self-Training for Sentiment Analysis of Code-Switched Data
Akshat Gupta, Sargam Menghani, Sai Krishna Rallabandi +1
Sentiment analysis is an important task in understanding social media content like customer reviews, Twitter and Facebook feeds etc. In multilingual communities around the world, a…
Task-Specific Pre-Training and Cross Lingual Transfer for Code-Switched Data
Akshat Gupta, Sai Krishna Rallabandi, Alan Black
Using task-specific pre-training and leveraging cross-lingual transfer are two of the most popular ways to handle code-switched data. In this paper, we aim to compare the effects o…