3 citations · 6 across the 8 of their papers we have counts for
8 papers
Jetsons at FinNLP 2024: Towards Understanding the ESG Impact of a News Article using Transformer-based Models
Parag Pravin Dakle, Alolika Gon, Sihan Zha +3
In this paper, we describe the different approaches explored by the Jetsons team for the Multi-Lingual ESG Impact Duration Inference (ML-ESG-3) shared task. The shared task focuses…
BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra
Parker Glenn, Parag Pravin Dakle, Liang Wang +1
Many existing end-to-end systems for hybrid question answering tasks can often be boiled down to a "prompt-and-pray" paradigm, where the user has limited control and insight into t…
Towards leveraging LLMs for Conditional QA
Syed-Amad Hussain, Parag Pravin Dakle, SaiKrishna Rallabandi +1
This study delves into the capabilities and limitations of Large Language Models (LLMs) in the challenging domain of conditional question-answering. Utilizing the Conditional Quest…
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…
Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding
Parker Glenn, Parag Pravin Dakle, Preethi Raghavan
In addressing the task of converting natural language to SQL queries, there are several semantic and syntactic challenges. It becomes increasingly important to understand and remed…
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…
Understanding BLOOM: An empirical study on diverse NLP tasks
Parag Pravin Dakle, SaiKrishna Rallabandi, Preethi Raghavan
We view the landscape of large language models (LLMs) through the lens of the recently released BLOOM model to understand the performance of BLOOM and other decoder-only LLMs compa…
CEREC: A Corpus for Entity Resolution in Email Conversations
Parag Pravin Dakle, Dan I. Moldovan
We present the first large scale corpus for entity resolution in email conversations (CEREC). The corpus consists of 6001 email threads from the Enron Email Corpus containing 36,44…