Search Results for author:
Found 8 papers, 5 papers with code
Enolp musk@SMM4H’22 : Leveraging Pre-trained Language Models for Stance And Premise Classification
This paper covers our approaches for the Social Media Mining for Health (SMM4H) Shared Tasks 2a and 2b.
CABINET: Content Relevance based Noise Reduction for Table Question Answering
Typically, only a small part of the whole table is relevant to derive the answer for a given question.
Ranked #1 on
Semantic Parsing
on WikiSQL
(Denotation accuracy (test) metric)
SepHRNet: Generating High-Resolution Crop Maps from Remote Sensing imagery using HRNet with Separable Convolution
One way to achieve this is by analyzing high-resolution satellite imagery.
$FastDoc$: Domain-Specific Fast Pre-training Technique using Document-Level Metadata and Taxonomy
As the demand for sophisticated Natural Language Processing (NLP) models continues to grow, so does the need for efficient pre-training techniques.
An Evaluation Framework for Legal Document Summarization
A law practitioner has to go through numerous lengthy legal case proceedings for their practices of various categories, such as land dispute, corruption, etc.
Fine-grained Intent Classification in the Legal Domain
A law practitioner has to go through a lot of long legal case proceedings.
Team Enigma at ArgMining-EMNLP 2021: Leveraging Pre-trained Language Models for Key Point Matching
We present the system description for our submission towards the Key Point Analysis Shared Task at ArgMining 2021.
A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks
In this paper, we implement a combination of technical analysis and machine/deep learning-based analysis to build a trend classification model.
