Search Results for author:
Found 11 papers, 6 papers with code
MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs
Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace.
Efficient Neural Ranking using Forward Indexes
In this paper, we propose the Fast-Forward index -- a simple vector forward index that facilitates ranking documents using interpolation of lexical and semantic scores -- as a replacement for contextual re-rankers and dense indexes based on nearest neighbor search.
FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop
It is therefore important to conduct user studies to correct models' inference biases and improve the model in a life-long learning manner in the future according to the user feedback.
Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents
Automatic summarization of legal case documents is an important and practical challenge.
Learnt Sparsity for Effective and Interpretable Document Ranking
We also show that our sentence selection approach can be used to provide explanations for models that operate on only parts of the document, such as BERT.
An In-depth Analysis of Passage-Level Label Transfer for Contextual Document Ranking
Recently introduced pre-trained contextualized autoregressive models like BERT have shown improvements in document retrieval tasks.
Explain and Predict, and then Predict Again
A desirable property of learning systems is to be both effective and interpretable.
Stance Detection in Web and Social Media: A Comparative Study
Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances.
Summarizing Situational and Topical Information During Crises
The use of microblogging platforms such as Twitter during crises has become widespread.
Functions of Code-Switching in Tweets: An Annotation Framework and Some Initial Experiments
Code-Switching (CS) between two languages is extremely common in communities with societal multilingualism where speakers switch between two or more languages when interacting with each other.
