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Found 16 papers, 8 papers with code
Data Augmentation for Sample Efficient and Robust Document Ranking
We then adapt a family of contrastive losses for the document ranking task that can exploit the augmented data to learn an effective ranking model.
Efficient Neural Ranking using Forward Indexes and Lightweight Encoders
Dual-encoder-based dense retrieval models have become the standard in IR.
Understanding Lexical Biases when Identifying Gang-related Social Media Communications
Individuals involved in gang-related activity use mainstream social media including Facebook and Twitter to express taunts and threats as well as grief and memorializing.
A Review of the Role of Causality in Developing Trustworthy AI Systems
This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models.
Supervised Contrastive Learning Approach for Contextual Ranking
This paper proposes a simple yet effective method to improve ranking performance on smaller datasets using supervised contrastive learning for the document ranking problem.
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.
Extractive Explanations for Interpretable Text Ranking
We introduce the Select-and-Rank paradigm for document ranking, where we first output an explanation as a selected subset of sentences in a document.
An In-depth Analysis of Passage-Level Label Transfer for Contextual Document Ranking
However, the documents are longer than the passages and such document ranking models suffer from the token limitation (512) of BERT.
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.
