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Found 20 papers, 11 papers with code
STHAL: Location-mention Identification in Tweets of Indian-context
We investigate the problem of extracting Indian-locations from a given crowd-sourced textual dataset.
Multi-Relational Graph Transformer for Automatic Short Answer Grading
A Graph Transformer is used to prepare relation-specific token embeddings within each subgraph, then aggregated to obtain a subgraph representation.
Probing Critical Learning Dynamics of PLMs for Hate Speech Detection
Despite the widespread adoption, there is a lack of research into how various critical aspects of pretrained language models (PLMs) affect their performance in hate speech detection.
Exploiting Representation Bias for Data Distillation in Abstractive Text Summarization
We employ clustering techniques to learn the diversity of a model's sample space and how data points are mapped from the embedding space to the encoder space and vice versa.
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selection
In this work, we explore the usage of an ensemble of deep learning models as our thief model.
MWPRanker: An Expression Similarity Based Math Word Problem Retriever
Math Word Problems (MWPs) in online assessments help test the ability of the learner to make critical inferences by interpreting the linguistic information in them.
Fusing Multimodal Signals on Hyper-complex Space for Extreme Abstractive Text Summarization (TL;DR) of Scientific Contents
In this paper, we deal with a novel task of extreme abstractive text summarization (aka TL;DR generation) by leveraging multiple input modalities.
Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment
Our proposed solution HEN-mBERT is a modular, multilingual, mixture-of-experts model that enriches the linguistic subspace with latent endogenous signals from history, topology, and exemplars.
Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising
This allows for learning a denoising function that operates over both aspects and produces semantically equivalent and syntactically diverse outputs through grounded noise injection.
Unsupervised Question Duplicate and Related Questions Detection in e-learning platforms
The repository of questions has to be constantly updated to ensure a diverse pool of questions to conduct assessments for learners.
TagRec++: Hierarchical Label Aware Attention Network for Question Categorization
Online learning systems have multiple data repositories in the form of transcripts, books and questions.
'John ate 5 apples' != 'John ate some apples': Self-Supervised Paraphrase Quality Detection for Algebraic Word Problems
There is a need for paraphrase scoring methods in the context of AWP to enable the training of good paraphrasers.
Auxiliary Task Guided Interactive Attention Model for Question Difficulty Prediction
Hence we soft label another dataset with a model fine-tuned to predict Bloom's labels to demonstrate the applicability of our method to datasets with only difficulty labels.
Topic Aware Contextualized Embeddings for High Quality Phrase Extraction
The salient phrases are extracted using a ranking algorithm on an undirected graph constructed for the given document.
TagRec: Automated Tagging of Questions with Hierarchical Learning Taxonomy
The loss function maximizes the similarity between the question-answer pair and the correct label representations and minimizes the similarity to unrelated labels.
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See, Hear, Read: Leveraging Multimodality with Guided Attention for Abstractive Text Summarization
However, existing methods use short videos as the visual modality and short summary as the ground-truth, therefore, perform poorly on lengthy videos and long ground-truth summary.
Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter
Meanwhile, to predict the retweet dynamics on Twitter, we propose RETINA, a novel neural architecture that incorporates exogenous influence using scaled dot-product attention.
Interpretability of Blackbox Machine Learning Models through Dataview Extraction and Shadow Model creation
Different deep learning models built on the same training data may capture different views of the data based on the underlying techniques used.
DiffQue: Estimating Relative Difficulty of Questions in Community Question Answering Services
In this paper, we propose DiffQue, a novel system that maps this problem to a network-aided edge directionality prediction problem.
Extracting Fairness Policies from Legal Documents
Automatic extraction of fairness policies, or in general, any specific kind of policies from large legal corpus can be very useful for the study of bias and fairness in the context of AI applications.
