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Found 15 papers, 11 papers with code
CitRet: A Hybrid Model for Cited Text Span Retrieval
The paper aims to identify cited text spans in the reference paper related to the given citance in the citing paper.
Cross-lingual Alignment of Knowledge Graph Triples with Sentences
We propose cross-lingual pairing of English triples with Hindi sentences to mitigate the unavailability of content overlap.
Evaluating Generalizability of Deep Learning Models Using Indian-COVID-19 CT Dataset
One of the major concerns in using ma-chine learning (ML) approaches for automatic processing of CT scan images in clinical setting is that these methods are trained on limited and biased sub-sets of publicly available COVID-19 data.
Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource Languages
We leverage pre-trained language models to solve the task of complex NER for two low-resource languages: Chinese and Spanish.
Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers
Extensive experiments on two benchmark datasets across three state-of-the-art MWP solvers show that proposed methods increase the generalization and robustness of existing solvers.
Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER
We qualitatively analyze the performance of multiple architectures for this task.
Adversarial Examples for Evaluating Math Word Problem Solvers
Standard accuracy metrics have shown that Math Word Problem (MWP) solvers have achieved high performance on benchmark datasets.
A Strong Baseline for Query Efficient Attacks in a Black Box Setting
Also, prior attacks do not maintain a consistent search space while comparing different search methods.
Generating Natural Language Attacks in a Hard Label Black Box Setting
Our proposed attack strategy leverages population-based optimization algorithm to craft plausible and semantically similar adversarial examples by observing only the top label predicted by the target model.
A Context Aware Approach for Generating Natural Language Attacks
We study an important task of attacking natural language processing models in a black box setting.
Scalable, Semi-Supervised Extraction of Structured Information from Scientific Literature
In this paper, we propose a novel, scalable, semi-supervised method for extracting relevant structured information from the vast available raw scientific literature.
Sequential Variational Autoencoders for Collaborative Filtering
We introduce a recurrent version of the VAE, where instead of passing a subset of the whole history regardless of temporal dependencies, we rather pass the consumption sequence subset through a recurrent neural network.
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AutoLearn - Automated Feature Generation and Selection
In recent years, the importance of feature engineering has been confirmed by the exceptional performance of deep learning techniques, that automate this task for some applications.
Injecting Word Embeddings with Another Language's Resource : An Application of Bilingual Embeddings
Word embeddings learned from text corpus can be improved by injecting knowledge from external resources, while at the same time also specializing them for similarity or relatedness.
