no code implementations • ACL (CODI, CRAC) 2021 • Joseph Renner, Priyansh Trivedi, Gaurav Maheshwari, Rémi Gilleron, Pascal Denis
We demonstrate the performance of an end-to-end transformer-based higher-order coreference model finetuned for the task of full bridging.
no code implementations • 18 Sep 2024 • Gaurav Maheshwari, Dmitry Ivanov, Kevin El Haddad
Large language models (LLMs) have enabled a range of applications in zero-shot and few-shot learning settings, including the generation of synthetic datasets for training and testing.
1 code implementation • 18 Sep 2024 • Gaurav Maheshwari, Dmitry Ivanov, Théo Johannet, Kevin El Haddad
Automatic Speech Recognition (ASR) systems have achieved remarkable performance on widely used benchmarks such as LibriSpeech and Fleurs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
no code implementations • 23 May 2024 • Gaurav Maheshwari, Aurélien Bellet, Pascal Denis, Mikaela Keller
In this paper, we introduce a data augmentation approach specifically tailored to enhance intersectional fairness in classification tasks.
no code implementations • 21 May 2023 • Gaurav Maheshwari, Aurélien Bellet, Pascal Denis, Mikaela Keller
In this work, we consider the problem of intersectional group fairness in the classification setting, where the objective is to learn discrimination-free models in the presence of several intersecting sensitive groups.
no code implementations • 22 Jun 2022 • Gaurav Maheshwari, Michaël Perrot
We address the problem of group fairness in classification, where the objective is to learn models that do not unjustly discriminate against subgroups of the population.
1 code implementation • 12 May 2022 • Gaurav Maheshwari, Pascal Denis, Mikaela Keller, Aurélien Bellet
Encoded text representations often capture sensitive attributes about individuals (e. g., race or gender), which raise privacy concerns and can make downstream models unfair to certain groups.
1 code implementation • EMNLP 2020 • Mikhail Galkin, Priyansh Trivedi, Gaurav Maheshwari, Ricardo Usbeck, Jens Lehmann
We also demonstrate that existing benchmarks for evaluating link prediction (LP) performance on hyper-relational KGs suffer from fundamental flaws and thus develop a new Wikidata-based dataset - WD50K.
Ranked #2 on
Link Prediction
on JF17K
no code implementations • 22 Jul 2019 • Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann, Asja Fischer
Question answering has emerged as an intuitive way of querying structured data sources, and has attracted significant advancements over the years.
1 code implementation • 2 Nov 2018 • Gaurav Maheshwari, Priyansh Trivedi, Denis Lukovnikov, Nilesh Chakraborty, Asja Fischer, Jens Lehmann
In this paper, we conduct an empirical investigation of neural query graph ranking approaches for the task of complex question answering over knowledge graphs.
no code implementations • 11 Feb 2018 • Sourish Dasgupta, Ankur Padia, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann
Ontology learning (OL) is the process of automatically generating an ontological knowledge base from a plain text document.
no code implementations • 15 Nov 2016 • Gaurav Maheshwari, Priyansh Trivedi, Harshita Sahijwani, Kunal Jha, Sourish Dasgupta, Jens Lehmann
Document similarity is the problem of estimating the degree to which a given pair of documents has similar semantic content.
no code implementations • 19 Mar 2015 • Sourish Dasgupta, Gaurav Maheshwari, Priyansh Trivedi
In this paper, we propose an algebraic similarity measure {\sigma}BS (BS stands for BitSim) for assigning semantic similarity score to concept definitions in ALCH+ an expressive fragment of Description Logics (DL).