no code implementations • 23 Feb 2024 • Divya Jyoti Bajpai, Ayush Maheshwari, Manjesh Kumar Hanawal, Ganesh Ramakrishnan
The availability of large annotated data can be a critical bottleneck in training machine learning algorithms successfully, especially when applied to diverse domains.
1 code implementation • 23 Nov 2023 • Abhishek Singh, Venkatapathy Subramanian, Ayush Maheshwari, Pradeep Narayan, Devi Prasad Shetty, Ganesh Ramakrishnan
We empirically show that our EIGEN framework can significantly improve the performance of state-of-the-art deep models with the availability of very few labeled data instances.
no code implementations • 23 May 2023 • Ayush Maheshwari, Ashim Gupta, Amrith Krishna, Ganesh Ramakrishnan, G. Anil Kumar, Jitin Singla
We include training splits from our contemporary dataset and the Sanskrit-English parallel sentences from the training split of Itih\={a}sa, a previously released classical era machine translation dataset containing Sanskrit.
1 code implementation • 15 Nov 2022 • Ayush Maheshwari, Nikhil Singh, Amrith Krishna, Ganesh Ramakrishnan
Keeping this in mind, we release a multi-domain dataset, from areas as diverse as astronomy, medicine and mathematics, with some of them as old as 18 centuries.
no code implementations • 13 Oct 2022 • Ayush Maheshwari, Piyush Sharma, Preethi Jyothi, Ganesh Ramakrishnan
In this work we present \dictdis, a lexically constrained NMT system that disambiguates between multiple candidate translations derived from dictionaries.
1 code implementation • 3 Mar 2022 • Ayush Maheshwari, Ajay Ravindran, Venkatapathy Subramanian, Ganesh Ramakrishnan
UDAAN has an end-to-end Machine Translation (MT) plus post-editing pipeline wherein users can upload a document to obtain raw MT output.
1 code implementation • 7 Feb 2022 • Durga Sivasubramanian, Ayush Maheshwari, Pradeep Shenoy, Prathosh AP, Ganesh Ramakrishnan
In several supervised learning scenarios, auxiliary losses are used in order to introduce additional information or constraints into the supervised learning objective.
no code implementations • 2 Feb 2022 • Samrat Dutta, Shreyansh Jain, Ayush Maheshwari, Souvik Pal, Ganesh Ramakrishnan, Preethi Jyothi
Post-editing in Automatic Speech Recognition (ASR) entails automatically correcting common and systematic errors produced by the ASR system.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
1 code implementation • Findings (ACL) 2022 • Ayush Maheshwari, KrishnaTeja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer, Marina Danilevsky, Lucian Popa
These LFs, in turn, have been used to generate a large amount of additional noisy labeled data, in a paradigm that is now commonly referred to as data programming.
1 code implementation • 1 Aug 2021 • Guttu Sai Abhishek, Harshad Ingole, Parth Laturia, Vineeth Dorna, Ayush Maheshwari, Rishabh Iyer, Ganesh Ramakrishnan
SPEAR facilitates weak supervision in the form of heuristics (or rules) and association of noisy labels to the training dataset.
1 code implementation • Findings (ACL) 2021 • Atul Sahay, Anshul Nasery, Ayush Maheshwari, Ganesh Ramakrishnan, Rishabh Iyer
We introduce a novel formulation that takes advantage of the syntactic grammar rules and is independent of the base system.
1 code implementation • 11 Apr 2021 • Atul Sahay, Ayush Maheshwari, Ritesh Kumar, Ganesh Ramakrishnan, Manjesh Kumar Hanawal, Kavi Arya
In this work, we propose an attention mechanism over Tree-LSTMs to learn more meaningful and explainable parse tree structures.
1 code implementation • EACL 2021 • Soumya Chatterjee, Ayush Maheshwari, Ganesh Ramakrishnan, Saketha Nath Jagaralpudi
Such a joint learning is expected to provide a twofold advantage: i) the classifier generalizes better as it leverages the prior knowledge of existence of a hierarchy over the labels, and ii) in addition to the label co-occurrence information, the label-embedding may benefit from the manifold structure of the input datapoints, leading to embeddings that are more faithful to the label hierarchy.
Ranked #1 on Multi-Label Text Classification on RCV1
General Classification Hierarchical Multi-label Classification +1
1 code implementation • Findings (ACL) 2021 • Ayush Maheshwari, Oishik Chatterjee, KrishnaTeja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer
The first contribution of this work is an introduction of a framework, \model which is a semi-supervised data programming paradigm that learns a \emph{joint model} that effectively uses the rules/labelling functions along with semi-supervised loss functions on the feature space.
no code implementations • 19 Aug 2019 • Ayush Maheshwari, Hrishikesh Patel, Nandan Rathod, Ritesh Kumar, Ganesh Ramakrishnan, Pushpak Bhattacharyya
The problem of event extraction is a relatively difficult task for low resource languages due to the non-availability of sufficient annotated data.
no code implementations • NAACL 2018 • Ayush Maheshwari, Vishwajeet Kumar, Ganesh Ramakrishnan, J. Saketha Nath
We present a system for resolving entities and disambiguating locations based on publicly available web data in the domain of ancient Hindu Temples.