Rishabh Iyer


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DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation
Suraj Kothawade | Anmol Mekala | D.Chandra Sekhara Hetha Havya | Mayank Kothyari | Rishabh Iyer | Ganesh Ramakrishnan | Preethi Jyothi
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

State-of-the-art Automatic Speech Recognition (ASR) systems are known to exhibit disparate performance on varying speech accents. To improve performance on a specific target accent, a commonly adopted solution is to finetune the ASR model using accent-specific labeled speech. However, acquiring large amounts of labeled speech for specific target accents is challenging. Choosing an informative subset of speech samples that are most representative of the target accents becomes important for effective ASR finetuning. To address this problem, we propose DITTO (Data-efficient and faIr Targeted subseT selectiOn that uses Submodular Mutual Information (SMI) functions as acquisition functions to find the most informative set of utterances matching a target accent within a fixed budget. An important feature of DITTO is that it supports fair targeting for multiple accents, i.e. it can automatically select representative data points from multiple accents when the ASR model needs to perform well on more than one accent. We show that compared to other speech selection methods, DITTO is 3-5 times as label-efficient for its improvements on the Indic-TTS and L2 datasets.

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INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Language Models
H S V N S Kowndinya Renduchintala | Krishnateja Killamsetty | Sumit Bhatia | Milan Aggarwal | Ganesh Ramakrishnan | Rishabh Iyer | Balaji Krishnamurthy
Findings of the Association for Computational Linguistics: EMNLP 2023

A salient characteristic of pre-trained language models (PTLMs) is a remarkable improvement in their generalization capability and emergence of new capabilities with increasing model capacity and pre-training dataset size. Consequently, we are witnessing the development of enormous models pushing the state-of-the-art. It is, however, imperative to realize that this inevitably leads to prohibitively long training times, extortionate computing costs, and a detrimental environmental impact. Significant efforts are underway to make PTLM training more efficient through innovations in model architectures, training pipelines, and loss function design, with scant attention being paid to optimizing the utility of training data. The key question that we ask is whether it is possible to train PTLMs by employing only highly informative subsets of the training data while maintaining downstream performance? Building upon the recent progress in informative data subset selection, we show how we can employ submodular optimization to select highly representative subsets of the training corpora and demonstrate that the proposed framework can be applied to efficiently train multiple PTLMs (BERT, BioBERT, GPT-2) using only a fraction of data. Further, we perform a rigorous empirical evaluation to show that the resulting models achieve up to ~99% of the performance of the fully-trained models. We made our framework publicly available at https://github.com/Efficient-AI/ingenious.


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SPEAR : Semi-supervised Data Programming in Python
Guttu Abhishek | Harshad Ingole | Parth Laturia | Vineeth Dorna | Ayush Maheshwari | Ganesh Ramakrishnan | Rishabh Iyer
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present SPEAR, an open-source python library for data programming with semi supervision. The package implements several recent data programming approaches including facility to programmatically label and build training data. SPEAR facilitates weak supervision in the form of heuristics (or rules) and association of noisy labels to the training dataset. These noisy labels are aggregated to assign labels to the unlabeled data for downstream tasks. We have implemented several label aggregation approaches that aggregate the noisy labels and then train using the noisily labeled set in a cascaded manner. Our implementation also includes other approaches that jointly aggregate and train the model for text classification tasks. Thus, in our python package, we integrate several cascade and joint data-programming approaches while also providing the facility of data programming by letting the user define labeling functions or rules. The code and tutorial notebooks are available at https://github.com/decile-team/spear. Further, extensive documentation can be found at https://spear-decile.readthedocs.io/. Video tutorials demonstrating the usage of our package are available https://youtube.com/playlist?list=PLW8agt_HvkVnOJoJAqBpaerFb-z-ZlqlP. We also present some real-world use cases of SPEAR.

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Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming
Ayush Maheshwari | Krishnateja Killamsetty | Ganesh Ramakrishnan | Rishabh Iyer | Marina Danilevsky | Lucian Popa
Findings of the Association for Computational Linguistics: ACL 2022

A critical bottleneck in supervised machine learning is the need for large amounts of labeled data which is expensive and time-consuming to obtain. Although a small amount of labeled data cannot be used to train a model, it can be used effectively for the generation of humaninterpretable labeling functions (LFs). 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. Previous methods of generating LFs do not attempt to use the given labeled data further to train a model, thus missing opportunities for improving performance. Additionally, since the LFs are generated automatically, they are likely to be noisy, and naively aggregating these LFs can lead to suboptimal results. In this work, we propose an LF-based bi-level optimization framework WISDOM to solve these two critical limitations. WISDOM learns a joint model on the (same) labeled dataset used for LF induction along with any unlabeled data in a semi-supervised manner, and more critically, reweighs each LF according to its goodness, influencing its contribution to the semi-supervised loss using a robust bi-level optimization algorithm. We show that WISDOM significantly outperforms prior approaches on several text classification datasets.

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Partitioned Gradient Matching-based Data Subset Selection for Compute-Efficient Robust ASR Training
Ashish Mittal | Durga Sivasubramanian | Rishabh Iyer | Preethi Jyothi | Ganesh Ramakrishnan
Findings of the Association for Computational Linguistics: EMNLP 2022

Training state-of-the-art ASR systems such as RNN-T often has a high associated financial and environmental cost. Training with a subset of training data could mitigate this problem if the subset selected could achieve on-par performance with training with the entire dataset. Although there are many data subset selection(DSS) algorithms, direct application to the RNN-T is difficult, especially the DSS algorithms that are adaptive and use learning dynamics such as gradients, as RNN-T tend to have gradients with a significantly larger memory footprint. In this paper, we propose Partitioned Gradient Matching (PGM) a novel distributable DSS algorithm, suitable for massive datasets like those used to train RNN-T. Through extensive experiments on Librispeech 100H and Librispeech 960H, we show that PGM achieves between 3x to 6x speedup with only a very small accuracy degradation (under 1% absolute WER difference). In addition, we demonstrate similar results for PGM even in settings where the training data is corrupted with noise.


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Semi-Supervised Data Programming with Subset Selection
Ayush Maheshwari | Oishik Chatterjee | Krishnateja Killamsetty | Ganesh Ramakrishnan | Rishabh Iyer
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Rule Augmented Unsupervised Constituency Parsing
Atul Sahay | Anshul Nasery | Ayush Maheshwari | Ganesh Ramakrishnan | Rishabh Iyer
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Summarization of Multi-Document Topic Hierarchies using Submodular Mixtures
Ramakrishna Bairi | Rishabh Iyer | Ganesh Ramakrishnan | Jeff Bilmes
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)