Pratik Joshi


2020

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The State and Fate of Linguistic Diversity and Inclusion in the NLP World
Pratik Joshi | Sebastin Santy | Amar Budhiraja | Kalika Bali | Monojit Choudhury
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Language technologies contribute to promoting multilingualism and linguistic diversity around the world. However, only a very small number of the over 7000 languages of the world are represented in the rapidly evolving language technologies and applications. In this paper we look at the relation between the types of languages, resources, and their representation in NLP conferences to understand the trajectory that different languages have followed over time. Our quantitative investigation underlines the disparity between languages, especially in terms of their resources, and calls into question the “language agnostic” status of current models and systems. Through this paper, we attempt to convince the ACL community to prioritise the resolution of the predicaments highlighted here, so that no language is left behind.

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Crowdsourcing Speech Data for Low-Resource Languages from Low-Income Workers
Basil Abraham | Danish Goel | Divya Siddarth | Kalika Bali | Manu Chopra | Monojit Choudhury | Pratik Joshi | Preethi Jyoti | Sunayana Sitaram | Vivek Seshadri
Proceedings of the Twelfth Language Resources and Evaluation Conference

Voice-based technologies are essential to cater to the hundreds of millions of new smartphone users. However, most of the languages spoken by these new users have little to no labelled speech data. Unfortunately, collecting labelled speech data in any language is an expensive and resource-intensive task. Moreover, existing platforms typically collect speech data only from urban speakers familiar with digital technology whose dialects are often very different from low-income users. In this paper, we explore the possibility of collecting labelled speech data directly from low-income workers. In addition to providing diversity to the speech dataset, we believe this approach can also provide valuable supplemental earning opportunities to these communities. To this end, we conducted a study where we collected labelled speech data in the Marathi language from three different user groups: low-income rural users, low-income urban users, and university students. Overall, we collected 109 hours of data from 36 participants. Our results show that the data collected from low-income participants is of comparable quality to the data collected from university students (who are typically employed to do this work) and that crowdsourcing speech data from low-income rural and urban workers is a viable method of gathering speech data.

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TaxiNLI: Taking a Ride up the NLU Hill
Pratik Joshi | Somak Aditya | Aalok Sathe | Monojit Choudhury
Proceedings of the 24th Conference on Computational Natural Language Learning

Pre-trained Transformer-based neural architectures have consistently achieved state-of-the-art performance in the Natural Language Inference (NLI) task. Since NLI examples encompass a variety of linguistic, logical, and reasoning phenomena, it remains unclear as to which specific concepts are learnt by the trained systems and where they can achieve strong generalization. To investigate this question, we propose a taxonomic hierarchy of categories that are relevant for the NLI task. We introduce TaxiNLI, a new dataset, that has 10k examples from the MNLI dataset with these taxonomic labels. Through various experiments on TaxiNLI, we observe that whereas for certain taxonomic categories SOTA neural models have achieved near perfect accuracies—a large jump over the previous models—some categories still remain difficult. Our work adds to the growing body of literature that shows the gaps in the current NLI systems and datasets through a systematic presentation and analysis of reasoning categories.

2019

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CoSSAT: Code-Switched Speech Annotation Tool
Sanket Shah | Pratik Joshi | Sebastin Santy | Sunayana Sitaram
Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP

Code-switching refers to the alternation of two or more languages in a conversation or utterance and is common in multilingual communities across the world. Building code-switched speech and natural language processing systems are challenging due to the lack of annotated speech and text data. We present a speech annotation interface CoSSAT, which helps annotators transcribe code-switched speech faster, more easily and more accurately than a traditional interface, by displaying candidate words from monolingual speech recognizers. We conduct a user study on the transcription of Hindi-English code-switched speech with 10 annotators and describe quantitative and qualitative results.

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Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities
Pratik Joshi | Christain Barnes | Sebastin Santy | Simran Khanuja | Sanket Shah | Anirudh Srinivasan | Satwik Bhattamishra | Sunayana Sitaram | Monojit Choudhury | Kalika Bali
Proceedings of the 16th International Conference on Natural Language Processing

In this paper, we examine and analyze the challenges associated with developing and introducing language technologies to low-resource language communities. While doing so we bring to light the successes and failures of past work in this area, challenges being faced in doing so, and what have they achieved. Throughout this paper, we take a problem-facing approach and describe essential factors which the success of such technologies hinges upon. We present the various aspects in a manner which clarify and lay out the different tasks involved, which can aid organizations looking to make an impact in this area. We take the example of Gondi, an extremely-low resource Indian language, to reinforce and complement our discussion.