Khyathi Raghavi Chandu


2021

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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann | Tosin Adewumi | Karmanya Aggarwal | Pawan Sasanka Ammanamanchi | Anuoluwapo Aremu | Antoine Bosselut | Khyathi Raghavi Chandu | Miruna-Adriana Clinciu | Dipanjan Das | Kaustubh Dhole | Wanyu Du | Esin Durmus | Ondřej Dušek | Chris Chinenye Emezue | Varun Gangal | Cristina Garbacea | Tatsunori Hashimoto | Yufang Hou | Yacine Jernite | Harsh Jhamtani | Yangfeng Ji | Shailza Jolly | Mihir Kale | Dhruv Kumar | Faisal Ladhak | Aman Madaan | Mounica Maddela | Khyati Mahajan | Saad Mahamood | Bodhisattwa Prasad Majumder | Pedro Henrique Martins | Angelina McMillan-Major | Simon Mille | Emiel van Miltenburg | Moin Nadeem | Shashi Narayan | Vitaly Nikolaev | Andre Niyongabo Rubungo | Salomey Osei | Ankur Parikh | Laura Perez-Beltrachini | Niranjan Ramesh Rao | Vikas Raunak | Juan Diego Rodriguez | Sashank Santhanam | João Sedoc | Thibault Sellam | Samira Shaikh | Anastasia Shimorina | Marco Antonio Sobrevilla Cabezudo | Hendrik Strobelt | Nishant Subramani | Wei Xu | Diyi Yang | Akhila Yerukola | Jiawei Zhou
Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for the 2021 shared task at the associated GEM Workshop.

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CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing
Sai Muralidhar Jayanthi | Kavya Nerella | Khyathi Raghavi Chandu | Alan W Black
Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching

The NLP community has witnessed steep progress in a variety of tasks across the realms of monolingual and multilingual language processing recently. These successes, in conjunction with the proliferating mixed language interactions on social media, have boosted interest in modeling code-mixed texts. In this work, we present CodemixedNLP, an open-source library with the goals of bringing together the advances in code-mixed NLP and opening it up to a wider machine learning community. The library consists of tools to develop and benchmark versatile model architectures that are tailored for mixed texts, methods to expand training sets, techniques to quantify mixing styles, and fine-tuned state-of-the-art models for 7 tasks in Hinglish. We believe this work has the potential to foster a distributed yet collaborative and sustainable ecosystem in an otherwise dispersed space of code-mixing research. The toolkit is designed to be simple, easily extensible, and resourceful to both researchers as well as practitioners. Demo: <http://k-ikkees.pc.cs.cmu.edu:5000> and Library: <https://github.com/murali1996/CodemixedNLP>

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Grounding ‘Grounding’ in NLP
Khyathi Raghavi Chandu | Yonatan Bisk | Alan W Black
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Switch Point biased Self-Training: Re-purposing Pretrained Models for Code-Switching
Parul Chopra | Sai Krishna Rallabandi | Alan W Black | Khyathi Raghavi Chandu
Findings of the Association for Computational Linguistics: EMNLP 2021

Code-switching (CS), a ubiquitous phenomenon due to the ease of communication it offers in multilingual communities still remains an understudied problem in language processing. The primary reasons behind this are: (1) minimal efforts in leveraging large pretrained multilingual models, and (2) the lack of annotated data. The distinguishing case of low performance of multilingual models in CS is the intra-sentence mixing of languages leading to switch points. We first benchmark two sequence labeling tasks – POS and NER on 4 different language pairs with a suite of pretrained models to identify the problems and select the best performing char-BERT model among them (addressing (1)). We then propose a self training method to repurpose the existing pretrained models using a switch-point bias by leveraging unannotated data (addressing (2)). We finally demonstrate that our approach performs well on both tasks by reducing the gap between the switch point performance while retaining the overall performance on two distinct language pairs in both the tasks. We plan to release our models and the code for all our experiments.

2020

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Proceedings of the The Fourth Widening Natural Language Processing Workshop
Rossana Cunha | Samira Shaikh | Erika Varis | Ryan Georgi | Alicia Tsai | Antonios Anastasopoulos | Khyathi Raghavi Chandu
Proceedings of the The Fourth Widening Natural Language Processing Workshop

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Reading Between the Lines: Exploring Infilling in Visual Narratives
Khyathi Raghavi Chandu | Ruo-Ping Dong | Alan W Black
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Generating long form narratives such as stories and procedures from multiple modalities has been a long standing dream for artificial intelligence. In this regard, there is often crucial subtext that is derived from the surrounding contexts. The general seq2seq training methods render the models shorthanded while attempting to bridge the gap between these neighbouring contexts. In this paper, we tackle this problem by using infilling techniques involving prediction of missing steps in a narrative while generating textual descriptions from a sequence of images. We also present a new large scale visual procedure telling (ViPT) dataset with a total of 46,200 procedures and around 340k pairwise images and textual descriptions that is rich in such contextual dependencies. Generating steps using infilling technique demonstrates the effectiveness in visual procedures with more coherent texts. We conclusively show a METEOR score of 27.51 on procedures which is higher than the state-of-the-art on visual storytelling. We also demonstrate the effects of interposing new text with missing images during inference. The code and the dataset will be publicly available at https://visual-narratives.github.io/Visual-Narratives/.