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ORCID5th CALCS@NAACL 2021: Online
- Thamar Solorio, Shuguang Chen, Alan W. Black, Mona T. Diab, Sunayana Sitaram, Victor Soto, Emre Yilmaz, Anirudh Srinivasan:
Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching, CALCS@NAACL 2021, Online, June 11, 2021. Association for Computational Linguistics 2021, ISBN 978-1-954085-45-9 - Dama Sravani, Lalitha Kameswari, Radhika Mamidi:
Political Discourse Analysis: A Case Study of Code Mixing and Code Switching in Political Speeches. 1-5 - Vivek Srivastava, Mayank Singh:
Challenges and Limitations with the Metrics Measuring the Complexity of Code-Mixed Text. 6-14 - Devansh Gautam, Kshitij Gupta, Manish Shrivastava:
Translate and Classify: Improving Sequence Level Classification for English-Hindi Code-Mixed Data. 15-25 - Suman Dowlagar, Radhika Mamidi:
Gated Convolutional Sequence to Sequence Based Learning for English-Hingilsh Code-Switched Machine Translation. 26-30 - Ramakrishna Appicharla, Kamal Kumar Gupta, Asif Ekbal, Pushpak Bhattacharyya:
IITP-MT at CALCS2021: English to Hinglish Neural Machine Translation using Unsupervised Synthetic Code-Mixed Parallel Corpus. 31-35 - Ganesh Jawahar, El Moatez Billah Nagoudi, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan:
Exploring Text-to-Text Transformers for English to Hinglish Machine Translation with Synthetic Code-Mixing. 36-46 - Devansh Gautam, Prashant Kodali, Kshitij Gupta, Anmol Goel
, Manish Shrivastava, Ponnurangam Kumaraguru:
CoMeT: Towards Code-Mixed Translation Using Parallel Monolingual Sentences. 47-55 - El Moatez Billah Nagoudi, AbdelRahim A. Elmadany, Muhammad Abdul-Mageed:
Investigating Code-Mixed Modern Standard Arabic-Egyptian to English Machine Translation. 56-64 - Dana-Maria Iliescu, Rasmus Grand, Sara Qirko, Rob van der Goot:
Much Gracias: Semi-supervised Code-switch Detection for Spanish-English: How far can we get? 65-71 - Saziye Betül Özates, Özlem Çetinoglu:
A Language-aware Approach to Code-switched Morphological Tagging. 72-83 - Jitao Xu, François Yvon:
Can You Traducir This? Machine Translation for Code-Switched Input. 84-94 - Marvin M. Agüero-Torales, David Vilares, Antonio Gabriel López-Herrera:
On the logistical difficulties and findings of Jopara Sentiment Analysis. 95-102 - Akshat Gupta, Sargam Menghani, Sai Krishna Rallabandi, Alan W. Black:
Unsupervised Self-Training for Sentiment Analysis of Code-Switched Data. 103-112 - Sai Muralidhar Jayanthi, Kavya Nerella, Khyathi Raghavi Chandu, Alan W. Black:
CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing. 113-118 - Dwija Parikh, Thamar Solorio:
Normalization and Back-Transliteration for Code-Switched Data. 119-124 - Salim Sazzed:
Abusive content detection in transliterated Bengali-English social media corpus. 125-130 - Zara Maxwell-Smith, Ben Foley:
Developing ASR for Indonesian-English Bilingual Language Teaching. 131-132 - Chihiro Taguchi, Yusuke Sakai, Taro Watanabe:
Transliteration for Low-Resource Code-Switching Texts: Building an Automatic Cyrillic-to-Latin Converter for Tatar. 133-140 - Samson Tan, Shafiq R. Joty:
Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots. - Genta Indra Winata, Samuel Cahyawijaya, Zihan Liu, Zhaojiang Lin, Andrea Madotto, Pascale Fung:
Are Multilingual Models Effective in Code-Switching? 142-153
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