SyMCoM - Syntactic Measure of Code Mixing A Study Of English-Hindi Code-Mixing

no code implementations • Findings (ACL) 2022 • Prashant Kodali, Anmol Goel, Monojit Choudhury, Manish Shrivastava, Ponnurangam Kumaraguru

Code mixing is the linguistic phenomenon where bilingual speakers tend to switch between two or more languages in conversations.

POS

CoMeT: Towards Code-Mixed Translation Using Parallel Monolingual Sentences

1 code implementation • NAACL (CALCS) 2021 • Devansh Gautam, Prashant Kodali, Kshitij Gupta, Anmol Goel, Manish Shrivastava, Ponnurangam Kumaraguru

Code-mixed languages are very popular in multilingual societies around the world, yet the resources lag behind to enable robust systems on such languages.

Machine Translation Translation

From Human Judgements to Predictive Models: Unravelling Acceptability in Code-Mixed Sentences

no code implementations • 9 May 2024 • Prashant Kodali, Anmol Goel, Likhith Asapu, Vamshi Krishna Bonagiri, Anirudh Govil, Monojit Choudhury, Manish Shrivastava, Ponnurangam Kumaraguru

To this end, we construct Cline - a dataset containing human acceptability judgements for English-Hindi (en-hi) code-mixed text.

Socratic Reasoning Improves Positive Text Rewriting

no code implementations • 5 Mar 2024 • Anmol Goel, Nico Daheim, Iryna Gurevych

In this work, we address this gap by augmenting open-source datasets for positive text rewriting with synthetically-generated Socratic rationales using a novel framework called \textsc{SocraticReframe}.

Language Modeling Language Modelling +1

InSaAF: Incorporating Safety through Accuracy and Fairness | Are LLMs ready for the Indian Legal Domain?

no code implementations • 16 Feb 2024 • Yogesh Tripathi, Raghav Donakanti, Sahil Girhepuje, Ishan Kavathekar, Bhaskara Hanuma Vedula, Gokul S Krishnan, Shreya Goyal, Anmol Goel, Balaraman Ravindran, Ponnurangam Kumaraguru

Task performance and fairness scores of LLaMA and LLaMA--2 models indicate that the proposed $LSS_{\beta}$ metric can effectively determine the readiness of a model for safe usage in the legal sector.

Fairness

X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents

1 code implementation • 30 Jun 2023 • Mehrad Moradshahi, Tianhao Shen, Kalika Bali, Monojit Choudhury, Gaël de Chalendar, Anmol Goel, Sungkyun Kim, Prashant Kodali, Ponnurangam Kumaraguru, Nasredine Semmar, Sina J. Semnani, Jiwon Seo, Vivek Seshadri, Manish Shrivastava, Michael Sun, Aditya Yadavalli, Chaobin You, Deyi Xiong, Monica S. Lam

We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean; and a code-mixed English-Hindi language.

Entity Alignment Machine Translation +1

Are Models Trained on Indian Legal Data Fair?

no code implementations • 13 Mar 2023 • Sahil Girhepuje, Anmol Goel, Gokul S Krishnan, Shreya Goyal, Satyendra Pandey, Ponnurangam Kumaraguru, Balaraman Ravindran

We highlight the propagation of learnt algorithmic biases in the bail prediction task for models trained on Hindi legal documents.

Fairness

PreCogIIITH at HinglishEval : Leveraging Code-Mixing Metrics & Language Model Embeddings To Estimate Code-Mix Quality

1 code implementation • 16 Jun 2022 • Prashant Kodali, Tanmay Sachan, Akshay Goindani, Anmol Goel, Naman Ahuja, Manish Shrivastava, Ponnurangam Kumaraguru

Code-Mixing is a phenomenon of mixing two or more languages in a speech event and is prevalent in multilingual societies.

Data Augmentation Language Modeling +1

HLDC: Hindi Legal Documents Corpus

1 code implementation • Findings (ACL) 2022 • Arnav Kapoor, Mudit Dhawan, Anmol Goel, T. H. Arjun, Akshala Bhatnagar, Vibhu Agrawal, Amul Agrawal, Arnab Bhattacharya, Ponnurangam Kumaraguru, Ashutosh Modi

Further, as a use-case for the corpus, we introduce the task of bail prediction.

Multi-Task Learning

Battling Hateful Content in Indic Languages HASOC '21

1 code implementation • Forum for Information Retrieval Evaluation (FIRE) 2021 • Aditya Kadam, Anmol Goel, Jivitesh Jain, Jushaan Singh Kalra, Mallika Subramanian, Manvith Reddy, Prashant Kodali, T. H. Arjun, Manish Shrivastava, Ponnurangam Kumaraguru

We adopt a multilingual transformer based approach and describe our architecture for all 6 subtasks as part of the challenge.

Hate Speech Detection