tharunsuresh-code / hit-acl2021-codemixed-representation

This repo contains the source code of HIT: A Hierarchically Fused Deep Attention Network for RobustCode-mixed Language Representation (Accepted in ACL 2021)

Hierarchical Transformer (HIT)

This repository contains the source code for HIT (Hierarchical Transformer). It uses Fused Attention Mechanism (FAME) for learning representation learning from code-mixed texts. We evaluate HIT on code-mixed sequence classification, token classification and generative tasks.

We publish the datasets (publicly available) and the experimental setup used for different tasks.

Installation for experiments

$ pip install -r requirements.txt

Commands to run

Sentiment Analysis

$ cd experiments && python experiments_hindi_sentiment.py \
		--train_data ../data/hindi_sentiment/IIITH_Codemixed.txt \
		--model_save_path ../models/model_hindi_sentiment/

PoS (Parts-of-Speech) Tagging

$ cd experiments && python experiments_hindi_POS.py \
		--train_data '../data/POS Hindi English Code Mixed Tweets/POS Hindi English Code Mixed Tweets.tsv' \
		--model_save_path ../models/model_hindi_pos/

Named Entity Recognition (NER)

$ cd experiments && python experiments_hindi_NER.py\
		--train_data '../data/NER/NER Hindi English Code Mixed Tweets.tsv' \
		--model_save_path ../models/model_hindi_NER/

Machine Translation (MT)

$ cd experiments && python nmt.py \
		--data_path '../data/IITPatna-CodeMixedMT' \
		--model_save_path ../models/model_hindi_NMT/

Citation

If you find this repo useful, please cite our paper:

@inproceedings{,
  author    = {Ayan Sengupta and
               Sourabh Kumar Bhattacharjee and
               Tanmoy Chakraborty and
               Md. Shad Akhtar},
  title     = {HIT: A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation},
  booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
  publisher = {Association for Computational Linguistics},
  year      = {2021},
  url       = {},
  doi       = {},
}