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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 also experiment with pre-training objectives such as Masked Language Modeling and Zero Shot Learning.

HIT

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/telegu_POS/FB_TE_EN_FN.txt' \
	--model_save_path ../models/model_telugu_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/

Sarcasm Detection

$ cd experiments && python experiments_hindi_SH.py \
	--train_data '../data/MSH-Comics-Sarcasm/hindi_sarcasm.txt' \
	--model_save_path ../models/model_hindi_sarcasm/

Humour Classification

$ cd experiments && python experiments_hindi_SH.py \
		--train_data '../data/MSH-Comics-Sarcasm/hindi_humour.txt' \
		--model_save_path ../models/model_hindi_humour/

Response Prediction

$ cd experiments && python experiments_response_prediction.py \
		--data_path '../data/IITMadras-CodeMixResponse/hindi' \
		--model_save_path ../models/model_hindi_response/

Intent Detection

$ cd experiments && python experiments_intent_detection.py \
	--train_data '../data/IITMadras-CodeMixIntent/GCN-SeA/data/hi-dstc2/' \
	--model_save_path ../models/model_hindi_intents/

Slot Filling

$ cd experiments && python experiments_slot_filling.py \
	--train_data '../data/IITMadras-CodeMixIntent/GCN-SeA/data/hi-dstc2/' \
	--model_save_path ../models/model_hindi_slots/

MLM pre-training

$ cd experiments && python experiments_hindi_MLM.py \
	--ismlm True \
	--model_save_path ../models/model_hindi_mlm/

ZSL pre-training

$ cd experiments && python experiments_hindi_ZSL.py \
		--model_save_path ../models/model_hindi_zsl/

Citation

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

@inproceedings{,
  author    = {Ayan Sengupta and
               Tharun Suresh and
               Tanmoy Chakraborty and
               Md. Shad Akhtar},
  title     = {A Comprehensive Understanding of Code-mixed Language Semantics using Hierarchical Transformer},
  booktitle = {},
  publisher = {},
  year      = {},
  url       = {},
  doi       = {},
}