How Effective is Incongruity? Implications for Code-mix Sarcasm Detection.

This repo contains codes for the following paper:

How Effective is Incongruity? Implications for Code-mix Sarcasm Detection.
Aditya Shah, Chandresh Kumar Maurya, In Proceedings of the 18th International Conference on Natural Language Processing - (ACL 2021).

The presentation slides are available here

Requirements

Python 3.6 or higher
Pytorch >= 1.3.0
Pytorch_transformers (also known as transformers)
Pandas, Numpy, Pickle
Fasttext

Download the fasttext embed file:

The fasttext embedding file can be obtained here

Dataset

We release the benchmark sarcasm dataset for Hinglish language to facilitate further research on code-mix NLP.

We create a dataset using TweetScraper built on top of scrapy to extract code-mix hindi-english tweets. We pass search tags like #sarcasm, #humor, #bollywood, #cricket, etc., combined with most commonly used code-mix Hindi words as query. All the tweets with hashtags like #sarcasm, #sarcastic, #irony, #humor etc. are treated as positive. Non sarcastic tweets are extracted using general hashtags like #politics, #food, #movie, etc. The balanced dataset comprises of 166K tweets.

Finally, we preprocess and clean the data by removing urls, hashtags, mentions, and punctuation in the data. The respective files can be found here as train.csv, val.csv, and test.csv

Arguments:

--epochs:  number of total epochs to run, default=10

--batch-size: train batchsize, default=2

--lr: learning rate for the model, default=5.16e-05

--hidden_size_lstm: hidden size of lstm, default=1024

--hidden_size_linear: hidden size of linear layer, default=128

--seq_len: sequence lenght of input text, default=56

--clip: gradient clipping, default=0.218

--dropout: dropout value, default=0.198

--num_layers: number of lstm layers, default=1

--lstm_bidirectional: bidirectional lstm, default=False

--fasttext_embed_file: path to fasttext embedding file, default='new_hing_emb'

--train_dir: path to train file, default='train.csv'

--valid_dir: path to validation file, default='valid.csv'

--test_dir: path to test file, default='test.csv'

--checkpoint_dir: path to the saved, default='selfnet.pt'

--test: testing the model, default=False

Train

python main.py

Test

python main.py --test True