Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context (2020)

Abstract: We present a novel data augmentation technique, CRA (Contextual Response Augmentation), which utilizes conversational context to generate meaningful samples for training. We also mitigate the issues regarding unbalanced context lengths by changing the input output format of the model such that it can deal with varying context lengths effectively. Specifically, our proposed model, trained with the proposed data augmentation technique, participated in the sarcasm detection task of FigLang2020, have won and achieves the best performance in both Reddit and Twitter datasets.

## introduction the performance of many nlp systems largely depends on their ability to understand figurative languages such as irony, sarcasm, and metaphor @xcite . the results from the sentiment analysis task held in @xcite , for example, show that apparent performance drops occur when the figurative language is involved in the task. this work aims, in particular, to design a model that identifies sarcasm in the conversational context. more specifically, the goal is to determine whether a response is sarcastic or not, given the immediate context (i.e. only the previous dialogue turn) and/or the full dialogue thread (if available). for evaluation of our model, we participated in the figlang2020 sarcasm challenge foot_0 , and have won the competition as our model is ranked 1 out of 35 teams for the twitter dataset and 1 out of 34 teams for the reddit dataset. we summarize our technical contributions to win the challenge as follows: our technique, called cra(contextual response augmentation), utilizes the conversational context of the unlabeled dataset to generate new training samples. to cope with such imbalance, we propose a context ensemble method that exploits multiple context lengths to train the model. the proposed format is easily applicable to any transformer @xcite encoders without changing any model architecture. ## approach the task of our interest is, given response (r 1 ) and its previous conversational context (c 1 , c 2 , • • • , c n ), to predict whether the response r 1 is sarcastic or not (see an example in figure 2 ). we below discuss our model (section 2. ## experiments we first report the quantitative results by referring to the statistics in the official evaluation server foot_2 of the figlang2020 sarcasm challenge as of the challenge deadline (i.e. april 16, 2020, 11:59 p.m. utc). ## conclusion we proposed a new data augmentation technique, cra (contextual response augmentation), that utilizes the conversational context of the unlabeled data to generate meaningful training samples. we demonstrated that the method boosts the perfor-mance of sarcasm detection significantly. the employment of both augmentations with labeled and unlabeled data enables the system to achieve the best f1 scores to win the figlang2020 sarcasm challenge on both datasets of twitter and reddit.