Developing Conversational Data and Detection of Conversational Humor in T elugu (2021)

Abstract: In the field of humor research, there has been a recent surge of interest in the sub-domain of Conversational Humor (CH). This study has two main objectives. (a) develop a conversational (humorous and non-humorous) dataset in Telugu. (b) detect CH in the compiled dataset. In this paper, the challenges faced while collecting the data and experiments carried out are elucidated. Transfer learning and non-transfer learning techniques are implemented by utilizing pre-trained models such as FastText word embeddings, BERT language models and Text GCN, which learns the word and document embeddings simultaneously of the corpus given. State-of-the-art results are observed with a 99.3% accuracy and a 98.5% f1 score achieved by BERT.

## introduction humor as a phenomenon has interested scholars from diverse fields since time immemorial @xcite . the abundance of research studies dedicated to humor is not only due to the fascinating nature of the domain but also due to its impact in everyday life @xcite . in a conversational discourse between interlocutors, their attitude, present state of mind, psychological distance between the topic of humor and the individual @xcite determine whether what is intended to be humorous is perceived so. conversational humor (ch) is a subset of verbal humor. verbal humor exists in the verbality of what is being spoken. canned jokes (such as light-bulb jokes, knock-knock jokes) are a part of verbal humor, where they are not contextdependent, i.e., they can be removed from a conversation and still perceived as humorous. on the other hand, ch is heavily dependent on various factors including speakers' personalities, the relevant culture that is referenced, and current events. numerous studies that focus on the detection of humor in short jokes/tweets rely on the contrastive dis-though extensive attempts to obtain conversational data from movies or tv shows were made, due to reasons such as unavailability of transcripts, manual transcription needed, and unavailability of multilingual ocrs 3 , this direction proved to be unfeasible. despite jokes being used, in the final dataset several conversational features are intact. all instances of the final humorous data used are of the same format as speaker 1: utterance 1 speaker 2: utterance 2 . . . speaker n: utterance n. therefore, features such as turn taking and sequence organization are present. turn-taking organization is where participants alternate their utterances, minimizing the noise arising from clashing of utterances to have a smooth or effective communication @xcite . sequence organization is the organization of these turns. if the conversational goal is to seek information, the first turn is the question, and the second turn is the answer @xcite . ## results and discussion after collecting and pre-processing the tweets and their replies to form non-humorous conversations, experiments were run with text gcn, fasttext and various bert models (refer to