“ I Know Who You Are”: Character-Based Features for Conversational Humor Recognition in C hinese (2022)

Abstract: Humor plays an important role in our daily life, as it is an essential and fascinating element in the communication between persons. Therefore, how to recognize punchlines from the dialogue, i.e. conversational humor recognition, has attracted much interest of computational linguistics communities. However, most existing work attempted to understand the conversational humor by analyzing the contextual information of the dialogue, but neglected the character of the interlocutor, such as age, gender, occupation, and so on. For instance, the same utterance could bring out humorous from a serious person, but may be a plain expression from a naive person. To this end, this paper proposes a Character Fusion Conversational Humor Recognition model (CFCHR) to explore character information to recognize conversational humor. CFCHR utilizes a multi-task learning framework that unifies two highly pertinent tasks, i.e., character extraction and punchline identification. Based on deep neural networks, we trained both tasks jointly by sharing weight to extract the common and task-invariant features while each task could still learn its task-specific features. Experiments were conducted on Chinese sitcoms corpus, which consisted of 12,677 utterances from 22 characters. The experimental results demonstrated that CFCHR could achieve 33.08% improvements in terms of F1-score over some strong baselines, and proved the effectiveness of the character information to identify the punchlines.

## introduction humor recognition is an important task of humor computation, which can not only enable machines to recognize humor, but also lay an important foundation for humor generation. according to the form of humorous text, humor recognition can be one-liners humor i failed math so many times at school, i can't even count. conversational humor character utterance 志国(zhiguo) middle age, male 唉，你们不能剩我一人儿啊。 oh, you can't leave me alone. 圆圆(yuanyuan) child, female 不剩您一人，还有小芳大妈 you are not alone. aunt xiaofang is with you. 志国(zhiguo) 那还不如剩我一人儿呢 i'd rather be left alone. 傅明(fuming) old man, male 都是你自己惹的麻烦， 不剩你剩谁 it's all your own troubles, who else is left without you? different from one-liners humor, the character is one of the most important influencing factors in conversational humor according to the theory of general verbal theory of @xcite . note that the character refers to the sitcom role in this paper. however, existing studies for conversational humor recognition mainly considered it as a punchline recognition task. most methods did not distinguish conversational humor from one-liners humor, and focused on the context representation to capture the humorous semantics. to the contrary, character information in the dialogue was neglected, such as, speaking style, gender, age, etc., and these information has some specific features, that are inherently funnier, and more likely to make people laugh. as a result, existing methods performed sub-optimal in conversational humor recognition. to solve the above issues, this paper attempts to explore character information to facilitate the machines to understand humor in the dialogue. a character fusion conversational humor recognition (cfchr) model is presented by integrating the character information and the contextual information to represent the conversational humorous information. to capture the contextual information, an utterance embedding is derived from each utterance; to capture the character information, character features and the attributes are learned from utterances and predefined attributes respectively. then, the utterance embedding and the character embedding are fed into the a multi-task learning framework @xcite . in this way, cfchr can capture both of the contextual information and the character information for character extraction and punchline recognition. we conducted experiments on chinese sitcoms corpus, which consisted of 12,677 utterances from 22 characters. cfchr was effective in character extraction task. compared with some strong baselines, cfchr could achieve the best performance of punchline recognition, i.e. 51.5%, in terms of f1-score. moreover, 33.08% improvements were achieved in terms of f1-score over the baseline model, and it proved the effectiveness of the character information in identifying the punchlines. ## related work humor recognition is usually formulated as a binary classification problem. most of the existing work on humor recognition mainly focus on oneliners, and a classifier is usually trained based on the whole texts to predict whether it is humorous or not @xcite @xcite @xcite . in recent years, much research work has a growing interest in conversational humor recognition. early work on conversational humor recognition attempted to use lstm, rnn, gru models @xcite to predict punchlines in dialogues. due to the lack of corpus for conversational humor recognition, there were some studies @xcite @xcite later focused on constructing humorous dialogue corpus based on sitcoms. however, none of the above work accounted for the character information, which was a crucial source of information for conversational humor recognition. this paper will explore the character information and integrate multi-faceted information of character into the humorous semantics representation. ## task formulation given a dialogue as the input text, and it is consisted of a sequence of m utterances. for an utterance s i , it is consisted of n words, and denoted by s @xmath0,1 , w i,2 , . . . , w i,j , . . . , w i,n }, where w i,j is the j th word in the utterance s i . note that each utterance in the dialogue corresponds to an interlocutor with the character features denoted as role v and the character attributes denoted as role d . we detail both of these in section 4.1. the objective of this paper is to identify if an utterance is a punchline by exploring the character information. ## cfchr in this paper, we propose character fusion conversational humor recognition (cfchr) for punchline recognition from the dialogue. cfchr consists of three sub-components: character extraction, punchline recognition, and multi-task learning framework. character extraction module is firstly designed to obtain the character information representation; punchline recognition module is then followed to obtain contextual information representation; and a multi-task learning framework is finally employed for character extraction and punchline identification. given a dialogue m , input each utterance s i and its corresponding character information (role v , role d ) into the model. for each utterance, we firstly derive an utterance embedding by the contextual sentence to capture the semantics in the text. to obtain the character features role v , cfchr will classify the interlocutor into the predefined category of the character based on each utterance. the character information role k can be represented by the character features role v together with the character attributes role d . then, both of the utterance embedding and the character embedding are fed into the a multi-task learning framework @xcite . in this way, cfchr can capture both of the contextual semantics and the character information for character extraction and punchline recognition. ## conclusions in this paper, we explore character information for conversational humor recognition, and present a multi-task learning framework for character extraction and punchline recognition. experimental results demonstrated that character information was effective for punchline identification and could achieve the performance of 51.5% in terms of f1score. compared with some strong baselines, our proposed model could achieve 33.08% improvement on chinese sitcom dataset.