Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances (2022)

Abstract: In this paper, we consider mimicking fictional characters as a promising direction for building engaging conversation models. To this end, we present a new practical task where only a few utterances of each fictional character are available to generate responses mimicking them. Furthermore, we propose a new method named Pseudo Dialog Prompting (PDP) that generates responses by leveraging the power of large-scale language models with prompts containing the target character’s utterances. To better reflect the style of the character, PDP builds the prompts in the form of dialog that includes the character’s utterances as dialog history. Since only utterances of the characters are available in the proposed task, PDP matches each utterance with an appropriate pseudo-context from a predefined set of context candidates using a retrieval model. Through human and automatic evaluation, we show that PDP generates responses that better reflect the style of fictional characters than baseline methods.

## introduction how would you feel if you could talk to your favorite character? in recent years, open-domain conversation models @xcite have achieved remarkable progress with the development of large-scale language models @xcite . meanwhile, recent studies have suggested several directions reflecting desirable traits of real-life conversation to make open-domain conversation models more engaging beyond plain chit-chat. style-controlling conversation models generate responses in the target styles such as emotion @xcite and empathy @xcite . personagrounded conversation models @xcite @xcite produce responses that preserve consistent personalities by leveraging personal descriptions (e.g., "i have two dogs"). in this paper, we consider mimicking fictional characters as a promising direction for building engaging conversation models. when it comes to building conversation models that mimic fictional characters, two major challenges prevent us from directly applying previous models designed for conditional response generation: (1) it is difficult to define fictional characters with only a few descriptions, as in personagrounded conversation models. furthermore, it is not expressive enough to represent characters' styles with discrete labels (e.g., angry, happy), as style-controlling conversation models do. (2) there lacks sufficient dialog data of fictional characters for training conversation models. it is inefficient to manually create dialog datasets of characters for training, especially considering that additional data is needed for each new character. to address these two challenges, we propose a new task where only a few utterances of the fictional characters are available to generate responses mimicking the characters. such setting is justified by the two following reasons: (1) utterances of fictional characters provide useful clues for gen-erating responses mimicking the characters as the personal traits or styles of speakers are inherent in their utterances @xcite . to perform the task, we introduce pseudo dialog prompting (pdp), a method that builds prompts using a few numbers of target characters' utterances to leverage the power of pre-trained language models. we claim that designing the prompt in the form of dialog that includes the character's utterances as dialog history (as in figure 1 ) is an effective method for reflecting the style of character. however, since only utterances of the characters are available in the proposed task, we match each utterance with an appropriate pseudo-context by using a retrieval model @xcite to select the relevant context from a predefined set of context candidates. through human and automatic evaluation, we show that pdp generates responses that better reflect the style of fictional characters than existing baseline models. ## method we model a conversation agent that generates a response r corresponding to a given context x while mimicking an arbitrary character with k utterances {u 1 , u 2 , • • • , u k } of the character. the simplest way to design the prompt with the character's utterances is to concatenate utterances as @xcite does for personachat @xcite . however, in our preliminary experiments, we observed that this method tends to generate dull responses that do not reflect the styles of the character (will be shown in section 4). we hypothesize that the language model fails to utilize the utterances because such a format of the prompt is unlikely to have appeared naturally in the training set @xcite . to address this issue, we propose pdp, which builds a dialog format prompt where character utterances are included in the dialog history, as depicted in figure 1 . since a speaker tends to maintain a consistent style throughout the conversation, using such a prompt will induce the language model to generate responses that seamlessly reflect the style from the character's utterances. to build a dialog when only given the utterances of the character, we require a pseudo-context c i matching each utterance u i to get a context-utterance pair (c i , u i ). we use a retriever r to acquire a pseudo-context c i . particularly, we employ bi-encoder @xcite as our retriever r. we first define a fixed set of single-turn context candidates c obtained from bst dataset @xcite , which is the largest open-domain conversation dataset released to date. we then select a candidate as the pseudo-context c i for the given utterance since language models quickly adapt to the context-response mapping of the given prompt via in-context learning, we believe providing pseudo-contexts that are semantically similar to the input context as in dynamic match facilitates the reflection of styles in corresponding utterances. we name this variant dynamic because the pseudo-context c i depends on the varying input context x. ## results discussion. gold match shows worse performance in style strength than pdp. we believe that the gold context-response pairs in the hla-chat are not always the most appropriate pairs for our experiments. since the hla-chat originated from scripts of tv shows, there might be some additional contexts outside of a single-turn dialogue (e.g., the background of characters, events that happened before the dialogue, audio-visual information, etc.). without understanding the context behind the scripts, even gold context-response pairs might seem irrelevant. therefore, directly using the context-response pairs in hla-chat as in gold match could adversely affect the quality of subsequent responses in style strength and coherency. pdp methods tend to have slightly lower response coherency scores compared to other baselines. our speculations for this phenomenon are as follows. pseudo-dialog pairs (c i , u i ) created by pdp methods might have some degree of incoherency, and it might incur adverse effects in coherency via in-context learning in the language model. the fact that the response coherency score of static match is higher compared to dynamic match, which finds a pseudo-context that is more similar to the input context, or random match, which finds a random pseudo context at all, supports this claim. additionally, automated metrics like maude are tuned to work with texts in standard dialog style. since responses that strongly reflect character styles (e.g., "yippie ki-yi-yay!" in figure 1 ) are out-of-domain examples when put next to standard texts, there might be an unavoidable decrease in maude scores. an interesting future work would be finding a method that does not reduce response coherency while also successfully reflecting the character styles. applicability of pdp to other language models. we further evaluate our method by leveraging different language models instead of base-lm to verify that our method generally works well on any language model. we use three pre-trained language models, gpt-j 6b (wang and komatsuzaki, 2021), gpt-neo 2.7b @xcite , and gpt2xl 1.5b @xcite , which are publicly available. similar to our main experiments, we conduct the automatic evaluation with these language models. the results are shown in ## conclusion in this paper, we introduce the task of mimicking a fictional character by using only a few utterances of the character. we propose a new method, pseudo dialog prompting, which builds a prompt for a language model to solve this task by creating a pseudo dialog using the given utterance set with a retrieval model. extensive experiments show that our method effectively generates responses that reflect the style of a given character better than baseline models and even advantaged models.