### Open-Domain Dialogues Below is a general format for open domain dialogues: ```js { "dataset_name--train/val/test--dialog_id": { "original dialog id": str, "dialog index": int, "original dialog info": dict, "log": [ { "turn id": int, "user utterance": str, "system response": str, "dialog history": str, "original user side information": dict, "original system side information": dict, }, ... ], "prompt": [ "This is a conversation between two speakers talking about history. Given the dialog context, please generate a relevant response.", ... ] }, ... } ``` Chitchat dialogues generally do not involve extra annotations. Therefore the "original user1/2 side information" are usually left blank. Unlike task-oriented dialogues, chitchat does not necessarily end on the user2 side (system side in task-oriented dialogues). So, there are some dialogues contain only user1 utterance in the last turn. For SODA, we design 6 prompts for each dialog and below shows the template: ```python { "Imagine you are {speaker_system} and you are talking to {speaker_user}. Generate a coherent and appropriate response.", "In the role of {speaker_system}, engage with {speaker_user}. Formulate a response that is both consistent with the conversation and suitable to the context.", "As {speaker_system}, you are in a dialogue with {speaker_user}. Create a coherent and relevant reply that fits the ongoing discussion.", "Assuming the persona of {speaker_system}, you're conversing with {speaker_user}. Generate a logical and suitable response that aligns with the conversation.", "Imagine yourself as {speaker_system} engaging with {speaker_user}. Your task is to produce a coherent and fitting response to continue the conversation.", "Pretend to be {speaker_system} in a conversation with {speaker_user}. Construct a response that maintains the coherence of the dialogue and is appropriate for the context." } ``` Where 'speaker_user' and 'speaker_system' represent the 'PersonX' name and the 'PersonY' name, respectively.