A Model-agnostic Data Manipulation Method for Persona-based Dialogue Generation (2022)

Abstract: Towards building intelligent dialogue agents, there has been a growing interest in introducing explicit personas in generation models. However, with limited persona-based dialogue data at hand, it may be difficult to train a dialogue generation model well. We point out that the data challenges of this generation task lie in two aspects: first, it is expensive to scale up current persona-based dialogue datasets; second, each data sample in this task is more complex to learn with than conventional dialogue data. To alleviate the above data issues, we propose a data manipulation method, which is model-agnostic to be packed with any persona-based dialogue generation model to improve their performance. The original training samples will first be distilled and thus expected to be fitted more easily. Next, we show various effective ways that can diversify such easier distilled data. A given base model will then be trained via the constructed data curricula, i.e. first on augmented distilled samples and then on original ones. Experiments illustrate the superiority of our method with two strong base dialogue models (Transformer encoder-decoder and GPT2).

## introduction the ability to generate responses with consistent personas is important towards building intelligent dialogue agents. in past years, there has been a growing interest in introducing explicit personas in dialogue generation models @xcite . a piece of persona text generally consists of profiles and background personal facts. a clipped persona-based dialogue from the personachat @xcite dataset is shown in figure 1 , which covers rich persona features. for * work was done when yu cao was an intern at tencent ai lab. ## related work persona-based dialogue generation it sees growing interest in recent years, thanks to the released benchmark datasets such as personachat/ convai2 @xcite . previous works mostly focus on modifying dialogue models to condition auxiliary persona information, including extra persona embedding @xcite , profile memory @xcite , copying from personas @xcite , cvae with persona information @xcite , and using meta-learning to augment low-resource personas @xcite . recent works try to adopt large-scale pretrained models on this task. gpt/gpt2 @xcite are chosen the most often and shown to improve the generation quality with different finetuning strategies @xcite @xcite . some leverage bert @xcite as backbones (? @xcite . other pretrained models also demonstrate their effectiveness @xcite . the aforementioned methods often need proper network modifications and finetuning loss functions in order to get satisfactory performance. it is hard to transfer them to be useful across different pretrained models. moreover, most of them simply concatenate persona texts and dialogue history together as a single input sequence @xcite , highly depending on the ability of the pretrained model to fast adapt to the target data domain. text data manipulation various data augmentation methods have been widely used in many nlp tasks @xcite @xcite , which are also effective to boost the performance of dialogue models. new generated dialogue utterances @xcite and retrieval results @xcite can be used to augment the training data. however, all previous work only studies the pairwise relationship between a query and a response to design the augmentation techniques, which are not applicable to involving auxiliary information, such as personas, simultaneously. besides data augmentation, there are other ways to manipulate dialogue data to improve model learning. for example, a few approaches filter uninformative or noisy samples to enhance data quality @xcite . @xcite combine data augmentation and re-weighting to make models learn more effectively. @xcite utilize learnable memory based on dialogue clusters to enhance the model. @xcite examine the benefits of training models using various curricula successively from easy to hard. it has been applied to many nlp tasks such as machine translation @xcite , reading comprehension @xcite and language understanding @xcite . @xcite adopt the idea in open-domain dialogue generation, where curriculum plausibility is determined by the response properties, including coherence and diversity. our work is different in that we introduce new distilled data regarding as a curriculum. ## our data manipulation method we first formally define a persona-based training sample. it consists of l persona description sentences @xmath0, p 2 , .., p l }, m dialogue history utterances @xmath1, h 2 , .., h m }, and a gold response r. the given training dataset is denoted as @xmath2, h, r)}. note that l and m in different training samples can be different. a dialogue model needs to generate a response r, which is coherent with the dialogue history h and consistent with persona information in p . our proposed data manipulation method d 3 is model-agnostic. for any dialogue model, we will not change the model itself, but only manipulate its training data. we develop three data manipulation operations in sequel, former two for augmentation and the last one eases training, shown in figure 2: 1. data distillation. we construct simple personaconsistent data d @xmath3, h, r)} by removing redundant information in p and h; 2. data diversification. due to the limited amount of distilled samples, we design various methods to increase the data variety and scale, and obtain the diversified data d @xmath4, h, r)}; 3. data curriculum. we combine d dis and d div as the augmented dataset d a . a curriculum strategy is defined to train the model with the easier distilled samples in d a first and then the original ones in d. ## experiments to validate the effectiveness of our proposed modelagnostic data manipulation method, we first experiment on two strong persona-based dialogue generation models (transformer encoder-decoder and gpt2) on the benchmark personachat @xcite dataset. next we conduct a series of analysis to examine the usefulness of different data manipulation operations in our method. foot_0