K now PAML :A Knowledge Enhanced Framework for Adaptable Personalized Dialogue Generation Using Meta-Learning (2022)

Abstract: In order to provide personalized interactions in a conversational system, responses must be consistent with the user and agent persona while still being relevant to the context of the conversation. Existing personalized conversational systems increase the consistency of the generated response by leveraging persona descriptions, which sometimes tend to generate irrelevant responses to the context. To solve this problems, we propose to extend the persona-agnostic meta-learning (PAML) framework by adding knowledge from ConceptNet knowledge graph with multi-hop attention mechanism. Knowledge is a concept in a triple form that helps in conversational flow. The multi-hop attention mechanism helps select the most appropriate triples with respect to the conversational context and persona description, as not all triples are beneficial for generating responses. The Meta-Learning (PAML) framework allows quick adaptation to different personas by utilizing only a few dialogue samples from the same user. Our experiments on the Persona-Chat dataset show that our method outperforms in terms of persona-adaptability, resulting in more persona-consistent responses, as evidenced by the entailment (Entl) score in the automatic evaluation and the consistency (Con) score in human evaluation.

## introduction recent advancements in personalized dialogue generation techniques that incorporate the personality of the speakers have enabled more human-like, natural, and persona-consistent responses. however, most methods require persona information in the form of style, persona profile, or persona statements, such as "i love meeting new people" and "autumn is my favorite season" which can be very the persona agnostic meta-learning model (paml) @xcite was developed to deal with these practical problems. this model, trained using meta-learning, would be able to adapt rapidly to new and unseen personas using only a few samples. the popular model-agnostic meta-learning (maml) @xcite framework served as the foundation for the paml framework. customized model agnostic meta-learning (cmaml) @xcite framework and generating personalized dialogue via multi-task meta-learning @xcite both largely follow the paml framework except for an extra network structure optimization component and persona reconstruction component respectively. the per-sonachat corpus @xcite , a popular personalized dialogue generating corpus that includes persona statements describing each speaker in addition to persona-specific dialogues, was used to benchmark all these frameworks. however, a comprehensive evaluation of these frameworks reveals that they continue to struggle to respond to situations and do not precisely match the persona statements. for example, in figure 1 , speaker1 asks a question about the nature of work that speaker2 is doing based on the persona statement: i enjoy coding such as web design. the responses generated by the state of the art frameworks are insufficiently precise. sota1, which is trained using dialogue history and persona statements and tested as a paml framework, fails to accurately understand the persona. sota2, similar to sota1, is only trained on dialogue history and hence fails to generate the correct response. sota3 is a paml framework that uses a meta-learning mechanism that wrongly understands the persona. neither approach appears to be capable of accurately comprehending the persona. our model's goal is to appropriately interpret the context and persona statement while generating an engaging response that is both context and persona consistent. real-life conversations, in general, begin with one topic and transition to other based on the personalities of the speakers. basic commonplace knowledge also tends to spark conversations. previously, external knowledge was used as a foundation for research, like "an open-domain knowledge graph" @xcite , "a commonsense knowledge base" @xcite , or "background documents" @xcite . such external knowledge is incorporated into this research by enhancing the entity representations in dialogues with it and then generating responses based on it. this enhanced encoding of information in the model improves the quality of the generated responses. also, commonsense knowledgegrounded @xcite , knowledge expansion @xcite , approaches were researched earlier, which also demonstrates an improvement in response generation. in this work, we enhance the entity representation in persona-profile by using conceptnet @xcite triples. we obtain triple representation using a graph encoding technique and incorporate these into the paml framework @xcite using attention mechanism with the attention mechanism @xcite . we then perform experiments on the persona-chat dataset @xcite to test the effect of knowledge on persona adaptability. the paml framework focuses on learning the different personas as independent tasks using the meta-learning approach, as opposed to building the model to represent all of the personas. the model is intended to be a fewshot learner using the persona-chat where the train-test split contains a non-overlapping personatype. the meta-learning training steps ensure that the model can swiftly adjust to a new unseen persona profile as well as the reaction style of a certain persona by utilizing only a few dialogues for training. incorporating commonsense knowledge helps in a deeper understanding of the persona and dialogue context, therefore helping in adaptability. the response generated by our model (c.f. figure 1 ) is pretty much accurate and understands the persona statement correctly. here, we extracted concepts from persona statement and dialogue context. for example figure 1 , "work" which is extracted from dialogue context is related to "freelance". similarly, "web" (extracted from persona statement), "design" and "work" all three are related to "freelance" in some way, which is utilised to generate the response. our experiment results show that our approach is effective in both automatic and human evaluation when compared to baseline models. personalized dialogue generation have caught the interest of many in recent years, following @xcite 's study of the task with the persona-chat dataset. recent research focuses on advancing the dialogue generation by grounding persona information @xcite @xcite or bringing external knowledge such as the knowledge graphs @xcite or supplementary texts @xcite into the model. this research demonstrates that by doing so, the model becomes more informative and consistent with the personas of the speakers, hence improving generation performance. furthermore, if the knowledge graph is properly formed, or if it is domain-specific @xcite , or if the knowledge base is large enough @xcite , the rich semantics representation is included through entities and relations @xcite . based on this, majumder et al. (2020) grounded the expanded persona statements using a commonsense knowledge graph, which aids in controlling the flow of the conversation. and in addition, reinforcement-learningbased framework was also proposed by @xcite and @xcite for making dialogue generation more informal. however, all of this work was based on either directly conditioning the response with the persona or by incorporating some commonsense knowledge into the model. none of the works attempted to generate a response from the persona except @xcite and with the adaption of the knowledge graph. ## methodology our method extends the paml framework @xcite by utilizing commonsense knowledge to generate personalized dialogues. the paml is adapted from the maml, which is capable of quickly adjusting to new, unknown tasks that were not employed during training. we continue to use the persona-chat dataset @xcite , which was used in the paml framework. the dialogue in persona-chat includes the utterances u 1:m and persona statements p 1:n . in previous research, response @xmath0:n and previous utterances @xmath1:m-1 following equation 1: knowpaml require: p train m , p valid m require: hyperparameters η inner , η outer require: iteration, patience, count randomly initialize parameter w while @xmath2 in paml, they first adapt w from the set of dialogue created by a persona and then respond conditioned only on the dialogue history rather than conditioning on both the dialogue history and persona sentences. so in this case eq.( 1 ) becomes: ## experiments our experiments are described in this section, including the dataset, implementation details, baselines, and evaluation metrics. ## result and discussion the results of the automatic and human evaluation for the 5-shot and 10-shot settings are shown in in the case of aknowpaml, the entl score is similar to the paml score, while the bleu score outperforms by 41.4% in 10-shots and 48.3% in 5-shots. fluency and coherence are equivalent in pmal, aknowpaml, and knowpaml in terms of human evaluation. the entl score versus k-shot results of knowpaml, aknowpaml, and paml frameworks are shown in figure 2 , where k is 1, 3, 5, 7, and 10. shots refer to the number of dialogues, such as 1-shot means one dialogue, 3shot means three dialogue, that is used to fine-tune. figure 2 shows that knowpaml behaves more linearly compare to paml and aknowpaml. entl score of knowpaml intersects with paml's score at 5-shot and then it keeps improving over paml. ## conclusion in this paper, we proposed the knowpaml framework, which uses a multi-hop attention mechanism to absorb concepts in the form of triples from the conceptnet knowledge graph in a metalearning setting. our persona-chat experiment demonstrates the advantage of using know-paml over previous frameworks in terms of persona-adaptability, resulting in more personaconsistent responses. the analysis of the generated responses reveals that the knowledge added model can successfully aid in persona adaptability, consistent response generation, and conversation flow. although fluency and coherence are comparable to those of others, they can be improved further in the future by using a pre-trained language model.