| Challenge: | Existing systems that can infer persona from dialogue can be used for computational narrative analysis and personalized dialogue generation. |
| Approach: | They propose neural models to learn persona embeddings in a character trope classification task using IMDB dialogue snippets. |
| Outcome: | The proposed methods could be applied to other domains, including personalized dialogue generation. |
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DAPPER: Learning Domain-Adapted Persona Representation Using Pretrained BERT and External Memory (2020.aacl-main)
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| Challenge: | Empirical evidence suggests that the learnt persona embeddings can be effective in downstream tasks like hate speech detection. |
| Approach: | They propose a model that embeds personas from natural language into text . they evaluate the transferability of the model by simulating low-resource scenarios . |
| Outcome: | The proposed model can learn to embed persona from natural language and alleviate task or domain-specific data sparsity issues related to personas. |
We Are What We Repeatedly Do: Inducing and Deploying Habitual Schemas in Persona-Based Responses (2023.emnlp-main)
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| Challenge: | a variety of personas can be elicited from large language models, but they are opaque and unpredictable. |
| Approach: | They propose an approach to dialogue generation that retrieves relevant schemas to condition a large language model to generate persona-based responses. |
| Outcome: | The proposed method captures habitual knowledge and generates persona-based responses from a large language model. |
A Corpus of Controlled Opinionated and Knowledgeable Movie Discussions for Training Neural Conversation Models (2020.lrec-1)
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| Challenge: | Fully data driven Chatbots suffer from inconsistent behaviour across their turns due to a general difficulty in controlling parameters like their assumed background personality and knowledge of facts. |
| Approach: | They propose a model that is based on pre-specified facts and opinions and validates the dialogues for adherence to their given fact and opinion profile. |
| Outcome: | The proposed model is able to generate opinionated responses that are judged to be natural and knowledgeable and show attentiveness. |
“In-Dialogues We Learn”: Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning (2024.emnlp-main)
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| Challenge: | Existing approaches to personalized dialogue generate pre-defined profiles that are time-consuming and labor-intensive to create. |
| Approach: | They propose a framework that leverages dialogue history to characterize personas without pre-defined profiles. |
| Outcome: | The proposed framework improves BLEU and ROUGE scores on three datasets and human evaluations further validate the proposed method. |
A Dynamic Speaker Model for Conversational Interactions (N19-1)
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| Challenge: | a neural model for characterizing individual differences in speakers is shown to be useful in human-computer interaction and dialog act prediction. |
| Approach: | They propose a neural model for learning a dynamically updated speaker embedding in a conversational context. |
| Outcome: | The proposed model is used for content ranking and dialog act prediction in human-human conversations. |
BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data (2021.acl-long)
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| Challenge: | Existing persona-based dialogue models use crowd-sourced data, such as the PersonaChat . however, the cost of such datasets is limited, and the model is not robust. |
| Approach: | They propose to disentangle persona-based dialogue generation into two sub-tasks by using a BERT-over-BERT model. |
| Outcome: | The proposed model outperforms baselines in response quality and persona consistency under different limited data settings. |
A Model-agnostic Data Manipulation Method for Persona-based Dialogue Generation (2022.acl-long)
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| Challenge: | Existing models for introducing explicit personas are expensive due to their expensive collection costs. |
| Approach: | They propose a data manipulation method which is model-agnostic to be packed with any persona-based dialogue generation model to improve their performance. |
| Outcome: | The proposed method is model-agnostic to be packed with any persona-based dialogue generation model to improve their performance. |
Towards Zero-Shot Persona Dialogue Generation with In-Context Learning (2023.findings-acl)
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| Challenge: | Existing methods to improve persona consistency on high-quality human-labeled persona datasets face high cost and poor scalability. |
| Approach: | They propose a method to improve zero-shot persona consistency via in-context learning by pre-training a persona-augmented dialogue generation model and then using in-constant prompting mechanism to realize zero- shot persona customization. |
| Outcome: | The proposed method improves persona consistency without compromising coherence and informativeness in zero-shot settings. |
Learning to Predict Persona Information for Dialogue Personalization without Explicit Persona Description (2023.findings-acl)
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| Challenge: | Existing approaches to personalize dialogue agents rely on explicit persona descriptions during inference, which severely limits their application in real-world scenarios. |
| Approach: | They propose a method that learns to predict persona information based on the dialogue history to personalize dialogue agents without relying on explicit persona descriptions during inference. |
| Outcome: | The proposed method improves the consistency and engagingness of generated responses when conditioning on the predicted profile of the dialogue agent. |
Towards Exploiting Background Knowledge for Building Conversation Systems (D18-1)
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| Challenge: | Existing dialog datasets contain a sequence of utterances without any explicit background knowledge associated with them. |
| Approach: | They propose to use movie chats to generate responses by copying unstructured background knowledge . they use a dataset of 9K conversations to test whether responses are generated by copy-and-modify models . |
| Outcome: | The proposed model mimics human process of conversing by copying and/or modifying sentences from unstructured background knowledge. |