Training Millions of Personalized Dialogue Agents

By Pierre-Emmanuel Mazaré, Samuel Humeau, Martin Raison, Antoine Bordes. 2018

295 citations recorded by the source corpus.

Current dialogue systems fail at being engaging for users, especially when trained end-to-end without relying on proactive reengaging scripted strategies. Zhang et al. (2018) showed that the engagement level of end-to-end dialogue models increases when conditioning them on text personas providing some personalized back-story to the model. However, the dataset used in Zhang et al. (2018) is synthetic and only contains around 1k different personas. In this paper we introduce a new dataset providing 5 million personas and 700 million persona-based dialogues. Our experiments show that, at this scale, training using personas still improves the performance of end-to-end systems. In addition, we show that other tasks benefit from the wide coverage of our dataset by fine-tuning our model on the data from Zhang et al. (2018) and achieving state-of-the-art results.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

Citations in the corpus

Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions (2020, methodology): “, 2020), we believe it is better for a bot to be able to adapt a multitude of personalities (Zhang et al., 2018; Mazaré et al., 2018).”
Wizard of Wikipedia: Knowledge-Powered Conversational agents (2018, methodology): “Where noted, the Transformer is pretrained on Reddit data (Mazaré et al., 2018), and fine-tuned for our task.”
Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring (2019, methodology): “, 2019; Vig & Ramea, 2019), which perform full (cross) self-attention over a given input and label candidate, tend to attain much higher accuracies than their counterparts, Bi-encoders (Mazaré et al., 2018; Dinan et al., 2019b), which perform self-attention over the input and candidate label separat”
Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion (2022, methodology): “Pushshift.io Reddit We use a variant of Reddit discussions, which has also been used in several existing studies (see e.g. Yang et al. (2018); Mazaré et al. (2018); Shuster et al. (2020)).”
Learning from Dialogue after Deployment: Feed Yourself, Chatbot! (2019, methodology): “The DIALOGUE and FEEDBACK tasks are set up as ranking problems, as in (Zhang et al., 2018b; Mazaré et al., 2018), where the model ranks a collection of candidate responses and returns the top-ranked one as its response.”
Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation (2019, background): “Recent work in dialogue incorporates personas, or personality descriptions that ground speaker’s chat, such as I love fishing (Zhang et al., 2018; Shuster et al., 2018; Mazaré et al., 2018; Olabiyi et al., 2018; Li et al., 2016).”
BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage (2022, methodology): “In addition it uses Pushshift.io Reddit, a variant of Reddit discussions, which has also been used in several existing studies (see e.g., Yang et al. (2018); Mazaré et al. (2018); Shuster et al. (2020)).”
Survey of Hallucination in Natural Language Generation (2022, background): “The PersonaChat datasets [31, 228] have accelerated research into persona consistency [68, 91, 126, 208, 219, 224, 232].”
Recipes for Building an Open-Domain Chatbot (2020, methodology): “pushshift.io Reddit We use a variant of Reddit discussions, which has also been used in several existing studies, see e.g. Yang et al. (2018); Mazaré et al. (2018); Keskar et al. (2019); Shuster et al. (2019).”
Can You Put it All Together: Evaluating Conversational Agents’ Ability to Blend Skills (2020, methodology): “Recent promising conversational models have leveraged very large conversation-like data such as datasets extracted from Reddit and made available by a third party on pushshift.io (Mazar´e et al., 2018; Humeau et al., 2019; Keskar et al., 2019; Rashkin et al., 2019).”
Beyond Goldfish Memory: Long-Term Open-Domain Conversation (2021, methodology): “In Mazaré et al. (2018) such personas were extracted from Reddit and used to train agents.”
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents (2019, methodology): “…the collection, and we thus measure in experiments its ability to help performance on other tasks. pushshift.io Reddit We use a variant of Reddit discussions (text-only), which has also been used in several existing studies, see e.g. Yang et al. (2018); Mazar´e et al. (2018); Keskar et al. (2019).”
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems (2019, background): “Even work aimed at maintaining a persona does not test in an interactive setting [7, 8].”
Importance of Search and Evaluation Strategies in Neural Dialogue Modeling (2018, background): “A number of variants of sequence-to-sequence models (Sutskever et al., 2014; Cho et al., 2014; Kalchbrenner and Blunsom, 2013) have been proposed for dialogue modeling in recent years, including hierarchical models (Serban et al., 2016) and transformers (Mazaré et al., 2018; Yang et al., 2018).”
Hash Layers For Large Sparse Models (2021, methodology): “Pushshift.io Reddit We use a variant of Reddit discussions, which has also been used in several existing studies, see e.g. [26, 27, 28, 29].”
Challenges in Building Intelligent Open-domain Dialog Systems (2019, methodology): “corpus is constructed, where each dialogue session involves two speakers and the persona of each speaker is defined by several sentences describing the speaker’s hobbies or preferences. Mazaré et al. [53] presented a simple method of constructing a large-scale personalized dataset from social media ”