Training Millions of Personalized Dialogue Agents

Mazaré, Pierre-Emmanuel; Humeau, Samuel; Raison, Martin; Bordes, Antoine

doi:10.48550/arxiv.1809.01984

Cited by 25 publications

(29 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To train the model, a cross entropy loss is used. Similar to Mazaré et al (2018), during training we consider the other elements of the batch as negatives.…”

Section: Modelsmentioning

confidence: 99%

“…Chit-chat agents, by contrast, might focus on coarse statistical regularities of dialogue data without accurately modeling the underlying "meaning"; but the data often covers a much wider space of natural language. For example, Twitter or Reddit chit-chat tasks (Li et al, 2016a;Yang et al, 2018;Mazaré et al, 2018) cover a huge spectrum of language and diverse topics. Chit-chat and goal-oriented dialogue are not mutually exclusive: when humans engage in chit-chat, their aim is to exchange information, or to elicit specific responses from their partners.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents

Prabhumoye¹,

Li²,

Urbanek³

et al. 2020

Preprint

View full text Add to dashboard Cite

Dialogue research tends to distinguish between chit-chat and goal-oriented tasks. While the former is arguably more naturalistic and has a wider use of language, the latter has clearer metrics and a straightforward learning signal. Humans effortlessly combine the two, for example engaging in chit-chat with the goal of exchanging information or eliciting a specific response. Here, we bridge the divide between these two domains in the setting of a rich multi-player text-based fantasy environment where agents and humans engage in both actions and dialogue. Specifically, we train a goal-oriented model with reinforcement learning against an imitation-learned "chit-chat" model with two approaches: the policy either learns to pick a topic or learns to pick an utterance given the top-K utterances from the chit-chat model. We show that both models outperform an inverse model baseline and can converse naturally with their dialogue partner in order to achieve goals.

show abstract

“…To train the model, a cross entropy loss is used. Similar to Mazaré et al (2018), during training we consider the other elements of the batch as negatives.…”

Section: Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents

Prabhumoye¹,

Li²,

Urbanek³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…However, we use a straightforward strategy that directly concatenates the speaker's name with the corresponding utterance. This strategy is inspired by recent research in personalized dialogue modeling that use persona information to represent speak- ers (Li et al, 2016;Zhang et al, 2018b;Mazaré et al, 2018). In subsection 5.2, we will empirically demonstrate its superiority over the feature-based method in Lee et al (2017).…”

Section: Input Representationsmentioning

confidence: 99%

“…We compare our speaker modeling strategy (denoted by speaker as input), which directly concatenates the speaker's name with the corresponding utterance, with the strategy in Wiseman et al larger number of speakers. Compared with the coarse modeling of whether two utterances are from the same speaker, a speaker's name can be thought of as speaker ID in persona dialogue learning (Li et al, 2016;Zhang et al, 2018b;Mazaré et al, 2018). Representations learned for names have the potential to better generalize the global information of the speakers in the multi-party dialogue situation, leading to better context modeling and thus better results.…”

Section: Analyses On Speaker Modeling Strategiesmentioning

confidence: 99%

Coreference Resolution as Query-based Span Prediction

Wu,

Wang,

Yuan

et al. 2019

Preprint

View full text Add to dashboard Cite

In this paper, we present an accurate and extensible approach for the coreference resolution task. We formulate the problem as a span prediction task, like in machine reading comprehension (MRC): A query is generated for each candidate mention using its surrounding context, and a span prediction module is employed to extract the text spans of the coreferences within the document using the generated query. This formulation comes with the following key advantages: (1) The span prediction strategy provides the flexibility of retrieving mentions left out at the mention proposal stage; (2) In the MRC framework, encoding the mention and its context explicitly in a query makes it possible to have a deep and thorough examination of cues embedded in the context of coreferent mentions; and (3) A plethora of existing MRC datasets can be used for data augmentation to improve the model's generalization capability. Experiments demonstrate significant performance boost over previous models, with 87.5 (+2.5) F1 score on the GAP benchmark and 83.1 (+3.5) F1 score on the CoNLL-2012 benchmark.

show abstract

“…Further developments propose to use a speaker embedding vector in neural models to capture the implicit speaking style of an individual speaker [20,23,31,46,48], or the style of a group of speakers [45]. Other approaches also attempt to endow dialogue models with personae which are described by natural language sentences [26,47].…”

Section: Introductionmentioning

confidence: 99%

Personalized Dialogue Generation with Diversified Traits

Zheng¹,

Chen²,

Huang³

et al. 2019

Preprint

View full text Add to dashboard Cite

Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues.To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science.Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.

show abstract

Training Millions of Personalized Dialogue Agents

Cited by 25 publications

References 0 publications

I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents

I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents

Coreference Resolution as Query-based Span Prediction

Personalized Dialogue Generation with Diversified Traits

Contact Info

Product

Resources

About