Learning to Speak and Act in a Fantasy Text Adventure Game

Urbanek, Jack; Fan, Angela; Karamcheti, Siddharth; Jain, Sameer; Humeau, Samuel; Dinan, Emily; Rocktäschel, Tim; Szlam, Arthur; Weston, Jason

doi:10.48550/arxiv.1903.03094

Cited by 13 publications

(23 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We work in the LIGHT game environment (Urbanek et al, 2019), which is a multi-user text-based game. Characters can speak to each other via free text, send emote actions like applaud, nod or pout (22 emote types in total), and take actions to move to different locations and interact with objects (e.g.…”

Section: Light Game Environmentmentioning

confidence: 99%

“…Players were not given specific goals, but instead asked to play the role convincingly of the character given, during play some of them effectively defined their own goals during the interactions, see Appendix Figure 3. Existing work (Urbanek et al, 2019) does not consider using this data to learn goal-based tasks, but instead has only used this for chit-chat and action imitation learning.…”

Section: Light Game Environmentmentioning

confidence: 99%

“…Base agent architecture For all our models we adopt the same base architecture, which is a 12-layer bidirectional transformer (Vaswani et al, 2017) pre-trained on a large dialogue corpus (Reddit, 174M examples), and then finetuned on our task. To score retrieval candidates, we use a bi-encoder as in (Humeau et al, 2019;Urbanek et al, 2019). That is, two transformers are used, one to encode the context, and another to encode a candidate response, and a dot product between the first output vector of each scores the match.…”

Section: Modelsmentioning

confidence: 99%

“…In this work, we introduce a family of tasks that bridge the divide between goal-oriented and chit-chat dialogue, combining clearer metrics and learning signals on the one hand, with the richness and complexity of situated but opendomain natural language on the other. The tasks are set in a multi-player text-based fantasy environment (Urbanek et al, 2019) with grounded actions and reference objects. Given a particular character to play in a particular scenario (location, set of objects and other characters to interact with), an agent should conduct open-ended dialogue with the aim of persuading their dialogue partner to execute a specified action.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Self Cite

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

Section: Light Game Environmentmentioning

confidence: 99%

Section: Light Game Environmentmentioning

confidence: 99%

Section: Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Remark on Further Impacts Our benchmark design opens opportunities beyond commonsense evaluation. For example, the form of our tasks, compared to the relevant tasks of next-sentence generation, such as the SWAG (Zellers et al, 2018) and LIGHT (Urbanek et al, 2019), introduces actions as intervention, thus encourage causal reasoning. Therefore it has a potential impact on causal knowledge acquisition.…”

Section: Data Construction From the Forward Prediction Taskmentioning

confidence: 99%

Deriving Commonsense Inference Tasks from Interactive Fictions

Yu,

Guo,

Feng

et al. 2020

Preprint

View full text Add to dashboard Cite

Commonsense reasoning simulates the human ability to make presumptions about our physical world, and it is an indispensable cornerstone in building general AI systems. We propose a new commonsense reasoning dataset based on human's interactive fiction game playings as human players demonstrate plentiful and diverse commonsense reasoning. The new dataset mitigates several limitations of the prior art. Experiments show that our task is solvable to human experts with sufficient commonsense knowledge but poses challenges to existing machine reading models, with a big performance gap of more than 30%.1 These datasets do contain a portion of instances that require explicit reasoning capacity, especially Huang et al., 2019;Bisk et al., 2020;Sap et al., 2019b). Still, many of the instances can be solved with standalone facts.

show abstract

Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge

Jang¹,

Lim²,

Hur³

et al. 2022

AAAI

View full text Add to dashboard Cite

Humans usually have conversations by making use of prior knowledge about a topic and background information of the people whom they are talking to. However, existing conversational agents and datasets do not consider such comprehensive information, and thus they have a limitation in generating the utterances where the knowledge and persona are fused properly. To address this issue, we introduce a call For Customized conversation (FoCus) dataset where the customized answers are built with the user's persona and Wikipedia knowledge. To evaluate the abilities to make informative and customized utterances of pre-trained language models, we utilize BART and GPT-2 as well as transformer-based models. We assess their generation abilities with automatic scores and conduct human evaluations for qualitative results. We examine whether the model reflects adequate persona and knowledge with our proposed two sub-tasks, persona grounding (PG) and knowledge grounding (KG). Moreover, we show that the utterances of our data are constructed with the proper knowledge and persona through grounding quality assessment.

show abstract

Learning to Speak and Act in a Fantasy Text Adventure Game

Cited by 13 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

Deriving Commonsense Inference Tasks from Interactive Fictions

Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge

Contact Info

Product

Resources

About