Arthur Szlam

Dialogue Natural Language Inference

2019

Consistency is a long standing issue faced by dialogue models. In this paper, we frame the consistency of dialogue agents as natural language inference (NLI) and create a new natural language inference dataset called Dialogue …

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

2020 · arXiv (Cornell University)

Dialogue research tends to distinguish between chit-chat and goal-oriented\ntasks. While the former is arguably more naturalistic and has a wider use of\nlanguage, the latter has clearer metrics and a straightforward learning signal.\nHumans effortlessly combine the …

End-To-End Memory Networks

2015 · arXiv (Cornell University)

We introduce a neural network with a recurrent attention model over a possibly large external memory. The architecture is a form of Memory Network (Weston et al., 2015) but unlike the model in that work, …

Tracking the World State with Recurrent Entity Networks

2016 · arXiv (Cornell University)

We introduce a new model, the Recurrent Entity Network (EntNet). It is equipped with a dynamic long-term memory which allows it to maintain and update a representation of the state of the world as it …

Automatic Rule Extraction from Long Short Term Memory Networks

2017 · arXiv (Cornell University)

Although deep learning models have proven effective at solving problems in natural language processing, the mechanism by which they come to their conclusions is often unclear. As a result, these models are generally treated as …

Personalizing Dialogue Agents: I have a dog, do you have pets too?

2018 · arXiv (Cornell University)

Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging …