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Research - Papers

Explore a selection of our published work on a variety of key research challenges in AI.

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Discourse-Aware Neural Rewards For Coherent Text Generation

Antoine BosselutAsli CelikyilmazXiaodong HePo-Sen Huang and Yejin Choi
2018
NAACL

In this paper, we investigate the use of discourse-aware rewards with reinforcement learning to guide a model to generate long, coherent text. In particular, we propose to learn neural rewards to… 

Neural Motifs: Scene Graph Parsing with Global Context

Rowan ZellersMark YatskarSam ThomsonYejin Choi
2018
CVPR

We investigate the problem of producing structured graph representations of visual scenes. Our work analyzes the role of motifs: regularly appearing substructures in scene graphs. We present new… 

Neural Poetry Translation

Marjan GhazvininejadYejin Choi and Kevin Knight
2018
NAACL

We present the first neural poetry translation system. Unlike previous works that often fail to produce any translation for fixed rhyme and rhythm patterns, our system always translates a source… 

Sounding Board: A User-Centric and Content-Driven Social Chatbot

Hao FangHao ChengMaarten Sapand Mari Ostendorf
2018
NAACL-HTL

We present Sounding Board, a social chatbot that won the 2017 Amazon Alexa Prize. The system architecture consists of several components including spoken language processing, dialogue management,… 

Simulating Action Dynamics with Neural Process Networks

Antoine BosselutOmer LevyAri Holtzmanand Yejin Choi
2018
ICLR

Understanding procedural language requires anticipating the causal effects of actions, even when they are not explicitly stated. In this work, we introduce Neural Process Networks to understand… 

Learning Interpretable Spatial Operations in a Rich 3D Blocks World

Yonatan BiskKevin J. ShihYejin Choiand Daniel Marcu
2018
AAAI

In this paper, we study the problem of mapping natural language instructions to complex spatial actions in a 3D blocks world. We first introduce a new dataset that pairs complex 3D spatial… 

Dynamic Entity Representations in Neural Language Models

Yangfeng JiChenhao TanSebastian MartschatNoah A. Smith
2017
EMNLP

Understanding a long document requires tracking how entities are introduced and evolve over time. We present a new type of language model, EntityNLM, that can explicitly model entities, dynamically… 

Zero-Shot Activity Recognition with Verb Attribute Induction

Rowan ZellersYejin Choi
2017
EMNLP

In this paper, we investigate large-scale zero-shot activity recognition by modeling the visual and linguistic attributes of action verbs. For example, the verb “salute” has several properties, such… 

The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze Task

Roy SchwartzMaarten SapIoannis KonstasNoah A. Smith
2017
CoNLL

A writer’s style depends not just on personal traits but also on her intent and mental state. In this paper, we show how variants of the same writing task can lead to measurable differences in… 

Verb Physics: Relative Physical Knowledge of Actions and Objects

Maxwell ForbesYejin Choi
2017
ACL

Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., “My house is bigger than me.” However, while rarely stated…