108 citations · 162 across the 11 of their papers we have counts for
16 papers
Should I Trust You? Detecting Deception in Negotiations using Counterfactual RL
Wichayaporn Wongkamjan, Yanze Wang, Feng Gu +4
An increasingly common socio-technical problem is people being taken in by offers that sound ``too good to be true'', where persuasion and trust shape decision-making. This paper i…
Personalized Help for Optimizing Low-Skilled Users' Strategy
Feng Gu, Wichayaporn Wongkamjan, Jonathan K. Kummerfeld +3
AIs can beat humans in game environments; however, how helpful those agents are to human remains understudied. We augment CICERO, a natural language agent that demonstrates superhu…
More Victories, Less Cooperation: Assessing Cicero's Diplomacy Play
Wichayaporn Wongkamjan, Feng Gu, Yanze Wang +6
The boardgame Diplomacy is a challenging setting for communicative and cooperative artificial intelligence. The most prominent communicative Diplomacy AI, Cicero, has excellent str…
Fool Me Twice: Entailment from Wikipedia Gamification
Julian Martin Eisenschlos, Bhuwan Dhingra, Jannis Bulian +2
We release FoolMeTwice (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game. Gamification encourages adversarial examples, dra…
CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims
Thomas Diggelmann, Jordan Boyd-Graber, Jannis Bulian +2
We introduce CLIMATE-FEVER, a new publicly available dataset for verification of climate change-related claims. By providing a dataset for the research community, we aim to facilit…
ClimaText: A Dataset for Climate Change Topic Detection
Francesco S. Varini, Jordan Boyd-Graber, Massimiliano Ciaramita +1
Climate change communication in the mass media and other textual sources may affect and shape public perception. Extracting climate change information from these sources is an impo…
Meta Answering for Machine Reading
Benjamin Borschinger, Jordan Boyd-Graber, Christian Buck +7
We investigate a framework for machine reading, inspired by real world information-seeking problems, where a meta question answering system interacts with a black box environment.…
What Question Answering can Learn from Trivia Nerds
Jordan Boyd-Graber, Benjamin Börschinger
In addition to the traditional task of getting machines to answer questions, a major research question in question answering is to create interesting, challenging questions that ca…
Automatic Evaluation of Local Topic Quality
Jeffrey Lund, Piper Armstrong, Wilson Fearn +4
Topic models are typically evaluated with respect to the global topic distributions that they generate, using metrics such as coherence, but without regard to local (token-level) t…
Automatic Estimation of Simultaneous Interpreter Performance
Craig Stewart, Nikolai Vogler, Junjie Hu +2
Simultaneous interpretation, translation of the spoken word in real-time, is both highly challenging and physically demanding. Methods to predict interpreter confidence and the ade…
Lessons from the Bible on Modern Topics: Low-Resource Multilingual Topic Model Evaluation
Shudong Hao, Jordan Boyd-Graber, Michael J. Paul
Multilingual topic models enable document analysis across languages through coherent multilingual summaries of the data. However, there is no standard and effective metric to evalu…
Learning to Color from Language
Varun Manjunatha, Mohit Iyyer, Jordan Boyd-Graber +1
Automatic colorization is the process of adding color to greyscale images. We condition this process on language, allowing end users to manipulate a colorized image by feeding in d…
Inducing and Embedding Senses with Scaled Gumbel Softmax
Fenfei Guo, Mohit Iyyer, Jordan Boyd-Graber
Methods for learning word sense embeddings represent a single word with multiple sense-specific vectors. These methods should not only produce interpretable sense embeddings, but s…
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
Khanh Nguyen, Hal Daumé, Jordan Boyd-Graber
Machine translation is a natural candidate problem for reinforcement learning from human feedback: users provide quick, dirty ratings on candidate translations to guide a system to…
The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives
Mohit Iyyer, Varun Manjunatha, Anupam Guha +4
Visual narrative is often a combination of explicit information and judicious omissions, relying on the viewer to supply missing details. In comics, most movements in time and spac…
Opponent Modeling in Deep Reinforcement Learning
He He, Jordan Boyd-Graber, Kevin Kwok +1
Opponent modeling is necessary in multi-agent settings where secondary agents with competing goals also adapt their strategies, yet it remains challenging because strategies intera…