Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Adversarial Training for Textual Entailment with Knowledge-Guided Examples
We consider the problem of learning textual entailment models with limited supervision (5K-10K training examples), and present two complementary approaches for it. First, we propose knowledge-guided…
Deep Communicating Agents For Abstractive Summarization
We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. With deep communicating agents, the…
Discourse-Aware Neural Rewards For Coherent Text Generation
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…
Natural Language to Structured Query Generation via Meta-Learning
In conventional supervised training, a model is trained to fit all the training examples. However, having a monolithic model may not always be the best strategy, as examples could vary widely. In…
Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension
We present a new dataset and models for comprehending paragraphs about processes (e.g., photosynthesis), an important genre of text describing a dynamic world. The new dataset, ProPara, is the first…
VISIR: Visual and Semantic Image Label Refinement
The social media explosion has populated the Internet with a wealth of images. There are two existing paradigms for image retrieval: 1)content-based image retrieval (BIR), which has traditionally…
What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text
Our goal is to answer questions about paragraphs describing processes (e.g., photosynthesis). Texts of this genre are challenging because the effects of actions are often implicit (unstated),…
Knowledge Completion for Generics Using Guided Tensor Factorization
Given a knowledge base or KB containing (noisy) facts about common nouns or generics, such as "all trees produce oxygen" or "some animals live in forests", we consider the problem of inferring…
Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
We present a new question set, text corpus, and baselines assembled to encourage AI research in advanced question answering. Together, these constitute the AI2 Reasoning Challenge (ARC), which…
Approximate Inference via Weighted Rademacher Complexity
Rademacher complexity is often used to characterize the learnability of a hypothesis class and is known to be related to the class size. We leverage this observation and introduce a new technique…