Conference

Enriching and Controlling Global Semantics for Text Summarization
Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative …
Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences
Understanding human preferences, along with cultural and social nuances, lives at the heart of natural language understanding. …
Capturing Greater Context for Question Generation
Automatic question generation can benefit many applications ranging from dialogue systems to reading comprehension. While questions are …
Light and Efficient Neural Natural Language Processing with Quaternion Networks
Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of …
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives
This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens. We …
Compositional De-Attention Networks
Attentional models are distinctly characterized by their ability to learn relative importance, i.e., assigning a different weight to …
Holographic Factorization Machines for Recommendation
Factorization Machines (FMs) are a class of popular algorithms that have been widely adopted for collaborative filtering and …
Densely Connected Attention Propagation for Reading Comprehension
We propose DecaProp (Densely Connected Attention Propagation), a new densely connected neural architecture for reading comprehension …
Holistic Multi-modal Memory Network for Movie Question Answering
Answering questions according to multi-modal context is a challenging problem as it requires a deep integration of different data …
Recurrently Controlled Recurrent Networks
Recurrent neural networks (RNNs) such as long short-term memory and gated recurrent units are pivotal building blocks across a broad …