4 papers
Local Additivity Based Data Augmentation for Semi-supervised NER
Jiaao Chen, Zhenghui Wang, Ran Tian +2
Named Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data. In this work, to alleviate the…
Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation
Ran Tian, Shashi Narayan, Thibault Sellam +1
We address the issue of hallucination in data-to-text generation, i.e., reducing the generation of text that is unsupported by the source. We conjecture that hallucination can be c…
Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder
Ryo Takahashi, Ran Tian, Kentaro Inui
Embedding models for entities and relations are extremely useful for recovering missing facts in a knowledge base. Intuitively, a relation can be modeled by a matrix mapping entity…
Learning Semantically and Additively Compositional Distributional Representations
Ran Tian, Naoaki Okazaki, Kentaro Inui
This paper connects a vector-based composition model to a formal semantics, the Dependency-based Compositional Semantics (DCS). We show theoretical evidence that the vector composi…