2020/12/28 by Keisuke Shirai, Kazuma Hashimoto, Shirai, Keisuke +7 · 3 citations
Computer Science · #Artificial Intelligence (cs.AI) #Artificial intelligence #Closed captioning #Computation and Language (cs.CL) #Computer science #FOS: Computer and information sciences #Focus (optics) #Function (biology) #Image (mathematics) #Inference #Machine learning #Machine translation #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Natural language processing #Pattern recognition (psychology) #Reinforcement learning #Speech recognition #Text generation #Topic Modeling #Translation (biology) #cs.AI #cs.CL
paper · pdf · doi:10.48550/arxiv.2012.14124
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/12/28 · openalex publication_date 2020/12/28 · arxiv updated 2020/12/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Neural text generation models conditioning on given input (e.g. machine translation and image captioning) are usually trained by maximum likelihood estimation of target text. However, the trained models suffer from various types of errors at inference time. In this paper, we propose to suppress an arbitrary type of errors by training the text generation model in a reinforcement learning framework, where we use a trainable reward function that is capable of discriminating between references and sentences containing the targeted type of errors. We create such negative examples by artificially injecting the targeted errors to the references. In experiments, we focus on two error types, repeated and dropped tokens in model-generated text. The experimental results show that our method can suppress the generation errors and achieve significant improvements on two machine translation and two image captioning tasks.