Neural text generation models that are conditioned on a given input (e.g., machine translation and image captioning) are typically trained through maximum likelihood estimation of the target text. However, models trained in this manner often suffer from various types of errors when making subsequent inferences. In this study, we propose suppressing an arbitrary type of error by training the text generation model in a reinforcement learning framework; herein, we use a trainable reward function that can discriminate between references and sentences, containing the targeted type of errors. We create such negative examples by artificially injecting the targeted errors into the references. In the experiments, we focus on two error types; repeated and dropped tokens in model-generated text. The experimental results demonstrate that our method can suppress generation errors, and achieves significant improvements on two machine translation and two image captioning tasks.
Neural machine translation often suffers from an under-translation problem owing to its limited modeling of the output sequence lengths. In this study, we propose a novel approach to training a Transformer model using length constraints based on length-aware positional encoding (PE). Because length constraints with exact target sentence lengths degrade the translation performance, we add a random perturbation with a uniform distribution within a certain range to the length constraints in the PE during the training. In the inference step, we predicted the output lengths from the input sequences using a length prediction model based on a large-scale pre-trained language model. In Japanese-to-English and English-to-Japanese translation, experimental results show that the proposed perturbation injection improves the robustness of the length prediction errors, particularly within a certain range.
Okazaki et al. (2018) have proposed a method for organizing the information contained in multiple documents into a table without limiting the information to be extracted. In this study, we propose a method for improving the accuracy of these tables. In our proposed method, information is first clustered hierarchically. Next, for the results of hierarchical clustering (with the number of clusters ranging from 1 to n), the degree of filling and the information density of the resulting table are calculated. The number of clusters when the balance between these two indicators is optimal is chosen as the optimal number of clusters. The results of the method using the chosen number of clusters are organized into a table. In the conventional method, the number of clusters estimated by the X-means method tends to be too small. As demonstrated by the results of experiments using 15 types of multiple documents, the proposed method improves this problem, with its estimated number of clusters being closer to the optimum. The average evaluation result in the tables (F-measure) when applying the conventional method was 0.43; the proposed method improves this to 0.65. We therefore confirm the effectiveness of the proposed method.