Fluency-Guided Cross-Lingual Image Captioning
This addresses the language restriction in image captioning applications, offering a method for generating captions in non-English languages, though it is incremental as it builds on existing cross-lingual approaches.
The paper tackles the problem of cross-lingual image captioning by learning from machine-translated sentences, proposing a fluency-guided framework that improves fluency and relevance in Chinese captions without using manually written data.
Image captioning has so far been explored mostly in English, as most available datasets are in this language. However, the application of image captioning should not be restricted by language. Only few studies have been conducted for image captioning in a cross-lingual setting. Different from these works that manually build a dataset for a target language, we aim to learn a cross-lingual captioning model fully from machine-translated sentences. To conquer the lack of fluency in the translated sentences, we propose in this paper a fluency-guided learning framework. The framework comprises a module to automatically estimate the fluency of the sentences and another module to utilize the estimated fluency scores to effectively train an image captioning model for the target language. As experiments on two bilingual (English-Chinese) datasets show, our approach improves both fluency and relevance of the generated captions in Chinese, but without using any manually written sentences from the target language.