Recognizing Phrasal Entailments using Image Groundings

在这个项目中，我创造了一种有效的技术来高效地提取短语，灵感来自树形结构的双语提取。我还设计并评估了一种比较图像集并评估其蕴涵关系的方法，这也是该领域的新方法。最后，我将这些创新集成到一个端到端系统中，以衡量它们对识别文本蕴涵任务的影响，观察到准确率净提高了1.2%。我的策略在名词、名词短语和形容词+名词上最成功；然而，对于大多数动词短语和抽象名词来说，这种方法并不有效，因为这些短语不能很好地扎根于图像中。这项工作已发表在JSAI 2017和EMNLP 2017上，并被其他有影响力的会议和期刊引用了9次。

For this project, I created an effective technique to efficiently extract phrases inspired by bilingual extractions of tree structures. I also designed and evaluated a method to compare sets of images and assess their entailment relation which was also new in the field. Finally, I integrated these innovations in an end-to-end system to measure their impact in the task of Recognizing Textual Entailment, observing a net increase in accuray of 1.2%. My strategy was most successful with nouns, noun phrases and adjectives + nouns; however, it was not effective for most verb phrases and abstract nouns since these phrases cannot be well grounded into images. This worked has been published in JSAI 2017 and EMNLP 2017 which has received 9 citations from other impactful conferences and journals.