Image Representations and New Domains in Neural Image Captioning

We examine the possibility that recent promising results in automatic caption generation are due primarily to language models. By varying image representation quality produced by a convolutional neural network, we find that a state-of-the-art neural captioning algorithm is able to produce quality captions even when provided with surprisingly poor image representations. We replicate this result in a new, fine-grained, transfer learned captioning domain, consisting of 66K recipe image/title pairs. We also provide some experiments regarding the appropriateness of datasets for automatic captioning, and find that having multiple captions per image is beneficial, but not an absolute requirement.Comment: 11 Pages, 5 Images, To appear at EMNLP 2015's Vision + Learning worksho

THE EFFECTIVENESS OF USING MYENGLISHSTEP AND SUMBER.BELAJAR.KEMDIKBUD.GO.ID IN COMPOSING DESCRIPTIVE

This study investigates how effective using myenglishstep and sumber.belajar.kemdikbud.go.id 7th grade students in composing descriptive text at SMP Negeri 42 Semarang. The writer used a quantitative method. There 2 as experimental classes which is using myenglishstep and sumber.belajar.kemdikbud.go.id. The result of the study is that there is correlation between myenglishstep and sumber.belajar.kemdikbud.go.id in composing descriptive text for 7th grade students.The objective is to discover which show is more viable than the two models when connected in learning to compose descriptive writings in course 7 junior high school. Based on the calculation of the normal increment, the student's last test normal score in the test course 1 of 87.230, where's the normal esteem of the student's last test in the test course 2 of 80.50. There's a distinction in esteem of 6.73 between experimental lesson 1 and exploratory lesson 2. So, it can be concluded that class Experiment 1 is more successful than test course 2.On the other hand, It can be seen that the normal beginning test score was 79,961 and the normal last test score was 87,230. This appears that the utilize of myenglishstep demonstrate is viable when connected to learning compose an informative content since the comes about appear an increment between some time recently and after the exploratory lesson 1 was given a treatment of 7.269.

In defense of compilation: A response to Davis' form and content in model-based reasoning

In a recent paper entitled 'Form and Content in Model Based Reasoning', Randy Davis argues that model based reasoning research aimed at compiling task specific rules from underlying device models is mislabeled, misguided, and diversionary. Some of Davis' claims are examined and his basic conclusions are challenged about the value of compilation research to the model based reasoning community. In particular, Davis' claim is refuted that model based reasoning is exempt from the efficiency benefits provided by knowledge compilation techniques. In addition, several misconceptions are clarified about the role of representational form in compilation. It is concluded that techniques have the potential to make a substantial contribution to solving tractability problems in model based reasoning

A Corpus-based Comparison between the Academic Word List and the Academic Vocabulary List

This study was a corpus-based comparison between two lists of academic words: Coxhead’s (2000) Academic Word List (AWL) and Gardner and Davies’ (2014) Academic Vocabulary List (AVL). Comparisons were made between different types of lexical coverage provided by the AWL and the AVL in the self-created University Academic Corpus (72-million tokens). The findings indicated that the performance of the AWL and the AVL was different when different evaluation criteria were adopted. Implications, limitations, and suggestions are listed for future research

Täiendav tugi ESL- inglise keele õppimiseks koduõppele elektrooniliste iseõppematerjalidega põhikooli õpilastele (9. klass)

https://www.ester.ee/record=b551838