The Effect of Synonym Relationship Upon the Acquisition of Multi-Dimensional Vocabulary Knowledge

Based on Nation’s framework of multi-dimensional vocabulary knowledge, this study designed a multi-dimensional vocabulary knowledge test and examined the effect of word pair and sentence with and without synonyms on the acquisition of the multi-dimensional vocabulary knowledge of target words with respect to orthography, meaning and form, grammatical function syntagmatic association and paradigmatic association. Experiment results indicated that the participants obtained significant more scores for the target words with known high frequency synonyms than for those without known synonyms in terms of the receptive vocabulary knowledge of syntagmatic association and orthography and the productive vocabulary knowledge of paradigmatic association. Hence it can be concluded that the known synonyms might be conducive to the acquisition of the unknown synonyms. Implications of the results were discussed

Auto-Encoding Scene Graphs for Image Captioning

We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions. Intuitively, we humans use the inductive bias to compose collocations and contextual inference in discourse. For example, when we see the relation `person on bike', it is natural to replace `on' with `ride' and infer `person riding bike on a road' even the `road' is not evident. Therefore, exploiting such bias as a language prior is expected to help the conventional encoder-decoder models less likely overfit to the dataset bias and focus on reasoning. Specifically, we use the scene graph --- a directed graph ($\mathcal{G}$) where an object node is connected by adjective nodes and relationship nodes --- to represent the complex structural layout of both image ($\mathcal{I}$) and sentence ($\mathcal{S}$). In the textual domain, we use SGAE to learn a dictionary ($\mathcal{D}$) that helps to reconstruct sentences in the $\mathcal{S}\rightarrow \mathcal{G} \rightarrow \mathcal{D} \rightarrow \mathcal{S}$ pipeline, where $\mathcal{D}$ encodes the desired language prior; in the vision-language domain, we use the shared $\mathcal{D}$ to guide the encoder-decoder in the $\mathcal{I}\rightarrow \mathcal{G}\rightarrow \mathcal{D} \rightarrow \mathcal{S}$ pipeline. Thanks to the scene graph representation and shared dictionary, the inductive bias is transferred across domains in principle. We validate the effectiveness of SGAE on the challenging MS-COCO image captioning benchmark, e.g., our SGAE-based single-model achieves a new state-of-the-art $127.8$ CIDEr-D on the Karpathy split, and a competitive $125.5$ CIDEr-D (c40) on the official server even compared to other ensemble models

Toivon johtaminen esimiesten kokemana

Tutkimuksen tavoitteena on tarkastella työelämää positiivisesta ja tulevaisuusorientoituneesta toivon näkökulmasta. Teoreettisena viitekehyksenä on C. R. Snyderin teoria toivosta tavoitteiden asettamisena ja tavoittelemisena. Puolistrukturoidulla teemahaastattelulla hankitun aineiston analyysissä sovellettiin fenomenologisen psykologian erityistieteelle kehitettyjä menetelmiä. Haastateltujen esimiesten ainutkertaisia kokemuksia toivosta analysoidaan suhteessa heihin itseensä, heidän työhönsä sekä työyhteisöihinsä. Toivon koettuna rakenteena tarkastellaan toiminnallisuutta, tavoitteellisuutta ja myönteisyyttä. Tutkimuksen empiirisen osan tulosten perusteella toivoa luova johtaminen on välittävässä hengessä myönteisen ja luottamuksellisen ilmapiirin luomista. Toivon johtamista tarkastellaan sekä yksilö- että ryhmätasolla