NUIG-DSI's submission to The GEM Benchmark 2021

This paper describes the submission by NUIG-DSI to the GEM benchmark 2021. We participate in the modeling shared task where we submit outputs on four datasets for data-to-text generation, namely, DART, WebNLG (en), E2E and CommonGen. We follow an approach similar to the one described in the GEM benchmark paper where we use the pre-trained T5-base model for our submission. We train this model on additional monolingual data where we experiment with different masking strategies specifically focused on masking entities, predicates and concepts as well as a random masking strategy for pre-training. In our results we find that random masking performs the best in terms of automatic evaluation metrics, though the results are not statistically significantly different compared to other masking strategies.

Structure-to-Text Generation with Self-Training, Acceptability Classifiers and Context-Conditioning for the GEM Shared Task

We explore the use of self-training and acceptability classifiers with pre-trained models for natural language generation in structure-to-text settings using three GEM datasets (E2E, WebNLG-en, Schema-Guided Dialog). With the Schema-Guided Dialog dataset, we also experiment with including multiple turns of context in the input. We find that self-training with reconstruction matching along with acceptability classifier filtering can improve semantic correctness, though gains are limited in the full-data setting. With context-conditioning, we find that including multiple turns in the context encourages the model to align with the user's word and phrasing choices as well as to generate more self-consistent responses. In future versions of the GEM challenge, we encourage the inclusion of few-shot tracks to encourage research on data efficiency.

The Relationship Between External Environment Factors and The Entrepreneurial Intention of Syrian University Students Using The Global Entrepreneurial Monitor (GEM)

The aim of this research is to explore the incentives and barriers in the external environment to entrepreneurial intention among Syrian University Students and the relevant importance of each variable, in addition to exploring the statistical relationship between several external variables and the entrepreneurial intention among students. The study utilized items from the Global Entrepreneurial Monitor (GEM) to assess the external environment conditions. Statistical analysis including descriptive statistics, Pearson correlation, and Factor analysis were optimized to test the research hypothesis.