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Immersive virtual environments provide users with the opportunity to escape from the real world, but scripted dialogues can disrupt the presence within the world the user is trying to escape within. Both Non-Playable Character (NPC) to Player and NPC to NPC dialogue can be non-natural and the reliance on responding with pre-defined dialogue does not always meet the players emotional expectations or provide responses appropriate to the given context or world states. This paper investigates the application of Artificial Intelligence (AI) and Natural Language Processing to generate dynamic human-like responses within a themed virtual world. Each thematic has been analysed against humangenerated responses for the same seed and demonstrates invariance of rating across a range of model sizes, but shows an effect of theme and the size of the corpus used for fine-tuning the context for the game world.
A Case Study of the Basic Learners’ Struggles in Guessing from Context to Retain Words Learned
(2022)
Guessing meaning from context is a challenging strategy for Second Language Learners (SLLs). In using the strategy, research found that poor students or low proficiency learners struggled in their attempts to use it. Mainly, it was reported that it was due to their vocabulary knowledge was limited. In another aspect, retaining vocabulary learnt is also important. Such is essential since learning vocabulary does not mean knowing the definition only. Yet, learners must also be able to use the vocabulary as they engage in language skills such as reading, writing, speaking and listening. The study aims at finding the hindrances faced among poor students’ using contextual clues in retaining vocabulary. The study employed a case study to collect data from two basic students studying at a tertiary level. The study found that their hindrances in guessing meaning contexts were due to their being confused in guessing meaning when reading a sentence. Also, it was found that they were not able to find clues since they lacked vocabulary to guess correctly. The study implied that guessing meaning from context required sizeable vocabulary knowledge. Therefore, more training is necessary to assist basic learners in being successful in guessing from contexts.
English language is being taught as a second foreign language in India. For most of the learners in India, English still a foreign language or target language. The study of this language is important to fulfill different kinds of academic and professional requirements. Still, there is a big gulf between demand and supply for which the failure of the system is largely responsible as its main emphasis on to adherence to the foreign curriculum. The government tries to impose this curriculum on English teachers, but, in fact, the curriculum is outdated.
France, Italy, and Spain are three Romance-speaking countries which – at least in Europe – have been affected to a very high degree by the consequences of the Corona pandemic. This paper examines discursive strategies on social media (Twitter and Facebook) by the three heads of government/state of the aforementioned countries – namely Emmanuel Macron (France), Giuseppe Conte (Italy), and Pedro Sánchez (Spain)- from a corpuslinguistic point of view. For this purpose, a corpus was created which contains all Twitter and Facebook messages posted by these heads of government/state from the beginning of February until the end of April 2020. By applying corpus-linguistic methods we find that all three politicians consciously use social media to sensitize, inform, and – in view of a dramatic pandemic situation – unite their respective populations behind them.