This research focuses on the influence of the first language (L1) transfer on discourse marker use in the second language (L2). Some researchers (e.g., Liu, 2013; Sankoff et al., 1997) conducted a contrastive analysis of L1 and L2 speech and suggested that non-native speakers’ use of discourse markers in their L1 may be transferred to their use of English discourse markers. However, there have been only a few detailed studies investigating the effect of L1 on discourse marker use by non-native speakers of English. To rectify the inadequacies, the current research comprises two cross-linguistic studies. The first study applied contrastive analysis to English-Japanese parallel corpus data: English speech data extracted from a Japanese EFL learner corpus and their Japanese translations. Through the comparison using the parallel data, the analysis revealed some correspondences between English and Japanese discourse markers. Based on the findings of the parallel texts, in the second study, a small-scale experiment was carried out using a picture description task. The learners’ L1 and L2 utterances were collected under the same task condition to explore how the use of Japanese discourse markers influenced the use of English discourse markers. The results of the quantitative and qualitative analyses suggested that the use of Japanese discourse markers or fillers may cause the overuse of some markers such as “and,” “so,” and “but.” Although the study conducted a limited observation of certain discourse markers, these findings may be part of the evidence of L1 influence on discourse marker use in L2 learners’ speech and contribute to identifying the features of their acquisition of discourse markers.
This session has been cancelled. Some nouns can be treated as countable and uncountable depending on contexts. However, it is sometimes difficult to distinguish them for novice english learners, especially for learners of ESL. This study applies Machine Learning techniques to make it clear when nouns should be treated as countable and when should not. A stochastic learning model like a well-known Bayesian Network is used for the purpose. It is trained to estimate the probability of simultaneous appearance of countable/uncountable nouns with other words. Native english texts taken from British National Corpus are converted into Latent Semantic Index (LSI) vectors with part-of-speech tags and used to train the model. A LSI is an indexing number of words, which becomes same when the different words are used in same or similar contexts and consequently thought as having same or similar meaning. Thus, LSI vectors make the word space compact and reduce ambiguity. Results will show the high probability in some combinations of specific type of words, in other words, the probability becomes high in some contexts. Which means that it is easy to determine whether nouns should be treated as countable or uncountable in the contexts. Contrarily, the low probability means difficult to determine. The information is useful for both teachers and students. Furthermore, the trained model can be applied to machine translation systems. There are several related works. Baldwin and Bold have proposed a clustering method to estimate countability from corpus data in 2003. Nagata et. al. have proposed a method to determine countability of nouns according to their contexts in 2006. However, they used deterministic models and could not estimate the probability of countability. We think the probability is important to know how the context is difficult to determine countability which can be used to teach and learn english.
It is difficult for ESL learners to discern if a noun is countable or not and in what context. This study applies Machine Learning techniques and tries to make it clear in what conditions nouns should be treated as countable or not.
A stochastic learning model like a well-known Bayesian Network is used for the purpose. The model is trained to estimate the probability of simultaneous appearance of countable/uncountable nouns with other non-target words.
Native English texts taken from a corpus are converted into Latent Semantic Index (LSI) vectors with part-of-speech tags and used to train the model. An LSI is an indexing number of words, which becomes the same when words are used in same or similar contexts and consequently have the same or similar meaning. Thus, LSI vectors make the word space compact and reduce ambiguity. The results will be shown as a probability in which nouns should be treated as countable or uncountable in the contexts under examination. The high probability means it is easy to determine whether nouns should be treated as countable or uncountable in the contexts. On the contrary, the low probability means it is difficult to determine.
Such information would be useful for both teachers and students of ESL. Furthermore, the trained model can be applied to machine translation systems. There are several related works in the field. Baldwin and Bold (2003) have proposed a clustering method to estimate countability from corpus data. Nagata et al. (2006) have proposed a method to determine countability of nouns. However, they used deterministic models and could not estimate the probability of countability.
We believe the probability is important to determine what kind of context is difficult for ESL students to discern countability and what contexts should be used in teaching and learning noun countability.
Teachers have contrived teaching methods and materials utilizing multimedia in classrooms so that their students can understand class easily. What has happened in TEFL class in Japan, is not an exception at all. Even a textbook has been changed from monochrome to colorful, and one picture per a few pages to one per one page. Such a textbook with much pictorial information enables students to feel that they can read the text without much difficulty, but this is not always true. Colorful pictures conveying the story help the students imagine and conjecture the outline of the story. A problem is that students who have got used to pictures won’t read the text thoroughly by bottom-up information processing, and that they may have a trouble in reading in the end.
This study will suggest a possibility of giving cognitive load to students so as to foster learners with much cognitive capacity.
