No abstract

Grammatical error correction has been considered as an application closely related to daily life and an important shared task in many prestigious competitions and workshops. The neural machine translation with an encoder-decoder architecture containing language models has been the fundamental solution for the grammatical error correction. Whereas Grammatical error correction task on texts of deaf people or its solution has not been seen yet, and common Grammatical error correction tasks are suffering several challenges, such as insufficient training data, insufficient accuracy due to the unsatisfactory capacity of extracting semantic and grammatical patterns. Under these circumstances, we proposed a novel encoderdecoder architecture based on multi-head self-attention along with multiple strategies, which excels at extracting deep representations from the corrupted sentences of deaf students and further reconstructing the sentences into grammatical ones. Via the re-ranking strategy, our model can correct various kinds of errors including spelling and complex syntax errors. The ablation experiments prove that the semantic extracting of self-attention mechanism excluding the position encoding with the word order shuffle operation can significantly learn the deaf students' sentence patterns whose word order is quite different from the ones of hearing people and improve the correction scores. The pre-training can enhance the restoring efficiency of sentence structure in the decoding process. The comparison experiments with baseline models show that our model obtains superior performance either in the deaf students' grammatical error correction or in a common grammatical error correction shared task.

No abstract

Language learning has increasingly benefited from Computer-Assisted Language Learning (CALL) technologies, especially with Artificial Intelligence involved in recent years. CALL in writing learning acknowledged as the core of language learning is being realized by technologies like Automated Writing Evaluation (AWE), and Automated Essay Scoring (AES), which have developed considerably in both computer and language education fields. AWE has effectively enhanced EFL students’ writing performance to some extent, but such technology can only provide an evaluation in the form of scores, the majority of which are based on holistic scoring, resulting in the inability to provide comprehensive and detailed content-based feedback. In order to provide not only the writing multiple trait-specific evaluation scores, but also detailed writing feedback, we proposed a computer-assisted EFL writing learning system incorporating the neural network models and a couple of semantic-based NLP techniques, MsCAEWL, which fully meets the requirements of writing feedback theory, i.e., multiple, continuous, timely, clear, and multi-aspect guidance interactive feedback. The results of comparison experiments with the AWE baseline models and human raters demonstrated the superiority and the high correlation contained by the proposed system. The independent-sample t-test and paired-sample t-test results of the experiments on MsCAEWL effect validation suggested the significant impact of our proposed system in enhancing students’ EFL writing proficiency.

Reading comprehension is an important language learning skill. In English class, we should pay attention to cultivating students’ reading comprehension ability to help students improve their reading comprehension ability. The application of language service products of artificial intelligence has brought great challenges and opportunities to language teaching. This research starts from the current situation of the teaching mode of English language and literature reading under the background of artificial intelligence. The following four questions are mainly studied. Can the multimodal oral teaching model improve students’ oral English performance? Has the students’ spoken English improved in the three aspects of language content, language accuracy, and pronunciation and intonation? Does multimodal oral teaching have a positive impact on students’ attitudes towards oral learning? How can students improve their English writing? This study uses two research methods, experimental research and questionnaire survey, to explore the above research questions through a 16-week multimodal oral teaching experiment. The subjects of the experiment were 61 non-English majors in two parallel classes in the first year of a university in Xi’an. The experiment is divided into three phases: pretest of oral test, implementation of multimodal oral teaching, and posttest of oral test. The experiment proved that most students (about 67.35%) did not show much interest in reading and writing in English. Only a few students (12.24%) like to read and write. It shows that after accepting the multimodal teaching experiment, the students’ oral English scores have been significantly improved, which is reflected in the language accuracy, pronunciation intonation, and language content. The students’ oral English learning attitude is positive after the multimodal oral teaching.