Research on the Construction and Practice of College English Spoken Language Network Training Camp under the “Let’s talk” Model

Based on the"Understandable Output Hypothesis"a practical study on the construction of college oral English network training camp is set up,through speech learning and imitation,building language input in natural environment,exploring effective output mode based on information technology platform,providing foreign language learners with opportunities to express language and get feedback.Students use relevant resources on the Internet to complete the oral activities of"thematic activities"together,so as to cultivate students'cooperative learning,communication skills,team spirit and language communication ability.

COMPREHENSION OF SPOKEN LANGUAGE AND ITS IMPLICATIONS FOR TEACHING LISTENING

Introduction For many years, listening comprehension has been taken for granted as a course and has simply been continuous and constant listening given to the students regardless of the strategies employed, the stages needed to achieve the goal and the appropriateness of the materials. Teachers teach their students listening in the way they themselves were taught. This empirical perspective of teaching, which lacks a sufficiently scientific basis, has been challenged in recent years by evidence from psycholinguistic research. This influential field of study has provided many fresh insights into the nature of the human mind and broadened our view of language teaching.

Intent-Slot Correlation Modeling for Joint Intent Prediction and Slot Filling

Slot filling and intent prediction are basic tasks in capturing semantic frame of human utterances.Slots and intent have strong correlation for semantic frame parsing.For each utterance,a specific intent type is generally determined with the indication information of words having slot tags(called as slot words),and in reverse the intent type decides that words of certain categories should be used to fill as slots.However,the Intent-Slot correlation is rarely modeled explicitly in existing studies,and hence may be not fully exploited.In this paper,we model Intent-Slot correlation explicitly and propose a new framework for joint intent prediction and slot filling.Firstly,we explore the effects of slot words on intent by differentiating them from the other words,and we recognize slot words by solving a sequence labeling task with the bi-directional long short-term memory(BiLSTM)model.Then,slot recognition information is introduced into attention-based intent prediction and slot filling to improve semantic results.In addition,we integrate the Slot-Gated mechanism into slot filling to model dependency of slots on intent.Finally,we obtain slot recognition,intent prediction and slot filling by training with joint optimization.Experimental results on the benchmark Air-line Travel Information System(ATIS)and Snips datasets show that our Intent-Slot correlation model achieves state-of-the-art semantic frame performance with a lightweight structure.