28,240 research outputs found

    BERT with History Answer Embedding for Conversational Question Answering

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    Conversational search is an emerging topic in the information retrieval community. One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question. Existing methods either prepend history turns to the current question or use complicated attention mechanisms to model the history. We propose a conceptually simple yet highly effective approach referred to as history answer embedding. It enables seamless integration of conversation history into a conversational question answering (ConvQA) model built on BERT (Bidirectional Encoder Representations from Transformers). We first explain our view that ConvQA is a simplified but concrete setting of conversational search, and then we provide a general framework to solve ConvQA. We further demonstrate the effectiveness of our approach under this framework. Finally, we analyze the impact of different numbers of history turns under different settings to provide new insights into conversation history modeling in ConvQA.Comment: Accepted to SIGIR 2019 as a short pape

    USING CONSTRUCTIVISM METHOD TO TEACH HORTATORY EXPOSITION FOR GRADE 8 JUNIOR HIGH SCHOOL STUDENTS

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    The types of learners are really different. They have their own style in understanding some materials. The teacher will face any obstacle in giving some materials for the grade 8 students of junior high school because there are some students who are really fast in catching some materials but we couldn’t forget that there are some students who are really slowly in getting some materials. The wise method to be applied in this case is constructivist because it will involve whole students for having collaborating in lesson activity. Moreover that the material will be taught is about hortatory exposition where students can share and argue their opinion relating with some recent issues. That is why there are so many beneficial in conducting this project. In the end of process, we will know that they will increase their comprehension and it will be shown an improvement in their attitude toward what hortatory exposition is

    Collaborative trails in e-learning environments

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    This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

    A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems

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    Conversational recommender systems (CRS) generate recommendations through an interactive process. However, not all CRS approaches use human conversations as their source of interaction data; the majority of prior CRS work simulates interactions by exchanging entity-level information. As a result, claims of prior CRS work do not generalise to real-world settings where conversations take unexpected turns, or where conversational and intent understanding is not perfect. To tackle this challenge, the research community has started to examine holistic CRS, which are trained using conversational data collected from real-world scenarios. Despite their emergence, such holistic approaches are under-explored. We present a comprehensive survey of holistic CRS methods by summarizing the literature in a structured manner. Our survey recognises holistic CRS approaches as having three components: 1) a backbone language model, the optional use of 2) external knowledge, and/or 3) external guidance. We also give a detailed analysis of CRS datasets and evaluation methods in real application scenarios. We offer our insight as to the current challenges of holistic CRS and possible future trends.Comment: Accepted by 5th KaRS Workshop @ ACM RecSys 2023, 8 page
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