Teaching Machines to Ask Useful Clarification Questions

Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. Asking questions is also a natural way for machines to express uncertainty, a task of increasing importance in an automated society. In the field of natural language processing, despite decades of work on question answering, there is relatively little work in question asking. Moreover, most of the previous work has focused on generating reading comprehension style questions which are answerable from the provided text. The goal of my dissertation work, on the other hand, is to understand how can we teach machines to ask clarification questions that point at the missing information in a text. Primarily, we focus on two scenarios where we find such question asking to be useful: (1) clarification questions on posts found in community-driven technical support forums such as StackExchange (2) clarification questions on descriptions of products in e-retail platforms such as Amazon. In this dissertation we claim that, given large amounts of previously asked questions in various contexts (within a particular scenario), we can build machine learning models that can ask useful questions in a new unseen context (within the same scenario). In order to validate this hypothesis, we firstly create two large datasets of context paired with clarification question (and answer) for the two scenarios of technical support and e-retail by automatically extracting these information from available datadumps of StackExchange and Amazon. Given these datasets, in our first line of research, we build a machine learning model that first extracts a set of candidate clarification questions and then ranks them such that a more useful question would be higher up in the ranking. Our model is inspired by the idea of expected value of perfect information: a good question is one whose expected answer will be useful. We hypothesize that by explicitly modeling the value added by an answer to a given context, our model can learn to identify more useful questions. We evaluate our model against expert human judgments on the StackExchange dataset and demonstrate significant improvements over controlled baselines. In our second line of research, we build a machine learning model that learns to generate a new clarification question from scratch, instead of ranking previously seen questions. We hypothesize that we can train our model to generate good clarification questions by incorporating the usefulness of an answer to the clarification question into the recent sequence-to-sequence based neural network approaches. We develop a Generative Adversarial Network (GAN) where the generator is a sequence-to-sequence model and the discriminator is a utility function that models the value of updating the context with the answer to the clarification question. We evaluate our model on our two datasets of StackExchange and Amazon, using both automatic metrics and human judgments of usefulness, specificity and relevance, showing that our approach outperforms both a retrieval-based model and ablations that exclude the utility model and the adversarial training. We observe that our question generation model generates questions that range a wide spectrum of specificity to the given context. We argue that generating questions at a desired level of specificity (to a given context) can be useful in many scenarios. In our last line of research we, therefore, build a question generation model which given a context and a level of specificity (generic or specific), generates a question at that level of specificity. We hypothesize that by providing the level of specificity of the question to our model during training time, it can learn patterns in the question that indicate the level of specificity and use those to generate questions at a desired level of specificity. To automatically label the large number of questions in our training data with the level of specificity, we train a binary classifier which given a context and a question, predicts whether the question is specific (to the context) or generic. We demonstrate the effectiveness of our specificity-controlled question generation model by evaluating it on the Amazon dataset using human judgements

What children want to know about their world.

Thesis (Ed.M.)--Boston Universit

Children of Mexican immigrants: Negotiating school in a two -way setting on the Texas -Mexico border

This problem under investigation was the schooling process of children of Mexican immigrants in an American school. The participants for the study included the 56 faculty members and 17 children of Mexican immigrants in school implementing a Two-Way Immersion Program. An ethnographic-like qualitative method was used to investigate the problem. Data was gathered through field immersion, participant observations, and unstructured interviews. Findings revealed diverse characteristics of the children of Mexican immigrants. The school\u27s ethos of reception included numerous attributes that served to accommodate the children and their parents. The practices in the Two-Way Program facilitated the adaptation of the children through the use of Vygotskian-based practices. The conclusion was that the particular program served the children of Mexican immigrants well. The implications were that changes in our educational system can be implemented to better serve all students

The effectiveness of three types of practice in word recognition in grades II and III

Thesis (Ed.M.)--Boston Universit

An Investigation of Student Interaction Patterns and Teacher Feedback at a Saudi EFL University Context

The current study is guided by the assumption that classroom interaction plays a key role in enhancing the quality of learning and teaching in a classroom setting. In an EFL context as this study concerns classroom interaction becomes more essential as it cannot merely increase the opportunities for learning the language but also allow students to practise using the target language by participating in classroom activities and interacting with both their teachers and peers. To date, there have been many research studies conducted for the purpose of fostering student communication and interaction in language learning contexts. The current study aimed at investigating different patterns of classroom interaction take place in a particular English classrooms context. The IRF: Initiation, Response, and Feedback patterns of classroom discourse investigated in this study are one of the most common structures of classroom interaction. The study conducted an exploratory study using two qualitative methods (i.e. observation and interviews) to answer two main research questions. Particularly, how EFL teachers use the third feedback turn of interaction whether for evaluation feedback and then closure of the cycle of interaction at this level, or follow-up feedback to maintain the flow of interaction. The data of the study identified five functions of the feedback the teachers employed in the classrooms observed. It is found that the teachers use the feedback turns: to initiate new questions; to make the discourse more communicative; to promote student engagement and contributions; and lastly to provide an embedded and explicit evaluation. In addition, the study investigated the teachers’ perspectives of, and insights into, the functions of the feedback they provide. It is found that the teachers provided four different ways of scaffolding to extend student participation and communication. Finally, some contributions, implications for the context and recommendations are provided as well as some suggestions for improving classroom discourse in light of future consideration

An analysis of the impact of a transformative action reflection inset model on teachers' understanding and classroom behaviour

Includes bibliography.This dissertation tests the core assumptions of a particular model for INSET (teacher in-service education and training). The study uses as an illustrative case study an INSET programme for junior primary teachers which self-consciously aligns itself with the assumptions underpinning the transformative action reflection model. The assumption of this model is that it is the impact of Courses on teachers' understanding and classroom behaviour of the model both in terms of technical practice and in terms of teachers' ability to employ appropriate practices which will bring about improvement in the quality of teaching and learning in classrooms. The enquiry entailed operationalising measures through which the core assumptions of the model could be tested. In particular the research entailed measuring whether an INSET course based on this model impacts on 1. a) teachers' understanding of a model for teaching; b) teachers' practice of the model in the classroom; and 2. assessing whether the impact can be judged as improvement in teaching quality. Instruments to measure the impact of the course on teachers' understanding and practice of new pedagogies have been constructed on the basis of explicit criteria drawn from the objectives of two Courses from the particular INSET programme used for the study. Qualitative and quantitative data are used to measure the impact of the two Courses on teachers' understanding and practice of the model. Assessing whether the impact can be said to be improvement in the quality of teaching involved using two independent experts in the field of junior primary teacher training. The craft experts used specially constructed schedules to observe videos of the lessons of a mixed sample of teachers who had attended the INSET course and judge the appropriateness of teachers' practices within specific contexts. Data from the study reveals reasonable evidence to support the assumption that, in terms of its objectives, the claims of the INSET model appear to be valid. The appropriateness of the classroom behaviour of those teachers who according to the study have demonstrated evidence of adequate understanding and practice or mastery of the model was singled out by the craft experts. However, data from the study also reveals that overall only a small band of teachers demonstrate adequate understanding and practice of the model and that, in spite of a quality intervention based on the INSET model, the focus of the teaching of most teachers in the sample selected is on teaching content and vocabulary rather than on teaching concepts, skills and strategies

Elementary Statistics Instructors\u27 Uptake of the Teaching Practices Recommended by the American Statistical Association

Previous studies point out there has been a wide gap, in the teaching of mathematics, between the practices recommended in the credential programs and the practices teachers actually do. The purpose of this study is to explore how consistent the teaching practices of introductory statistics instructors are to the recommendations endorsed by the American Statistical Association, to understand what their attitudes to the gap are, and to identify the factors that prevent the instructors from implementing the recommended practices. Data were collected from nine statistics instructors at a state university through survey, classroom observations, and interviews. Findings indicate that the disjuncture between recommended and actual practices is wide for some instructors and suggest that various factors such as teaching experience, institutional support, instructors’ beliefs on the recommended guidelines, and their eagerness to adopt new practices play an important role in instructors’ implementation of suggested practices

An evaluation of the effectiveness of three types of study guides in producing immediate and delayed recall.

Thesis (Ed.M.)--Boston Universit

A Survey to Determine Teacher Attitudes and Treatment of Secondary Business Law

The purpose of this study was to determine teacher attitudes, and the treatment of business law as a subject included in instruction on the secondary level. Specific questions dealing with attitudes and perceptions of business law were asked of the participating teachers. Questions were also asked concerning the content currently being taught. More specifically, there were two categories of questions - those aimed at teachers\u27 personal philosophies regarding business law, and those concerned with the mechanics (scope and methodology) used in the business law classes