OVERCOMING INTERCULTURAL CHALLENGES IN INTERPRETING: A CASE STUDY ON CHINESE-ENGLISH CONFERENCE INTERPRETING

This exploratory case study examines the role of culture in Chinese-English conference interpreting. Given that there has been a lack of empirical research in understanding the role of culture in conference interpreting through the lens of intercultural communication frameworks, we know relatively little about conference interpreters’ experiences with intercultural communication challenges. This project helps address this research gap by investigating the types of intercultural communication challenges that Chinese-English conference interpreters experience and their strategies in managing those challenges. This study hears the voices of both professionals and postgraduate interpreting students. A total number of 27 participants were recruited for this research. Twenty professional conference interpreter were interviewed and seven interpreting students were organized for a focus group discussion. Grounded theory was used to analyze the participants’ observations and strategies in managing intercultural communication challenges when doing Chinese-English conference interpreting. The data analysis process led to the emergence of two procedural guidelines and one process – Interpreters’ Intercultural Mediation Process. The two procedural guidelines offer guidance for the interpreters to provide the most appropriate and effective service: meet with the clients beforehand and be prepared to offer intercultural insights when consulted. Interpreters are found to follow the Interpreters’ Intercultural Mediation Process to decide when and how to mediate intercultural communication challenges at work. This Process includes four criteria, seven intercultural challenges, and seven coping strategies. This study offers theoretical and applied contributions to our understanding of the role of culture in interpreting. By jointly applying frameworks from intercultural communication and interpreting studies to examine the conference interpreting process, this case study makes great efforts to connect the field of intercultural communication with the field of interpreting studies. This study identifies the types of intercultural differences that would lead to challenges in Chinese-English conference interpreting. It also contributes to the call for a cultural turn in interpreting studies. By learning the two procedural guidelines, conference interpreters can be better prepared for their work. By following the Interpreters’ Intercultural Mediation Process, conference interpreters can better anticipate and manage the intercultural challenges at work. This study also offers guidance on tailoring intercultural communication courses for postgraduate interpreting training programs.

Structured Approaches for Exploring Interpersonal Relationships in Natural Language Text

Human relationships have long been studied by scientists from domains like sociology, psychology, literature, etc. for understanding people's desires, goals, actions and expected behaviors. In this dissertation we study inter-personal relationships as expressed in natural language text. Modeling inter-personal relationships from text finds application in general natural language understanding, as well as real-world domains such as social networks, discussion forums, intelligent virtual agents, etc. We propose that the study of relationships should incorporate not only linguistic cues in text, but also the contexts in which these cues appear. Our investigations, backed by empirical evaluation, support this thesis, and demonstrate that the task benefits from using structured models that incorporate both types of information. We present such structured models to address the task of modeling the nature of relationships between any two given characters from a narrative. To begin with, we assume that relationships are of two types: cooperative and non-cooperative. We first describe an approach to jointly infer relationships between all characters in the narrative, and demonstrate how the task of characterizing the relationship between two characters can benefit from including information about their relationships with other characters in the narrative. We next formulate the relationship-modeling problem as a sequence prediction task to acknowledge the evolving nature of human relationships, and demonstrate the need to model the history of a relationship in predicting its evolution. Thereafter, we present a data-driven method to automatically discover various types of relationships such as familial, romantic, hostile, etc. Like before, we address the task of modeling evolving relationships but don't restrict ourselves to two types of relationships. We also demonstrate the need to incorporate not only local historical but also global context while solving this problem. Lastly, we demonstrate a practical application of modeling inter-personal relationships in the domain of online educational discussion forums. Such forums offer opportunities for its users to interact and form deeper relationships. With this view, we address the task of identifying initiation of such deeper relationships between a student and the instructor. Specifically, we analyze contents of the forums to automatically suggest threads to the instructors that require their intervention. By highlighting scenarios that need direct instructor-student interactions, we alleviate the need for the instructor to manually peruse all threads of the forum and also assist students who have limited avenues for communicating with instructors. We do this by incorporating the discourse structure of the thread through latent variables that abstractly represent contents of individual posts and model the flow of information in the thread. Such latent structured models that incorporate the linguistic cues without losing their context can be helpful in other related natural language understanding tasks as well. We demonstrate this by using the model for a very different task: identifying if a stated desire has been fulfilled by the end of a story.