Metadiscourse repertoire of L1 Mandarin undergraduates writing in English : a cross-contextual, cross-disciplinary study

This article presents a qualitative, comparative study of metadiscourse in the academic writing of two groups of undergraduate students working in two different disciplines. The groups of students were: 1) Native speakers of Mandarin studying in China through the medium of English; 2) Native speakers of Mandarin studying in the UK through the medium of English. For each group of students, we examined writing undertaken in two undergraduate disciplinary courses: Literary Criticism and Translation Studies. Our aim was to extend research into English writing by L1 Mandarin speakers, and to identify patterns of difference and similarity both between educational contexts and between disciplines. Results suggest that patterns of metadiscourse use in our corpus are associated with both disciplinary and contextual factors, but that contextual factors may have a stronger effect than disciplinary factors. For our data, local institutional culture seems to have a noticeable influence on student writers' use of metadiscourse

Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing

We introduce a novel method for multilingual transfer that utilizes deep contextual embeddings, pretrained in an unsupervised fashion. While contextual embeddings have been shown to yield richer representations of meaning compared to their static counterparts, aligning them poses a challenge due to their dynamic nature. To this end, we construct context-independent variants of the original monolingual spaces and utilize their mapping to derive an alignment for the context-dependent spaces. This mapping readily supports processing of a target language, improving transfer by context-aware embeddings. Our experimental results demonstrate the effectiveness of this approach for zero-shot and few-shot learning of dependency parsing. Specifically, our method consistently outperforms the previous state-of-the-art on 6 tested languages, yielding an improvement of 6.8 LAS points on average.Comment: NAACL 201

Context-Aware Self-Attention Networks

Self-attention model have shown its flexibility in parallel computation and the effectiveness on modeling both long- and short-term dependencies. However, it calculates the dependencies between representations without considering the contextual information, which have proven useful for modeling dependencies among neural representations in various natural language tasks. In this work, we focus on improving self-attention networks through capturing the richness of context. To maintain the simplicity and flexibility of the self-attention networks, we propose to contextualize the transformations of the query and key layers, which are used to calculates the relevance between elements. Specifically, we leverage the internal representations that embed both global and deep contexts, thus avoid relying on external resources. Experimental results on WMT14 English-German and WMT17 Chinese-English translation tasks demonstrate the effectiveness and universality of the proposed methods. Furthermore, we conducted extensive analyses to quantity how the context vectors participate in the self-attention model.Comment: AAAI 201

Contextual Parameter Generation for Universal Neural Machine Translation

We propose a simple modification to existing neural machine translation (NMT) models that enables using a single universal model to translate between multiple languages while allowing for language specific parameterization, and that can also be used for domain adaptation. Our approach requires no changes to the model architecture of a standard NMT system, but instead introduces a new component, the contextual parameter generator (CPG), that generates the parameters of the system (e.g., weights in a neural network). This parameter generator accepts source and target language embeddings as input, and generates the parameters for the encoder and the decoder, respectively. The rest of the model remains unchanged and is shared across all languages. We show how this simple modification enables the system to use monolingual data for training and also perform zero-shot translation. We further show it is able to surpass state-of-the-art performance for both the IWSLT-15 and IWSLT-17 datasets and that the learned language embeddings are able to uncover interesting relationships between languages.Comment: Published in the proceedings of Empirical Methods in Natural Language Processing (EMNLP), 201