Learning Character-level Compositionality with Visual Features

Previous work has modeled the compositionality of words by creating character-level models of meaning, reducing problems of sparsity for rare words. However, in many writing systems compositionality has an effect even on the character-level: the meaning of a character is derived by the sum of its parts. In this paper, we model this effect by creating embeddings for characters based on their visual characteristics, creating an image for the character and running it through a convolutional neural network to produce a visual character embedding. Experiments on a text classification task demonstrate that such model allows for better processing of instances with rare characters in languages such as Chinese, Japanese, and Korean. Additionally, qualitative analyses demonstrate that our proposed model learns to focus on the parts of characters that carry semantic content, resulting in embeddings that are coherent in visual space.Comment: Accepted to ACL 201

サブ文字レベルの情報を使用したニューラル機械翻訳

東京都立大

Domain adaptation for neural machine translation

The development of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and training time. However, such translation models struggle when translating text of a specific domain. A domain may consist of text on a well-defined topic, or text of unknown provenance with an identifiable vocabulary distribution, or language with some other stylometric feature. While NMT models can achieve good translation performance on domain-specific data via simple tuning on a representative training corpus, such data-centric approaches have negative side-effects. These include over-fitting, brittleness, and `catastrophic forgetting' of previous training examples. In this thesis we instead explore more robust approaches to domain adaptation for NMT. We consider the case where a system is adapted to a specified domain of interest, but may also need to accommodate new language, or domain-mismatched sentences. We explore techniques relating to data selection and curriculum, model parameter adaptation procedure, and inference procedure. We show that iterative fine-tuning can achieve strong performance over multiple related domains, and that Elastic Weight Consolidation can be used to mitigate catastrophic forgetting in NMT domain adaptation across multiple sequential domains. We develop a robust variant of Minimum Risk Training which allows more beneficial use of small, highly domain-specific tuning sets than simple cross-entropy fine-tuning, and can mitigate exposure bias resulting from domain over-fitting. We extend Bayesian Interpolation inference schemes to Neural Machine Translation, allowing adaptive weighting of NMT ensembles to translate text from an unknown domain. Finally we demonstrate the benefit of multi-domain adaptation approaches for other lines of NMT research. We show that NMT systems using multiple forms of data representation can benefit from multi-domain inference approaches. We also demonstrate a series of domain adaptation approaches to mitigating the effects of gender bias in machine translation

An Efficient Method for Generating Synthetic Data for Low-Resource Machine Translation – An empirical study of Chinese, Japanese to Vietnamese Neural Machine Translation

Data sparsity is one of the challenges for low-resource language pairs in Neural Machine Translation (NMT). Previous works have presented different approaches for data augmentation, but they mostly require additional resources and obtain low-quality dummy data in the low-resource issue. This paper proposes a simple and effective novel for generating synthetic bilingual data without using external resources as in previous approaches. Moreover, some works recently have shown that multilingual translation or transfer learning can boost the translation quality in low-resource situations. However, for logographic languages such as Chinese or Japanese, this approach is still limited due to the differences in translation units in the vocabularies. Although Japanese texts contain Kanji characters that are derived from Chinese characters, and they are quite homologous in sharp and meaning, the word orders in the sentences of these languages have a big divergence. Our study will investigate these impacts in machine translation. In addition, a combined pre-trained model is also leveraged to demonstrate the efficacy of translation tasks in the more high-resource scenario. Our experiments present performance improvements up to +6.2 and +7.8 BLEU scores over bilingual baseline systems on two low-resource translation tasks from Chinese to Vietnamese and Japanese to Vietnamese