Statistical Machine Translation Features with Multitask Tensor Networks

We present a three-pronged approach to improving Statistical Machine Translation (SMT), building on recent success in the application of neural networks to SMT. First, we propose new features based on neural networks to model various non-local translation phenomena. Second, we augment the architecture of the neural network with tensor layers that capture important higher-order interaction among the network units. Third, we apply multitask learning to estimate the neural network parameters jointly. Each of our proposed methods results in significant improvements that are complementary. The overall improvement is +2.7 and +1.8 BLEU points for Arabic-English and Chinese-English translation over a state-of-the-art system that already includes neural network features.Comment: 11 pages (9 content + 2 references), 2 figures, accepted to ACL 2015 as a long pape

Distributed Representations of Sentences and Documents

Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite their popularity, bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. For example, "powerful," "strong" and "Paris" are equally distant. In this paper, we propose Paragraph Vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents. Our algorithm represents each document by a dense vector which is trained to predict words in the document. Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models. Empirical results show that Paragraph Vectors outperform bag-of-words models as well as other techniques for text representations. Finally, we achieve new state-of-the-art results on several text classification and sentiment analysis tasks

문맥 인식기반의 문서 단위 신경망 기계 번역 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022.2. 정교민.The neural machine translation (NMT) has attracted great attention in recent years, as it has yielded state-of-the-art translation quality. Despite of their promising results, many current NMT systems are sentence-level; translating each sentence independently. This ignores contexts on text thus producing inadequate and inconsistent translations at the document-level. To overcome the shortcomings, the context-aware NMT (CNMT) has been proposed that takes contextual sentences as input. This dissertation proposes novel methods for improving the CNMT system and an application of CNMT. We first tackle the efficient modeling of multiple contextual sentences on CNMT encoder. For this purpose, we propose a hierarchical context encoder that encodes contextual sentences from token-level to sentence-level. This novel architecture enables the model to achieve state-of-the-art performance on translation quality while taking less computation time on training and translation than existing methods. Secondly, we investigate the training method for CNMT models, where most models rely on negative log-likelihood (NLL) that do not fully exploit contextual dependencies. To overcome the insufficiency, we introduce coreference-based contrastive learning for CNMT that generates contrastive examples from coreference chains between the source and target sentences. The proposed method improves pronoun resolution accuracy of CNMT models, as well as overall translation quality. Finally, we investigate an application of CNMT on dealing with Korean honorifics which depends on contextual information for generating adequate translations. For the English-Korean translation task, we propose to use CNMT models that capture crucial contextual information on the English source document and adopt a context-aware post-editing system for exploiting contexts on Korean target sentences, resulting in more consistent Korean honorific translations.신경망 기계번역 기법은 최근 번역 품질에 있어서 큰 성능 향상을 이룩하여 많은 주목을 받고 있다. 그럼에도 불구하고 현재 대부분의 신경망 번역 시스템은 텍스트를 독립된 문장 단위로 번역을 수행하기 때문에 텍스트에 존재하는 문맥을 무시하고 결국 문서 단위로 파악했을 때 적절하지 않은 번역문을 생성할 수 있는 단점이 있다. 이를 극복하기 위해 주변 문장을 동시에 고려하는 문맥 인식 기반 신경망 번역 기법이 제안되고 있다. 본 학위 논문은 문맥 인식 기반 신경망 번역 시스템의 성능을 개선시킬 수 있는 기법들과 문맥 인식 기반 신경망 번역 기법의 활용 방안을 제시한다. 먼저 여러 개의 문맥 문장들을 효과적으로 모델링하기 위해 문맥 문장들을 토큰 레벨 및 문장 레벨로 단계적으로 표현하는 계층적 문맥 인코더를 제시하였다. 제시된 모델은 기존 모델들과 비교하여 가장 좋은 번역 품질을 얻으면서 동시에 학습 및 번역에 걸리는 연산 시간을 단축하였다. 두 번째로는 문맥 인식 기반 신경망 번역모델의 학습 방법을 개선하고자 하였는데 이는 기존 연구에서는 문맥에 대한 의존 관계를 전부 활용하지 못하는 전통적인 음의 로그우도 손실함수에 의존하고 있기 때문이다. 이를 보완하기 위해 문맥 인식 기반 신경망 번역모델을 위한 상호참조에 기반한 대조학습 기법을 제시한다. 제시된 기법은 원문과 주변 문맥 문장들 사이에 존재하는 상호참조 사슬을 활용하여 대조 사례를 생성하며, 문맥 인식 기반 신경망 번역 모델들의 전반적인 번역 품질 뿐만 아니라 대명사 해결 성능도 크게 향상시켰다. 마지막으로는 맥락 정보가 필요한 한국어 경어체 번역에 있어서 문맥 인식 기반 신경망 번역 기법의 활용 방안에 대해서도 연구하였다. 이에 영어-한국어 번역 문제에 문맥 인식 기반 신경망 번역 기법을 적용하여 영어 원문에서 필수적인 맥락 정보를 추출하는 한편 한국어 번역문에서도 문맥 인식 사후편집 시스템을 활용하여 보다 일관된 한국어 경어체 표현을 번역하도록 개선하는 기법을 제시하였다.Abstract i Contents ii List of Tables vi List of Figures viii 1 Introduction 1 2 Background: Neural Machine Translation 7 2.1 A Brief History 7 2.2 Problem Setup 9 2.3 Encoder-Decoder architectures 10 2.3.1 RNN-based Architecture 11 2.3.2 SAN-based Architecture 13 2.4 Training 16 2.5 Decoding 16 2.6 Evaluation 17 3 Efficient Hierarchical Architecture for Modeling Contextual Sentences 18 3.1 Related works 20 3.1.1 Modeling Context in NMT 20 3.1.2 Hierarchical Context Modeling 21 3.1.3 Evaluation of Context-aware NMT 21 3.2 Model description 22 3.2.1 Context-aware NMT encoders 22 3.2.2 Hierarchical context encoder 27 3.3 Data 28 3.3.1 English-German IWSLT 2017 corpus 29 3.3.2 OpenSubtitles corpus 29 3.3.3 English-Korean subtitle corpus 31 3.4 Experiments 31 3.4.1 Hyperparameters and Training details 31 3.4.2 Overall BLEU evaluation 32 3.4.3 Model complexity analysis 32 3.4.4 BLEU evaluation on helpful/unhelpful context 34 3.4.5 EnKo pronoun resolution test suite 35 3.4.6 Qualitative Analysis 37 3.5 Summary of Efficient Hierarchical Architecture for Modeling Contextual Sentences 43 4 Contrastive Learning for Context-aware Neural Machine Translation 44 4.1 Related Works 46 4.1.1 Context-aware NMT Architectures 46 4.1.2 Coreference and NMT 47 4.1.3 Data augmentation for NMT 47 4.1.4 Contrastive Learning 47 4.2 Context-aware NMT models 48 4.3 Our Method: CorefCL 50 4.3.1 Data Augmentation Using Coreference 50 4.3.2 Contrastive Learning for Context-aware NMT 52 4.4 Experiments 53 4.4.1 Datasets 53 4.4.2 Settings 54 4.4.3 Overall BLEU Evaluation 55 4.4.4 Results on English-German Contrastive Evaluation Set 57 4.4.5 Analysis 58 4.5 Summary of Contrastive Learning for Context-aware Neural Machine Translation 59 5 Improving English-Korean Honorific Translation Using Contextual Information 60 5.1 Related Works 63 5.1.1 Neural Machine Translation dealing with Korean 63 5.1.2 Controlling the Styles in NMT 63 5.1.3 Context-Aware NMT Framework and Application 64 5.2 Addressing Korean Honorifics in Context 65 5.2.1 Overview of Korean Honorifics System 65 5.2.2 The Role of Context on Choosing Honorifics 68 5.3 Context-Aware NMT Frameworks 69 5.3.1 NMT Model with Contextual Encoders 71 5.3.2 Context-Aware Post Editing (CAPE) 71 5.4 Our Proposed Method - Context-Aware NMT for Korean Honorifics 73 5.4.1 Using CNMT methods for Honorific-Aware Translation 74 5.4.2 Scope of Honorific Expressions 75 5.4.3 Automatic Honorific Labeling 76 5.5 Experiments 77 5.5.1 Dataset and Preprocessing 77 5.5.2 Model Implementation and Training Details 80 5.5.3 Metrics 80 5.5.4 Results 81 5.5.5 Translation Examples and Analysis 86 5.6 Summary of Improving English-Korean Honorific Translation Using Contextual Information 89 6 Future Directions 91 6.1 Document-level Datasets 91 6.2 Document-level Evaluation 92 6.3 Bias and Fairness of Document-level NMT 93 6.4 Towards Practical Applications 94 7 Conclusions 96 Abstract (In Korean) 117 Acknowledgment 119박

From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc