On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference

We propose a process for investigating the extent to which sentence representations arising from neural machine translation (NMT) systems encode distinct semantic phenomena. We use these representations as features to train a natural language inference (NLI) classifier based on datasets recast from existing semantic annotations. In applying this process to a representative NMT system, we find its encoder appears most suited to supporting inferences at the syntax-semantics interface, as compared to anaphora resolution requiring world-knowledge. We conclude with a discussion on the merits and potential deficiencies of the existing process, and how it may be improved and extended as a broader framework for evaluating semantic coverage.Comment: To be presented at NAACL 2018 - 11 page

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT

Semantic Representation and Inference for NLP

Semantic representation and inference is essential for Natural Language Processing (NLP). The state of the art for semantic representation and inference is deep learning, and particularly Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and transformer Self-Attention models. This thesis investigates the use of deep learning for novel semantic representation and inference, and makes contributions in the following three areas: creating training data, improving semantic representations and extending inference learning. In terms of creating training data, we contribute the largest publicly available dataset of real-life factual claims for the purpose of automatic claim verification (MultiFC), and we present a novel inference model composed of multi-scale CNNs with different kernel sizes that learn from external sources to infer fact checking labels. In terms of improving semantic representations, we contribute a novel model that captures non-compositional semantic indicators. By definition, the meaning of a non-compositional phrase cannot be inferred from the individual meanings of its composing words (e.g., hot dog). Motivated by this, we operationalize the compositionality of a phrase contextually by enriching the phrase representation with external word embeddings and knowledge graphs. Finally, in terms of inference learning, we propose a series of novel deep learning architectures that improve inference by using syntactic dependencies, by ensembling role guided attention heads, incorporating gating layers, and concatenating multiple heads in novel and effective ways. This thesis consists of seven publications (five published and two under review).Comment: PhD thesis, the University of Copenhage

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail

The integration of machine translation and translation memory

We design and evaluate several models for integrating Machine Translation (MT) output into a Translation Memory (TM) environment to facilitate the adoption of MT technology in the localization industry. We begin with the integration on the segment level via translation recommendation and translation reranking. Given an input to be translated, our translation recommendation model compares the output from the MT and the TMsystems, and presents the better one to the post-editor. Our translation reranking model combines k-best lists from both systems, and generates a new list according to estimated post-editing effort. We perform both automatic and human evaluation on these models. When measured against the consensus of human judgement, the recommendation model obtains 0.91 precision at 0.93 recall, and the reranking model obtains 0.86 precision at 0.59 recall. The high precision of these models indicates that they can be integrated into TM environments without the risk of deteriorating the quality of the post-editing candidate, and can thereby preserve TM assets and established cost estimation methods associated with TMs. We then explore methods for a deeper integration of translation memory and machine translation on the sub-segment level. We predict whether phrase pairs derived from fuzzy matches could be used to constrain the translation of an input segment. Using a series of novel linguistically-motivated features, our constraints lead both to more consistent translation output, and to improved translation quality, reflected by a 1.2 improvement in BLEU score and a 0.72 reduction in TER score, both of statistical significance (p < 0.01). In sum, we present our work in three aspects: 1) translation recommendation and translation reranking models that can access high quality MT outputs in the TMenvironment, 2) a sub-segment translation memory and machine translation integration model that improves both translation consistency and translation quality, and 3) a human evaluation pipeline to validate the effectiveness of our models with human judgements

CCG-augmented hierarchical phrase-based statistical machine translation

Augmenting Statistical Machine Translation (SMT) systems with syntactic information aims at improving translation quality. Hierarchical Phrase-Based (HPB) SMT takes a step toward incorporating syntax in Phrase-Based (PB) SMT by modelling one aspect of language syntax, namely the hierarchical structure of phrases. Syntax Augmented Machine Translation (SAMT) further incorporates syntactic information extracted using context free phrase structure grammar (CF-PSG) in the HPB SMT model. One of the main challenges facing CF-PSG-based augmentation approaches for SMT systems emerges from the difference in the definition of the constituent in CF-PSG and the ‘phrase’ in SMT systems, which hinders the ability of CF-PSG to express the syntactic function of many SMT phrases. Although the SAMT approach to solving this problem using ‘CCG-like’ operators to combine constituent labels improves syntactic constraint coverage, it significantly increases their sparsity, which restricts translation and negatively affects its quality. In this thesis, we address the problems of sparsity and limited coverage of syntactic constraints facing the CF-PSG-based syntax augmentation approaches for HPB SMT using Combinatory Cateogiral Grammar (CCG). We demonstrate that CCG’s flexible structures and rich syntactic descriptors help to extract richer, more expressive and less sparse syntactic constraints with better coverage than CF-PSG, which enables our CCG-augmented HPB system to outperform the SAMT system. We also try to soften the syntactic constraints imposed by CCG category nonterminal labels by extracting less fine-grained CCG-based labels. We demonstrate that CCG label simplification helps to significantly improve the performance of our CCG category HPB system. Finally, we identify the factors which limit the coverage of the syntactic constraints in our CCG-augmented HPB model. We then try to tackle these factors by extending the definition of the nonterminal label to be composed of a sequence of CCG categories and augmenting the glue grammar with CCG combinatory rules. We demonstrate that our extension approaches help to significantly increase the scope of the syntactic constraints applied in our CCG-augmented HPB model and achieve significant improvements over the HPB SMT baseline