Unsupervised generation of parallel treebanks through sub-tree alignment

The need for syntactically annotated data for use in natural language processing has increased dramatically in recent years. This is true especially for parallel treebanks, of which very few exist. The ones that exist are mainly hand-crafted and too small for reliable use in data-oriented applications. In this paper we introduce an open-source system for fast and robust automatic generation of parallel treebanks. We expect the opening of the presented platform to the scientific community to help boost research in the field of data-oriented machine translation and lead to advancements in other fields where parallel treebanks can be employed

Matching Natural Language Sentences with Hierarchical Sentence Factorization

Semantic matching of natural language sentences or identifying the relationship between two sentences is a core research problem underlying many natural language tasks. Depending on whether training data is available, prior research has proposed both unsupervised distance-based schemes and supervised deep learning schemes for sentence matching. However, previous approaches either omit or fail to fully utilize the ordered, hierarchical, and flexible structures of language objects, as well as the interactions between them. In this paper, we propose Hierarchical Sentence Factorization---a technique to factorize a sentence into a hierarchical representation, with the components at each different scale reordered into a "predicate-argument" form. The proposed sentence factorization technique leads to the invention of: 1) a new unsupervised distance metric which calculates the semantic distance between a pair of text snippets by solving a penalized optimal transport problem while preserving the logical relationship of words in the reordered sentences, and 2) new multi-scale deep learning models for supervised semantic training, based on factorized sentence hierarchies. We apply our techniques to text-pair similarity estimation and text-pair relationship classification tasks, based on multiple datasets such as STSbenchmark, the Microsoft Research paraphrase identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments show that the proposed hierarchical sentence factorization can be used to significantly improve the performance of existing unsupervised distance-based metrics as well as multiple supervised deep learning models based on the convolutional neural network (CNN) and long short-term memory (LSTM).Comment: Accepted by WWW 2018, 10 page

Highlighting matched and mismatched segments in translation memory output through sub-­tree alignment

In recent years, it is becoming more and more clear that the localisation industry does not have the necessary manpower to satisfy the increasing demand for high-quality translation. This has fuelled the search new and existing technologies that would increase translator throughput. As Translation Memory (TM) systems are the most commonly employed tool by translators, a number of enhancements are available to assist them in their job. One such enhancement would be to show the translator which parts of the sentence that needs to be translated match which parts of the fuzzy match suggested by the TM. For this information to be used, however, the translators have to carry it over to the TM translation themselves. In this paper, we present a novel methodology that can automatically detect and highlight the segments that need to be modified in a TM-­suggested translation. We base it on state-­of-the-art sub-­tree align- ment technology (Zhechev,2010) that can produce aligned phrase-­based-­tree pairs from unannotated data. Our system operates in a three-­step process. First, the fuzzy match selected by the TM and its translation are aligned. This lets us know which segments of the source-­language sentence correspond to which segments in its translation. In the second step, the fuzzy match is aligned to the input sentence that is currently being translated. This tells us which parts of the input sentence are available in the fuzzy match and which still need to be translated. In the third step, the fuzzy match is used as an intermediary, through which the alignments between the input sentence and the TM translation are established. In this way, we can detect with precision the segments in the suggested translation that the translator needs to edit and highlight them appropriately to set them apart from the segments that are already good translations for parts of the input sentence. Additionally, we can show the alignments—as detected by our system—between the input and the translation, which will make it even easier for the translator to post-edit the TM suggestion. This alignment information can additionally be used to pre- translate the mismatched segments, further reducing the post-­editing load

DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images

The domain adaptation of satellite images has recently gained an increasing attention to overcome the limited generalization abilities of machine learning models when segmenting large-scale satellite images. Most of the existing approaches seek for adapting the model from one domain to another. However, such single-source and single-target setting prevents the methods from being scalable solutions, since nowadays multiple source and target domains having different data distributions are usually available. Besides, the continuous proliferation of satellite images necessitates the classifiers to adapt to continuously increasing data. We propose a novel approach, coined DAugNet, for unsupervised, multi-source, multi-target, and life-long domain adaptation of satellite images. It consists of a classifier and a data augmentor. The data augmentor, which is a shallow network, is able to perform style transfer between multiple satellite images in an unsupervised manner, even when new data are added over the time. In each training iteration, it provides the classifier with diversified data, which makes the classifier robust to large data distribution difference between the domains. Our extensive experiments prove that DAugNet significantly better generalizes to new geographic locations than the existing approaches

Seeding statistical machine translation with translation memory output through tree-based structural alignment

With the steadily increasing demand for high-quality translation, the localisation industry is constantly searching for technologies that would increase translator throughput, with the current focus on the use of high-quality Statistical Machine Translation (SMT) as a supplement to the established Translation Memory (TM) technology. In this paper we present a novel modular approach that utilises state-of-the-art sub-tree alignment to pick out pre-translated segments from a TM match and seed with them an SMT system to produce a final translation. We show that the presented system can outperform pure SMT when a good TM match is found. It can also be used in a Computer-Aided Translation (CAT) environment to present almost perfect translations to the human user with markup highlighting the segments of the translation that need to be checked manually for correctness