dataesr/scientific-paragraphs-categorization huggingface dataset:
For each language pair, the baseline system is trained on a mixture of 10 million parallel sentences randomly taken from all general-domain sources, outlined in Section 2. 2. 3, and 30 million monolingual sentences sampled from all general-domain monolingual sources, outlined in Section 2. 2. 5, and tuned on 3,003 sentence pairs of general-domain (gen) development data, as described in Section 2. 2. 7. These configurations of parallel and monolingual training data are referred to as P0 and M0, respectively. The performance of the resulting systems measured on various test sets is presented in Table  4. The BLEU scores for the gen domain range from 24. 13 to 29. 62 depending on the language pair. Such scores are comparable to the state-of-the-art results reported recently on the same data sets  [91].
In our experiments, we apply this technique to select both monolingual data for language models and parallel data for translation models. Selection of parallel data is based on the target language (English) only -so we only need two scoring models for all experiments (both English): the in-domain one is trained on the HON data set and the general-domain one on the WMT News data (the resources are described in Section 2. 2. 4). Compared to the approach of Moore and Lewis  [24]  and Axelrod et al. [26] , we prune the model vocabulary more aggressively -we discard not only the singletons, but also all words with non-Latin characters, which helps clean the models from noise introduced by the automatic process of data acquisition by web crawling. Parallel data selection. For parallel data selection, we experiment with three configurations which differ in the proportion of in-domain and pseudo-in-domain material in our training data, always summing up to 10 million sentence pairs so the training data size is comparable to other experiments.
The most efficient configuration is the intelligent selection from all sources (M4). This approach brings an additional 5 BLEU points for CS-EN and 3 BLEU points for DE-EN. In the case of FR-EN, we observe a slight degradation, but the difference is not statistically significant. The distribution of the monolingual data sources in the individual configurations is illustrated in Table  10. It is evident that the general-domain MultiUN corpus is more relevant to the medical domain than WMT News and Gigaword (compare M0 and M1), and the distribution of M2 and M3 does not differ a lot. Unsurprisingly, the best-performing configuration (M4) contains most material from the HON data and other indomain sources, but the general-domain corpora are present too and their contribution to translation quality is quite substantial (compare M3 and M4 in Table  9 , especially for CS-EN and DE-EN).
We investigate whether morphological normalization performed on the source language side improves translation quality in our specific domain of medical queries. We take    The splitting models C3 and C6 are trained on the same parallel data as P3 and P6, respectively. OOV refers to the absolute and relative (%) number of out-ofvocabulary words, and splits refers to the number of splits (in 1,951 words). the P6 M4 configuration as a baseline and apply three stemming methods on the source language side of the parallel training data: N1 trimming words to 5 characters (used also for word alignment, Section 2. 3), N2 realized by Snowball stemmer  [95] , and N3 based on lemmatization by the Treex NLP framework  [74]. As shown in Table  12 , the results are not affirmative. None of the three methods leads to better translation quality in any translation direction.
Splitting German compound words In Section 2. 1. 5, we outlined the issue of German compounds, which increases the vocabulary size and leads to out-of-vocabulary problems in MT (especially in specific domains with rich terminology, such as medicine). The technique proposed to reduce the problem is based on splitting the compounds into components which are then treated as regular (invocabulary) words. In this work, we employ a simple unsupervised frequencybased method for splitting compound words introduced by Koehn and Knight  [45]  and implemented in the script compound-splitter. perl as a part of the Moses toolkit  [4]. This method is easy to use and does not require any annotated data. For a given word, it finds a split S = p 1 ,. , p n with the highest geometric mean of word frequencies of its parts p i : S = arg max S         p i ∈S count(p i )         1 n , where count(p i ) indicates the number of times the potential part of the compound p i occurs as a single token in the corpus and n is a number of parts in the particular split S.
Exploiting synonyms as translation variants As highlighted previously, one of the issues that MT of texts from professional domains has to deal with is the presence of domain-specific terms which are rare or absent in the training data and can lead to out-of-vocabulary words. In our work, we try to address the issue by introducing the notion of synonyms, mined from structured data, as several researchers have already shown this approach to be useful (see Section 2. 1. 6). While we often lack bilingual pairs of some terms, we may still be able to find synonymous or nearly-synonymous terms for which we do have bilingual pairs in our data. We explore two sources of synonymy information -the UMLS Metathesaurus and DBpedia described in Section 2. 2. 1. Both of the data sources can be viewed as providing a mapping from various synonymous terms to one canonical term.
Manual inspection of the translation outputs on the development data set shows that in many cases, the target term is substituted by its synonym or near-synonym, which is most probably caused by the fact that there are far more synonyms for English than for other languages in our data, as our source data sets are much larger for English. In some cases, this can be regarded as canonicalization, while in other cases, it is probably closer to term expansion. Unfortunately, we have observed very few cases where our approach led to avoidance of the outof-vocabulary words -their frequency decreased from approximately 5% to approximately 4. 6%. Still, we believe that exploiting synonyms from UMLS and DBpedia has a potential to improve the performance of our cross-lingual IR system.
Overview of main translation results So far, we have focused on optimizing the translation quality of medical queries. The main achievements are summarized in Table  15 , which compares performance to two freely available MT systems on the web: Google Translate 8  and Microsoft Bing Translator  9. The general-domain baselines trained on 10 million parallel sentence pairs and 30 million monolingual sentences and tuned on general-domain data have been improved greatly for all language pairs. The improvement comes incrementally from in-domain tuning, careful selection of parallel and monolingual training data, and word decompounding (for DE-EN). In terms of BLEU, we have added a total of 14. 86 points for CS-EN, 17. 62 points for DE-EN, and 11. 83 points for FR-EN. All these results are statistically significant (p < 0.
The first such evaluation campaign using medical data for evaluation of IR was OHSUMED  [100]. The test collection contained around 350,000 abstracts from medical journals taken from the MEDLINE database over a period of five years and two sets of topics: a manually created one and another one based on the controlled vocabulary thesaurus of the Medical Subject Headings (MeSH)  [68]. The TREC Genomics Track ran between 2003 and 2007. The test collection comprised publications from medical journals and clinical reports related to genes and genomics. The track included tasks ranging from ad-hoc retrieval to document categorization, passage retrieval, and entity-based question-answering  [101]. More recently, the TREC Medical Records Track ran in 2011 and 2012  [102]. This track was based on a collection of anonymized medical records and queries that resembled eligibility criteria of clinical studies.
The queries in the collection aim to model those used by laypeople (i. e. , patients, their relatives, or other representatives) to find out more about their disorders in a specific situation, after they have examined their discharge summary. The discharge summaries used for the task originate from the anonymized clinical free-text notes of the MIMIC II database, version 2. 5  16. The queries are intended to be representative of real patients' information needs and statements, and as such are relatively short with average length of no more than two terms (words). The generated queries consist of a topic title (text of the query), description (longer description of what the query means), and narrative (expected content of the relevant documents). The query set (topic titles) has been manually translated for the purpose of this work by medical professionals into German, French, and Czech (and double reviewed).
05. There were no BLEU or PER results significantly worse than the baseline. The best scores for each language pair are again indicated by the symbol. The 95% confidence intervals for BLEU observed on the IR test sets are much larger (21-29 points) than in the case of the MT test sets (7-12 points). This is not surprising because the IR test sets contain just 50 queries compared to the 1,000 queries in the MT test sets and the sample variance is much higher. Therefore, the results presented in  the IR test sets were created in a more controlled setting. In addition to the automatic evaluation using BLEU and PER, we also conduct a human evaluation (column HUM in Table  18 ) using the same method as described in Section 2. 5 but performed on the entire IR test sets. The overall translation results on the IR test sets do not confirm the results from Section 2 (Table  15 ) achieved on the MT test sets, where our best system significantly improved baselines for all language pairs and even outperformed Google Translate and Microsoft Bing Translator.
Acknowledgments This work was supported by the  EU   FP7  project  Khresmoi  (contract no. 257528 ), the  Czech Science Foundation  (grant no. P103/12/G084 ), the  Science Foundation Ireland  (grant no. 07/CE/I1142 ) as part of the  Centre for Next Generation Localisation at Dublin City University , and by the  ESF  project  ELIAS. The work described herein uses language resources hosted by the  LINDAT/CLARIN  repository  20  , funded by the project  LM2010013  of the MEYS of the Czech Republic.
Context Many urban or environmental models are defined with the objective of helping practitioners and stakeholders in their decision-making processes. Models which represent in three dimensions the geometric elements of a city are called 3D city models. These models are increasingly used in different cities and countries for an intended wide range of applications beyond mere visualisation. Indeed, unlike maps and drawings, 3D visualisation reflects a view of the environment that a lot of people are familiar with and acts as a communication tool. Non geometrical data (such as social, historic or acoustic data or statistics, for example) can be associated to urban geometrical objects (such as buildings or streets) that can be represented and visualized in 3D, leading to a localization as well as a better understanding of these data.
Semantics as opposed to geometry and images: data oriented semantics Very early, geographical data producers used the term semantics to refer to part of geographical data that were not geometric, and that were related to thematic properties of features and their attributes, such as the "number of lanes" for a road. In many virtual models, there are two kinds of data: coordinates and textures. Enriching such data with semantics can thus be interpreted as making explicit, at the level of data, features as well as their nature and properties. This is what is proposed in CityGML model  (Kolbe et al. , 2005). The authors use semantics to denote properties differently from spatial ones. They proposed to integrate into CityGML semantic elements relevant to disaster management using a taxonomy of classes that distinguishes between buildings, vegetation objects, water bodies, and transportation facilities such as streets and railways.
Semantics as a formal connection with reality (nature oriented semantics) Another basic definition of semantics proposed by  Kavouras et al. (2003)  is given by the relationship between data and reality. Many works analyse how to represent this information, essentially in order to integrate heterogeneous data or in order to facilitate dataset discovery in catalogues. The underlying idea is that data integration is necessary to cope with various kinds of heterogeneities related to design choices made by producers when they define their geospatial data. These various kinds can be classified into system, syntax and structure, and semantic heterogeneities  (Visser et al. , 2005; Bittner et al. , 2005). System refers to hardware, operating systems, and communications heterogeneity, such as communication protocols.
Syntax and structure interoperability covers data representation, formatting, data models, and spatial schemas heterogeneity. Semantic interoperability reflects the meanings of objects. Through the use of new architectures and CityGML, most heterogeneities of the first group are solved; however semantic interoperability is the most challenging problem. With the development of Spatial Information Infrastructures (SII) the semantic interoperability is becoming a critical issue. Ontology is seen as a very promising approach for dealing with semantic interoperability. Besides, formalising our conceptualisation of the real world it is necessary to relate it to the digital city model. Most approaches consist in associating a real world feature type (also known as a category in an ontology) to a database feature type  (Klein et al.
, 2006). Mustiere et al. (2003)  and  Abadie (2009)  have proposed a more precise representation of the relationship between a geographical database representation construct and a category of the real world. Their model tracks every choice made by the data producer to create the database structure and to populate it from his observation of the real world -such as, the decision to decompose the representation of a road into road segments, or the decision not to put into the databases dead ends that are less than 20 m long. The formal connection with reality can also be required at the level of instances. Digital city models should support the unambiguous designation of objects and places in a way that is both machine and human readable. This is typically useful for firefighters, civilians, or manages to communicate relevant information about the scene by phone.
Street names and addresses are the common human readable elements of the reference system for urban objects. But it might be useful to have other ones (for instance, if a street name plate is destroyed or happens to be too far to be read). In most works, it is suggested to identify landmarks that can be used to reference things in the landscape. An interesting proposal is that of Morita's real scale map who defined objects in the real world (such as, a fire light) that, together with relationships such as "5 steps from", compose a fully functional reference system  (Morita, 2007). An alternative way to solve this issue is to design identifiers for objects in the real world. This approach is investigated in the context of collaborative data and in the Semantic Web where people want to attach descriptors to a resource identified with a URL.
3D city models have a lot to bring to assessing this kind of semantics. In the context of fire disasters, affordances can be analysed in the perspective of several processes: the fire (example of affordances are, objects that burn well), escaping from the fire (examples of affordances are, objects that can be a good shelter waiting for the rescue team), fighting the fire and rescuing people (example of affordances are, water body and pumps to extinguish the fire). In here, we think it is most relevant to distinguish between: • The information the user needs to take a decision (e. g. firefighters need to know if they can access specific doors and how close they can drive to them). • The data needed to calculate this information. At this point, it is important to distinguish between different kinds of city models (the different LODs of 3D models, 2.
5D and 2D). Classical 3D virtual models, comprising a digital elevation model (DEM) and realistic textures are very useful for user visualisation. Indeed, a great difference between virtual globes and maps is that the virtual globe is closer to the user experience of the real world than the map. Those data can be provided by data producers or can be simulated if a specific landscape or atmosphere is required. Besides this, terrain information objects are necessary to perform some analysis as explained by  Kolbe et al. (2005)  when they present CityGML relevance for disaster management. With respect to the fire-fighting simulator introduced in the previous section, the relevant categories of objects are buildings (with floors and indoor details), roads, urban areas, and forest areas have been introduced.
It is important to determine which dimensions are needed for these objects and which levels of details, because this could dramatically affect the number and structure of the categorisation. For example, the classification of buildings could be extended in order to take into account the skyscrapers typology. We distinguish between 2D, 2. 5D, and 3D data. In 2D representation, coordinates are only planimetric. In 2. 5D there is one altitude for each planimetric coordinate (such as, a DEM). In 3D it is possible to describe several points with different altitudes and the same planimetric coordinates (such as, on a wall or when a bridge crosses a river). From an algorithmic point of view, sometimes 2D data is more suitable than 3D. The calculation of solar radiation or wind effect at street level do not require the same accurate representation as the intervisibility problem, used to check if two objects (e.
g. , firefighter and injured people) could see each other inside a building. This combination of 2. 5D and 3D objects, as well as the combination of different levels of data details, leads to interoperability problems. These problems become clearer when different heterogeneous data sources are used at the same time, for example, terrain data, buildings, road networks, and water resources map (such as, hydrants -a very important element in the fire-fighting process). Depending on data resolution and scale, the hydrant location in the virtual world could not be precisely aligned with respect to terrain data, as it would be required in the Virtual Reality Simulator. The solution for these situations is based on the utilisation of multi-resolution data, assuring data quality, alignment, and integration at a specific level.
Aerial images have several advantages over terrestrial images. In fact, besides providing accurate building footprints and roof heights, they can be rectified into orthoprojections that facilitate the merging process of multiple images to cover large geographic areas. Orthoprojections also facilitate fusion with 2D GIS systems, integrating models with rich information databases. The complexity and the quantity of data needed to represent large urban areas make fully automatic techniques highly desirable. Many barriers to fully automated systems exist, however. For example, image noise, lightening conditions, occlusions, and scene complexity make building segmentation (their individual identification in a image) an awkward process. Moreover, 3D reconstruction of complete buildings is difficult when a single building is made of complex substructures.
Aerial images often lack of façade information, resulting in models with no visual realism. Integrating façade data is usually a manual process and requires additional sensor data. Active sensors directly measure the depth of objects, which provide an ideal data set for urban modelling. Sensors can be further divided into two categories: ground based and airborne-based (LiDAR, light detection and ranging). Semiautomatic model construction requires a fair amount of operator intervention and results in painfully slow evolution of wide area models. Automatic LiDAR modelling requires automatic structure segmentation and 3D reconstruction, which is rather complex process because of occlusions between buildings or vegetation against buildings. Merging together ground and aerial image sensors, aerial active sensors, and 2D footprint data from GIS or CAD data can generate more accurate and automatic urban models (hybrid sensor systems).
Building information modelling (BIM) Building Information Modelling (BIM) means the semantic modelling of objects and processes in the field of AEC/FM and CAAD, such as in CityGML, where thematic objects are represented with their 3D spatial properties and interrelationships. Data are typically exchanged using the Industry Foundation Classes (IFC), an ISO standard describing a product model and data exchange format for the built-up environment developed by the International Alliance for Interoperability (IAI). IFC provides a very detailed semantic model for 3D building representations using constructive elements such as beams, walls etc. Such as in GML, IFC geometries are spatial properties of semantic objects. IFC has a very flexible geometry model (CSG, BRep, and Sweep representations), but does not provide support for coordinate reference systems; geo-referencing is not yet possible.
Netherlands 3D pilot The Netherlands has completed a two-phases project named 3D pilot. The pilot was initiated by Dutch Kadaster, Geonovum (the National Spatial Data Infrastructure executive committee in the Netherlands, which develops and manages the geo-standards), The Netherlands's Geodetic Commission (NCG), and the Dutch Ministry of Infrastructure and Environment. The first phase took place between January 2010 and June 2011 and the general purpose of the project was to explore the need and use of 3D city models. The main goal was to establish a uniform approach for acquiring, maintaining, and disseminating 3D geo-information. The first phase was executed with 65 stakeholders in The Netherlands  (Stoter et al. , 2012). The findings of the pilot project were formally expressed in a national 3D standard achieved as a CityGML Application Domain Extension (ADE).
0 a number of topics requiring further attention were identified. For example, the need to understand how the national 3D standard works in practice, e. g. how to preserve the links between the different Levels of Detail (LODs), and how to upgrade 2D LOD to higher LODs. It is also important to make sure that 3D IMGeo data can be read and visualised by CityGML-compliant software. For example, whether software systems are compatible with IMGeo extensions, and, if not, which changes are necessary. Finally, further research is needed concerning the creation and management of CityGML-IMGeo data. Which approaches are most appropriate to generate CityGML-IMGeo data? How should this data be validated and maintained? These issues have been the main focus of the second phase of the 3D Pilot, which took place in October 2012.
The second phase's goals were more resultoriented and aimed at writing best practice documents and instructions by joint efforts of the 3D Pilot community. About 100 organisations participated in the second phase and completed six activities (see www. 3dpilot. nl). The second phase was closed with technical specification for the reconstruction of 3D Ingeo CityGML data (available at: http://www. geonovum. nl/sites/default/files/3D/ toolkit/3DFinalReport_2013_1. 01. pdf). The 3D pilot in The Netherlands has clearly revealed several important aspects of 3D city models. On one hand many specialists need and appreciate the use of 3D city models but either do not have access to such data or are not aware of their availability. On the other hand, producers of 3D data have difficulties to estimate what kind of applications might need their 3D data.