We present Charles Translator, a machine translation system between Ukrainian and Czech, developed as part of a society-wide effort to mitigate the impact of the Russian-Ukrainian war on individuals and society. The system was developed in the spring of 2022 with the help of many language data providers in order to quickly meet the demand for such a service, which was not available at the time in the required quality. The translator was later implemented as an online web interface and as an Android app with speech input, both featuring Cyrillic-Latin script transliteration. The system translates directly, in comparison to other available systems that use English as a pivot, and thus makes advantage of the typological similarity of the two languages. It uses the block back-translation method which allows for efficient use of monolingual training data. The paper describes the development process including data collection and implementation, evaluation, mentions several use cases and outlines possibilities for further development of the system for educational purposes.

The Parliament of the Czech Republic consists of two chambers: the Chamber of Deputies (Lower House) and the Senate (Upper House). In our work, we focus on agenda and documents that relate to the Chamber of Deputies exclusively. We pay particular attention to stenographic protocols that record the Chamber of Deputies’ meetings. Our overall goal is to (1) compile the protocols into a ParlaCLARIN TEI encoded corpus, (2) make this corpus accessible and searchable in the TEITOK web-based platform, (3) annotate the corpus using the modules available in TEITOK, e.g. detect and recognize named entities, and (4) highlight the annotations in TEITOK. In addition, we add two more goals that we consider innovative: (5) update the corpus every time a new stenographic protocol is published online by the Chambers of Deputies and (6) expose the annotations as the linked open data in order to improve the protocols’ interoperability with other existing linked open data. This paper is devoted to the goals (1) and (5).

Researchers in Natural Language Processing rely on availability of data and software, ideally under open licenses, but little is done to actively encourage it. In fact, the current Copyright framework grants exclusive rights to authors to copy their works, make them available to the public and make derivative works (such as annotated language corpora). Moreover, in the EU databases are protected against unauthorized extraction and re-utilization of their contents. Therefore, proper public licensing plays a crucial role in providing access to research data. A public license is a license that grants certain rights not to one particular user, but to the general public (everybody). Our article presents a tool that we developed and whose purpose is to assist the user in the licensing process. As software and data should be licensed under different licenses, the tool is composed of two separate parts: Data and Software. The underlying logic as well as elements of the graphic interface are presented below.

In this paper, we describe an addition to the corpus query system Kontext that enables to enhance the search using syntactic attributes in addition to the existing features, mainly lemmas and morphological categories. We present the enhancements of the corpus query system itself, the attributes we use to represent syntactic structures in data, and some examples of querying the syntactically annotated corpora, such as treebanks in various languages as well as an automatically parsed large corpus.

We present HindEnCorp, a parallel corpus of Hindi and English, and HindMonoCorp, a monolingual corpus of Hindi in their release version 0.5. Both corpora were collected from web sources and preprocessed primarily for the training of statistical machine translation systems. HindEnCorp consists of 274k parallel sentences (3.9 million Hindi and 3.8 million English tokens). HindMonoCorp amounts to 787 million tokens in 44 million sentences. Both the corpora are freely available for non-commercial research and their preliminary release has been used by numerous participants of the WMT 2014 shared translation task.

Statistical machine translation to morphologically richer languages is a challenging task and more so if the source and target languages differ in word order. Current state-of-the-art MT systems thus deliver mediocre results. Adding more parallel data often helps improve the results; if it doesn't, it may be caused by various problems such as different domains, bad alignment or noise in the new data. In this paper we evaluate the English-to-Hindi MT task from this data perspective. We discuss several available parallel data sources and provide cross-evaluation results on their combinations using two freely available statistical MT systems. We demonstrate various problems encountered in the data and describe automatic methods of data cleaning and normalization. We also show that the contents of two independently distributed data sets can unexpectedly overlap, which negatively affects translation quality. Together with the error analysis, we also present a new tool for viewing aligned corpora, which makes it easier to detect difficult parts in the data even for a developer not speaking the target language.