Vicinity-driven paragraph and sentence alignment for comparable corpora

Parallel corpora have driven great progress in the field of Text Simplification. However, most sentence alignment algorithms either offer a limited range of alignment types supported, or simply ignore valuable clues present in comparable documents. We address this problem by introducing a new set of flexible vicinity-driven paragraph and sentence alignment algorithms that 1-N, N-1, N-N and long distance null alignments without the need for hard-to-replicate supervised models

DEPLAIN: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification

Text simplification is an intralingual translation task in which documents, or sentences of a complex source text are simplified for a target audience. The success of automatic text simplification systems is highly dependent on the quality of parallel data used for training and evaluation. To advance sentence simplification and document simplification in German, this paper presents DEplain, a new dataset of parallel, professionally written and manually aligned simplifications in plain German ("plain DE" or in German: "Einfache Sprache"). DEplain consists of a news domain (approx. 500 document pairs, approx. 13k sentence pairs) and a web-domain corpus (approx. 150 aligned documents, approx. 2k aligned sentence pairs). In addition, we are building a web harvester and experimenting with automatic alignment methods to facilitate the integration of non-aligned and to be published parallel documents. Using this approach, we are dynamically increasing the web domain corpus, so it is currently extended to approx. 750 document pairs and approx. 3.5k aligned sentence pairs. We show that using DEplain to train a transformer-based seq2seq text simplification model can achieve promising results. We make available the corpus, the adapted alignment methods for German, the web harvester and the trained models here: https://github.com/rstodden/DEPlain.Comment: Accepted to ACL 202

Construcción de un corpus comparable y un recurso de referencia para la simplificación de textos médicos en español

We report the collection of the CLARA-MeD comparable corpus, which is made up of 24 298 pairs of professional and simplified texts in the medical domain for the Spanish language (>96M tokens). Texts types range from drug leaflets and summaries of product characteristics (10 211 pairs of texts, >82M words), abstracts of systematic reviews (8138 pairs of texts, >9M words), cancer-related information summaries (201 pairs of texts, >3M tokens) and clinical trials announcements (5748 pairs of texts, 451 690 words). We also report the alignment of professional and simplified sentences, conducted manually by pairs of annotators. A subset of 3800 sentence pairs (149 862 tokens) has been aligned each by 2 experts, with an average inter-annotator agreement kappa score of 0.839 (0.076). The data are available in the community and contributes with a new benchmark to develop and evaluate automatic medical text simplification systems.Se describe la recogida del corpus comparable CLARA-MeD, formado por 24 298 pares de textos profesionales y simplificados de dominio médico en lengua española (>96M palabras). Los tipos de textos varían desde prospectos médicos y fichas técnicas de medicamentos (10 211 pares de textos, >82M palabras), resúmenes de revisiones sistemáticas (8138 pares de textos, >9M palabras), resúmenes de información sobre el cáncer (201 pares de textos, >3M palabras) y anuncios de ensayos clínicos (5748 pares de textos, 451 690 palabras). También presentamos el alineamiento de frases técnicas y simplificadas, realizado a mano por pares de anotadores. Un subconjunto de 3800 pares de frases (149 862 tokens) se han emparejado, con un acuerdo medio entre anotadores con valor kappa = 0.839 (0.076). Los datos están disponibles en la comunidad y este nuevo recurso permite desarrollar y evaluar sistemas de simplificación automática de textos médicos.Project CLARA-MED (PID2020-116001RA-C33) funded by MCIN/AEI/10.13039/501100011033/, in project call: “Proyectos I+D+i Retos Investigación”

Data-driven sentence simplification: Survey and benchmark

Sentence Simplification (SS) aims to modify a sentence in order to make it easier to read and understand. In order to do so, several rewriting transformations can be performed such as replacement, reordering, and splitting. Executing these transformations while keeping sentences grammatical, preserving their main idea, and generating simpler output, is a challenging and still far from solved problem. In this article, we survey research on SS, focusing on approaches that attempt to learn how to simplify using corpora of aligned original-simplified sentence pairs in English, which is the dominant paradigm nowadays. We also include a benchmark of different approaches on common datasets so as to compare them and highlight their strengths and limitations. We expect that this survey will serve as a starting point for researchers interested in the task and help spark new ideas for future developments

{UNIQORN}: {U}nified Question Answering over {RDF} Knowledge Graphs and Natural Language Text

Question answering over knowledge graphs and other RDF data has been greatly advanced, with a number of good systems providing crisp answers for natural language questions or telegraphic queries. Some of these systems incorporate textual sources as additional evidence for the answering process, but cannot compute answers that are present in text alone. Conversely, systems from the IR and NLP communities have addressed QA over text, but barely utilize semantic data and knowledge. This paper presents the first QA system that can seamlessly operate over RDF datasets and text corpora, or both together, in a unified framework. Our method, called UNIQORN, builds a context graph on the fly, by retrieving question-relevant triples from the RDF data and/or the text corpus, where the latter case is handled by automatic information extraction. The resulting graph is typically rich but highly noisy. UNIQORN copes with this input by advanced graph algorithms for Group Steiner Trees, that identify the best answer candidates in the context graph. Experimental results on several benchmarks of complex questions with multiple entities and relations, show that UNIQORN, an unsupervised method with only five parameters, produces results comparable to the state-of-the-art on KGs, text corpora, and heterogeneous sources. The graph-based methodology provides user-interpretable evidence for the complete answering process

A free/open-source hybrid morphological disambiguation tool for Kazakh

This paper presents the results of developing a morphological disambiguation tool for Kazakh. Starting with a previously developed rule-based approach, we tried to cope with the complex morphology of Kazakh by breaking up lexical forms across their derivational boundaries into inflectional groups and modeling their behavior with statistical methods. A hybrid rule-based/statistical approach appears to benefit morphological disambiguation demonstrating a per-token accuracy of 91% in running text

Extracting and Attributing Quotes in Text and Assessing them as Opinions

News articles often report on the opinions that salient people have about important issues. While it is possible to infer an opinion from a person's actions, it is much more common to demonstrate that a person holds an opinion by reporting on what they have said. These instances of speech are called reported speech, and in this thesis we set out to detect instances of reported speech, attribute them to their speaker, and to identify which instances provide evidence of an opinion. We first focus on extracting reported speech, which involves finding all acts of communication that are reported in an article. Previous work has approached this task with rule-based methods, however there are several factors that confound these approaches. To demonstrate this, we build a corpus of 965 news articles, where we mark all instances of speech. We then show that a supervised token-based approach outperforms all of our rule-based alternatives, even in extracting direct quotes. Next, we examine the problem of finding the speaker of each quote. For this task we annotate the same 965 news articles with links from each quote to its speaker. Using this, and three other corpora, we develop new methods and features for quote attribution, which achieve state-of-the-art accuracy on our corpus and strong results on the others. Having extracted quotes and determined who spoke them, we move on to the opinion mining part of our work. Most of the task definitions in opinion mining do not easily work with opinions in news, so we define a new task, where the aim is to classify whether quotes demonstrate support, neutrality, or opposition to a given position statement. This formulation improved annotator agreement when compared to our earlier annotation schemes. Using this we build an opinion corpus of 700 news documents covering 7 topics. In this thesis we do not attempt this full task, but we do present preliminary results

A free/open-source hybrid morphological disambiguation tool for Kazakh

This paper presents the results of developing a morphological disambiguation tool for Kazakh. Starting with a previously developed rule-based approach, we tried to cope with the complex morphology of Kazakh by breaking up lexical forms across their derivational boundaries into inflectional groups and modeling their behavior with statistical methods. A hybrid rule-based/statistical approach appears to benefit morphological disambiguation demonstrating a per-token accuracy of 91% in running text