Story components, namely, events, time, participants, and their relations are present in narrative texts from different domains such as journalism, medicine, finance, and law. The automatic extraction of narrative elements encompasses several NLP tasks such as Named Entity Recognition, Semantic Role Labeling, Event Extraction, Coreference resolution, and Temporal Inference. The text2story python, an easy-to-use modular library, supports the narrative extraction and visualization pipeline. The package contains an array of narrative extraction tools that can be used separately or in sequence. With this toolkit, end users can process free text in English or Portuguese and obtain formal representations, like standard annotation files or a formal logical representation. The toolkit also enables narrative visualization as Message Sequence Charts (MSC), Knowledge Graphs, and Bubble Diagrams, making it useful to visualize and transform human-annotated narratives. The package combines the use of off-the-shelf and custom tools and is easily patched (replacing existing components) and extended (e.g. with new visualizations). It includes an experimental module for narrative element effectiveness assessment and being is therefore also a valuable asset for researchers developing solutions for narrative extraction. To evaluate the baseline components, we present some results of the main annotators embedded in our packages for datasets in English and Portuguese. We also compare the results with the extraction of narrative elements by GPT-3, a robust LLM model.

In this work, we propose a Named Entity handling approach to improve translation quality within an existing Natural Language Processing (NLP) pipeline without modifying the Neural Machine Translation (NMT) component. Our approach seeks to enable fast delivery of such improvements and alleviate user experience problems related to NE distortion. We implement separate NE recognition and translation steps. Then, a combination of standard entity masking technique and a novel semantic equivalent placeholder guarantees that both NE translation is respected and the best overall quality is obtained from NMT. The experiments show that translation quality improves in 38.6% of the test cases when compared to a version of the NLP pipeline with less-developed NE handling capability.

This paper describes the research developed at Unbabel, a Portuguese Machine-translation start-up, that combines MT with human post-edition and focuses strictly on customer service content. We aim to contribute to furthering MT quality and good-practices by exposing the importance of having a continuously-in-development robust Named Entity Recognition system compliant with General Data Protection Regulation (GDPR). Moreover, we have tested semiautomatic strategies that support and enhance the creation of Named Entities gold standards to allow a more seamless implementation of Multilingual Named Entities Recognition Systems. The project described in this paper is the result of a shared work between Unbabel ́s linguists and Unbabel ́s AI engineering team, matured over a year. The project should, also, be taken as a statement of multidisciplinary, proving and validating the much-needed articulation between the different scientific fields that compose and characterize the area of Natural Language Processing (NLP).

We propose BeamSeg, a joint model for segmentation and topic identification of documents from the same domain. The model assumes that lexical cohesion can be observed across documents, meaning that segments describing the same topic use a similar lexical distribution over the vocabulary. The model implements lexical cohesion in an unsupervised Bayesian setting by drawing from the same language model segments with the same topic. Contrary to previous approaches, we assume that language models are not independent, since the vocabulary changes in consecutive segments are expected to be smooth and not abrupt. We achieve this by using a dynamic Dirichlet prior that takes into account data contributions from other topics. BeamSeg also models segment length properties of documents based on modality (textbooks, slides, etc.). The evaluation is carried out in three datasets. In two of them, improvements of up to 4.8% and 7.3% are obtained in the segmentation and topic identifications tasks, indicating that both tasks should be jointly modeled.