Cross-lingual aspect-based sentiment analysis (ABSA) involves detailed sentiment analysis in a target language by transferring knowledge from a source language with available annotated data.Most existing methods depend heavily on often unreliable translation tools to bridge the language gap.In this paper, we propose a new approach that leverages a large language model (LLM) to generate highquality pseudo-labelled data in the target language without the need for translation tools.First, the framework trains an ABSA model to obtain predictions for unlabelled target language data.Next, LLM is prompted to generate natural sentences that better represent these noisy predictions than the original text.The ABSA model is then further fine-tuned on the resulting pseudo-labelled dataset.We demonstrate the effectiveness of this method across six languages and five backbone models, surpassing previous state-of-the-art translation-based approaches.The proposed framework also supports generative models, and we show that finetuned LLMs outperform smaller multilingual models.

Aspect-based sentiment analysis (ABSA) has made significant strides, yet challenges remain for low-resource languages due to the predominant focus on English. Current cross-lingual ABSA studies often centre on simpler tasks and rely heavily on external translation tools. In this paper, we present a novel sequence-to-sequence method for compound ABSA tasks that eliminates the need for such tools. Our approach, which uses constrained decoding, improves cross-lingual ABSA performance by up to 10\%. This method broadens the scope of cross-lingual ABSA, enabling it to handle more complex tasks and providing a practical, efficient alternative to translation-dependent techniques. Furthermore, we compare our approach with large language models (LLMs) and show that while fine-tuned multilingual LLMs can achieve comparable results, English-centric LLMs struggle with these tasks.

Text summarization is the task of shortening a larger body of text into a concise version while retaining its essential meaning and key information. While summarization has been significantly explored in English and other high-resource languages, Czech text summarization, particularly for historical documents, remains underexplored due to linguistic complexities and a scarcity of annotated datasets. Large language models such as Mistral and mT5 have demonstrated excellent results on many natural language processing tasks and languages. Therefore, we employ these models for Czech summarization, resulting in two key contributions: (1) achieving new state-of-the-art results on the modern Czech summarization dataset SumeCzech using these advanced models, and (2) introducing a novel dataset called Posel od Čerchova for summarization of historical Czech documents with baseline results. Together, these contributions provide a great potential for advancing Czech text summarization and open new avenues for research in Czech historical text processing.

Abstract Historical document analysis plays a crucial role in understanding and preserving our past. However, this task is often hindered by challenges such as limited annotated training data and the diverse nature of historical handwritten documents. In this paper, we explore the potential of self-supervised learning (SSL) in historical document analysis, with a particular focus on historical handwritten document segmentation, to overcome the need for extensive annotated data while enhancing efficiency and robustness. We present an overview of SSL methods suitable for historical document analysis and discuss their potential applications and benefits. Furthermore, we present an approach for SSL in the document domain, considering various setups, augmentations, and resolutions. We also provide experimental results that demonstrate its feasibility and effectiveness. Our findings indicate that most document segmentation tasks can be effectively addressed using SSL features, highlighting the potential of SSL to advance historical document analysis and pave the way for more efficient and robust document processing workflows.

Dialogue act (DA) recognition is usually realized from a speech signal that is transcribed and segmented into text. However, only a little work in DA recognition from images exists. Therefore, this paper concentrates on this modality and presents a novel DA recognition approach for image documents, namely comic books. To the best of our knowledge, this is the first study investigating dialogue acts from comic books and represents the first steps to building a model for comic book understanding. The proposed method is composed of the following steps: speech balloon segmentation, optical character recognition (OCR), and DA recognition itself. We use YOLOv8 for balloon segmentation, Google Vision for OCR, and Transformer-based models for DA classification. The experiments are performed on a newly created dataset comprising 1,438 annotated comic panels. It contains bounding boxes, transcriptions, and dialogue act annotation. We have achieved nearly 98% average precision for speech balloon segmentation and exceeded the accuracy of 70% for the DA recognition task. We also present an analysis of dialogue structure in the comics domain and compare it with the standard DA datasets, representing another contribution of this paper.

In this paper, we introduce a novel Czech dataset for aspect-based sentiment analysis (ABSA), which consists of 3.1K manually annotated reviews from the restaurant domain. The dataset is built upon the older Czech dataset, which contained only separate labels for the basic ABSA tasks such as aspect term extraction or aspect polarity detection. Unlike its predecessor, our new dataset is specifically designed to allow its usage for more complex tasks, e.g. target-aspect-category detection. These advanced tasks require a unified annotation format, seamlessly linking sentiment elements (labels) together. Our dataset follows the format of the well-known SemEval-2016 datasets. This design choice allows effortless application and evaluation in cross-lingual scenarios, ultimately fostering cross-language comparisons with equivalent counterpart datasets in other languages. The annotation process engaged two trained annotators, yielding an impressive inter-annotator agreement rate of approximately 90%. Additionally, we provide 24M reviews without annotations suitable for unsupervised learning. We present robust monolingual baseline results achieved with various Transformer-based models and insightful error analysis to supplement our contributions. Our code and dataset are freely available for non-commercial research purposes.

This paper presents a novel method for grid detection in historical maps. The approach is based on Hough transform accompanied with a sophisticated post-processing. They are applied to detect the grid that consists of graticule lines. It works without any training and does not require any annotated data. The proposed approach is very efficient in detecting the rectangular grid and the intersection points as shown in the international "MapSeg" segmentation competition, where it won the Task 3 with a significant margin. The robustness of the proposed method has been demonstrated by evaluating on another dataset composed of significantly different cadastral map images with excellent results.

International audience

With the author(s)’ decision to order Open Choice, the copyright of the article changed on 3rd December 2020 to [The Authors] [2020] and the article is forthwith distributed under the terms of copyright.

Obvyklý způsob použití POEM deskriptorů je vytvoření příznaků v pravidelných obdélníkových regionech, které pokrývají celý snímek. Příznaky jsou spojeny do jednoho vektoru, který reprezentuje snímek obličeje. V článku je navržena vylepšená metoda, která využívá automaticky detekované body pro vytvoření příznaků. Zároveň je použita komplexnější metoda pro porovnávání příznakových vektorů. Navržená metoda nalezne uplatnění zejména v případech, kdy je k dispozici omezené množství dat a použití např. neuronových sítí by proto bylo obtížné. Metoda je testována na třech standardních obličejových korpusech. Dosažené výsledky ukazují, že použití POEM deskriptorů a příznaků, vytvořených v automaticky detekovaných bodech, dosahuje výrazně lepších výsledků, než základní metody.

As the number of digitized archival documents increases very rapidly, named entity recognition (NER) in historical documents has become very important for information extraction and data mining. For this task an annotated corpus is needed, which has up to now been missing for Czech. In this paper we present a new annotated data collection for historical NER, composed of Czech historical newspapers. This corpus is freely available for research purposes. For this corpus, we have defined relevant domain-specific named entity types and created an annotation manual for corpus labelling. We further conducted some experiments on this corpus using recurrent neural networks. We experimented with randomly initialized embeddings and static and dynamic fastText word embeddings. We achieved 0.73 F1 score with a bidirectional LSTM model using static fastText embeddings.

Abstract As the number of digitized historical documents has increased rapidly during the last a few decades, it is necessary to provide efficient methods of information retrieval and knowledge extraction to make the data accessible. Such methods are dependent on optical character recognition (OCR) which converts the document images into textual representations. Nowadays, OCR methods are often not adapted to the historical domain; moreover, they usually need a significant amount of annotated documents. Therefore, this paper introduces a set of methods that allows performing an OCR on historical document images using only a small amount of real, manually annotated training data. The presented complete OCR system includes two main tasks: page layout analysis including text block and line segmentation and OCR. Our segmentation methods are based on fully convolutional networks, and the OCR approach utilizes recurrent neural networks. Both approaches are state of the art in the relevant fields. We have created a novel real dataset for OCR from Porta fontium portal. This corpus is freely available for research, and all proposed methods are evaluated on these data. We show that both the segmentation and OCR tasks are feasible with only a few annotated real data samples. The experiments aim at determining the best way how to achieve good performance with the given small set of data. We also demonstrate that obtained scores are comparable or even better than the scores of several state-of-the-art systems. To sum up, this paper shows a way how to create an efficient OCR system for historical documents with a need for only a little annotated training data.

Global pooling layers are an essential part of Convolutional Neural Networks (CNN). They are used to aggregate activations of spatial locations to produce a fixed-size vector in several state-of-the-art CNNs. Global average pooling or global max pooling are commonly used for converting convolutional features of variable size images to a fix-sized embedding. However, both pooling layer types are computed spatially independent: each individual activation map is pooled and thus activations of different locations are pooled together. In contrast, we propose Deep Generalized Max Pooling that balances the contribution of all activations of a spatially coherent region by re-weighting all descriptors so that the impact of frequent and rare ones is equalized. We show that this layer is superior to both average and max pooling on the classification of Latin medieval manuscripts (CLAMM'16, CLAMM'17), as well as writer identification (Historical-WI'17).

Current optical character recognition (OCR) systems commonly make use of recurrent neural networks (RNN) that process whole text lines. Such systems avoid the task of character segmentation necessary for character-based approaches. A disadvantage of this approach is a need of a large amount of annotated data. This can be solved by sing generated synthetic data instead of costly manually annotated ones. Unfortunately, such data is often not suitable for historical documents particularly for quality reasons. This work presents a hybrid approach for generating annotated data for OCR at a low cost. We first collect a small dataset of isolated characters from historical document images. Then, we generate historical looking text lines from the generated characters. Another contribution lies in the design and implementation of an OCR system based on a convolutional-LSTM network. We first pre-train this system on hybrid data. Afterwards, the network is fine-tuned with real printed text lines. We demonstrate that this training strategy is efficient for obtaining state-of-the-art results. We also show that the score of the proposed system is comparable or even better in comparison to several state-of-the-art systems.

This paper deals with multi-lingual dialogue act (DA) recognition. The proposed approaches are based on deep neural networks and use word2vec embeddings for word representation. Two multi-lingual models are proposed for this task. The first approach uses one general model trained on the embeddings from all available languages. The second method trains the model on a single pivot language and a linear transformation method is used to project other languages onto the pivot language. The popular convolutional neural network and LSTM architectures with different set-ups are used as classifiers. To the best of our knowledge this is the first attempt at multi-lingual DA recognition using neural networks. The multi-lingual models are validated experimentally on two languages from the Verbmobil corpus.

Cancer is nowadays considered as one of the most dangerous diseases in the world. Especially, breast cancer represents for women the second most common type of cancer and is a main cause of cancer dead. This paper presents a novel method for breast cancer detection from mammographic images based on Local Binary Patterns (LBP). This approach successfully uses LBP based features with a classifier and thresholding. The proposed method is evaluated on a set composed of images extracted from MIAS and DDSM databases. We have experimentally shown that the proposed method is efficient and effective because the achieved accuracy is about 84%.

This paper is focused on automatic face recognition in order to annotate people in photographs taken in completely uncontrolled environment. Recognition accuracy of the current approaches is not sufficient in this case and it is thus beneficial to improve the results. We would like to solve this issue by proposing a novel confidence measure method to identify the incorrectly classified examples at the output of our classifier. The proposed approach combines two measures based on the posterior probability and two ones based on the predictor features in a supervised way. The experiments show that the proposed approach is very efficient, because it detects almost all erroneous examples.

This paper deals with Automatic Face Recognition (AFR). A novel approach which combines the SIFT and SURF features for the face representation is proposed. The obtained combined SIFT/SURF descriptor is then used for face comparison by the adapted Kepenekci matching method. The proposed method is evaluated on the FERET and CTK corpora. The obtained recognition rates are 98.4% and 64.6% respectively. These recognition scores show that our approach outperforms significantly all other methods on these corpora. The differences between recognition error rates of the proposed approach and the second best one are 41% and 7% in relative value respectively.

This work proposes a new research direction to address the lack of structures in traditional n-gram models. It is based on a weakly supervised dependency parser that can model speech syntax without relying on any annotated training corpus. La- beled data is replaced by a few hand-crafted rules that encode basic syntactic knowledge. Bayesian inference then samples the rules, disambiguating and combining them to create complex tree structures that maximize a discriminative model's posterior on a target unlabeled corpus. This posterior encodes sparse se- lectional preferences between a head word and its dependents. The model is evaluated on English and Czech newspaper texts, and is then validated on French broadcast news transcriptions.

Automatic speech transcripts can be made more readable and useful for further processing by enriching them with punctuation marks and other meta-linguistic information. We study in this work how to improve automatic recovery of one of the most difficult punctuation marks, commas, in French and in Czech. We show that commas detection performances are largely improved in both languages by integrating into our baseline Conditional Random Field model syntactic features derived from dependency structures. We further study the relative impact of language-independent vs. specific features, and show that a combination of both of them gives the largest improvement. Robustness of these features to speech recognition errors is finally discussed.

This paper deals with semi-supervised classifier training for automatic dialog acts (DAs) recognition. In our previous works, we have designed a dialog act recognition system for reservation applications in the Czech language. In this work, we propose to retrain this system on another corpus, for another task (broadcast news speech), in a different language (French) and with another set of dialog acts. This is realized using a semi-supervised approach based on the expectation-maximization (EM) algorithm. We show that, in the proposed experimental setup, the use of confidence measures to filter out incorrectly recognized dialog acts is required to improve the results. Two confidence measures are thus proposed and evaluated on the French broadcast news corpus. Experimental results confirm the interest of this approach for the task of training automatic dialog act classifiers.

This paper deals with automatic dialog acts (DAs) recognition in Czech. Our work focuses on two applications: a multimodal reservation system and an animated talking head for hearing-impaired people. In that context, we consider the following DAs: statements, orders, investigation questions and other questions. The main goal of this paper is to propose, implement and evaluate new approaches to automatic DAs recognition based on sentence structure and prosody. Our system is tested on a Czech corpus that simulates a task of train tickets reservation. With lexical-only information, the classification accuracy is 91 %. We proposed two methods to include sentence structure information, which respectively give 94 % and 95 %. When prosodic information is further considered, the recognition accuracy reaches 96 %.

This paper deals with automatic dialog acts (DAs) recognition in Czech. The dialog acts are sentence-level labels that represent different states of a dialogue, depending on the application. Our work focuses on two applications: a multimodal reservation system and an animated talking head for hearing-impaired people. In that context, we consider the following DAs: statements, orders, yes/no questions and other questions. We propose to use both lexical and prosodic information for DAs recognition. The main goal of this paper is to compare different methods to combine the results of both classifiers. On a Czech corpus simulating a reservation of train tickets, the lexical information only gives about 92 % of classification accuracy, while prosody gives only about 45 % of accuracy. When both classifiers are combined with a multilayer perceptron, the lowest (lexical) word error rate further decreases by 26 %. We show that this improvement is close to the optimal one, given the correlation of the lexical and prosodic features. The other combination schemes do not outperform the lexical-only results.