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Found 14 papers, 5 papers with code
Explaining Classes through Stable Word Attributions
Input saliency methods have recently become a popular tool for explaining predictions of deep learning models in NLP.
Towards better structured and less noisy Web data: Oscar with Register annotations
Web-crawled datasets are known to be noisy, as they feature a wide range of language use covering both user-generated and professionally edited content as well as noise originating from the crawling process.
Multilingual and Zero-Shot is Closing in on Monolingual Web Register Classification
This article studies register classification of documents from the unrestricted web, such as news articles or opinion blogs, in a multilingual setting, exploring both the benefit of training on multiple languages and the capabilities for zero-shot cross-lingual transfer.
Explaining Classes through Word Attribution
In this work, we propose a method for explaining classes using deep learning models and the Integrated Gradients feature attribution technique by aggregating explanations of individual examples in text classification to general descriptions of the classes.
Beyond the English Web: Zero-Shot Cross-Lingual and Lightweight Monolingual Classification of Registers
We explore cross-lingual transfer of register classification for web documents.
Morphological Tagging and Lemmatization of Albanian: A Manually Annotated Corpus and Neural Models
In this paper, we present the first publicly available part-of-speech and morphologically tagged corpus for the Albanian language, as well as a neural morphological tagger and lemmatizer trained on it.
Is Multilingual BERT Fluent in Language Generation?
The multilingual BERT model is trained on 104 languages and meant to serve as a universal language model and tool for encoding sentences.
Template-free Data-to-Text Generation of Finnish Sports News
News articles such as sports game reports are often thought to closely follow the underlying game statistics, but in practice they contain a notable amount of background knowledge, interpretation, insight into the game, and quotes that are not present in the official statistics.
A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations
We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches.
Bank distress in the news: Describing events through deep learning
While many models are purposed for detecting the occurrence of significant events in financial systems, the task of providing qualitative detail on the developments is not usually as well automated.
Detect & Describe: Deep learning of bank stress in the news
We model bank distress with data on 243 events and 6. 6M news articles for 101 large European banks.
Exploratory topic modeling with distributional semantics
As we continue to collect and store textual data in a multitude of domains, we are regularly confronted with material whose largely unknown thematic structure we want to uncover.
Interactive Visual Exploration of Topic Models using Graphs
Probabilistic topic modeling is a popular and powerful family of tools for uncovering thematic structure in large sets of unstructured text documents.
Cluster coloring of the Self-Organizing Map: An information visualization perspective
From the viewpoint of information visualization, this paper provides a general, yet simple, solution to projection-based coloring of the SOM that reveals structures.
