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Emotion Classification in German Plays with Transformer-based language Models Pretrained on Historical and Contemporary language 5

Emotion Classification in German Plays with Transformer-base...

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5th Joint SIGHUM workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, LaTeCHCLfL 2021

作者： Schmidt, Thomas Dennerlein, Katrin Wolff, Christian Media Informatics Group University of Regensburg Germany German Literary Studies University of Würzburg Germany

ISBN: (纸本)9781954085916

We present results of a project on emotion classification on historical German plays of Enlightenment, Storm and Stress, and German Classicism. We have developed a hierarchical annotation scheme consisting of 13 sub-emotions like suffering, love and joy that sum up to 6 main and 2 polarity classes (positive/negative). We have conducted textual annotations on 11 German plays and have acquired over 13,000 emotion annotations by two annotators per play. We have evaluated multiple traditional machine learning approaches as well as transformer-based models pretrained on historical and contemporary language for a single-label text sequence emotion classification for the different emotion categories. The evaluation is carried out on three different instances of the corpus: (1) taking all annotations, (2) filtering overlapping annotations by annotators, (3) applying a heuristic for speech-based analysis. Best results are achieved on the filtered corpus with the best models being large transformer-based models pretrained on contemporary German language. For the polarity classification accuracies of up to 90% are achieved. The accuracies become lower for settings with a higher number of classes, achieving 66% for 13 sub-emotions. Further pretraining of a historical model with a corpus of dramatic texts led to no improvements. © LaTeCHCLfL,EMNLP 2021 - *** rights reserved.

关键词： natural language processing systems

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Style Control for Schema-Guided natural language Generation 3

Style Control for Schema-Guided Natural Language Generation

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3rd workshop on natural language processing for Conversational AI, NLP4ConvAI 2021

作者： Tsai, Alicia Y. Oraby, Shereen Perera, Vittorio Kao, Jiun-Yu Du, Yuheng Narayan-Chen, Anjali Chung, Tagyoung Hakkani-Tur, Dilek University of California Berkeley United States Amazon Alexa AI United States

ISBN: (纸本)9781954085862

natural language Generation (NLG) for task-oriented dialogue systems focuses on communicating specific content accurately, fluently, and coherently. While these attributes are crucial for a successful dialogue, it is also desirable to simultaneously accomplish specific stylistic goals, such as response length, point-of-view, descriptiveness, sentiment, formality, and empathy. In this work, we focus on stylistic control and evaluation for schema-guided NLG, with joint goals of achieving both semantic and stylistic control. We experiment in detail with various controlled generation methods for large pretrained language models: specifically, conditional training, guided fine-tuning, and guided decoding. We discuss their advantages and limitations, and evaluate them with a broad range of automatic and human evaluation metrics. Our results show that while high style accuracy and semantic correctness are easier to achieve for more lexically-defined styles with conditional training, stylistic control is also achievable for more semantically complex styles using discriminator-based guided decoding methods. The results also suggest that methods that are more scalable (with less hyper-parameters tuning) and that disentangle content generation and stylistic variations are more effective at achieving semantic correctness and style accuracy. © 2021 Association for Computational Linguistics.

关键词： Semantics

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Parsing with Pretrained language Models, Multiple Datasets, and Dataset Embeddings 20

Parsing with Pretrained Language Models, Multiple Datasets, ...

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20th International workshop on Treebanks and Linguistic Theories, TLT 2021

作者： van der Goot, Rob de Lhoneux, Miryam IT University of Copenhagen Denmark Uppsala University Sweden KU Leuven Belgium University of Copenhagen Denmark

ISBN: (纸本)9781955917162

With an increase of dataset availability, the potential for learning from a variety of data sources has increased. One particular method to improve learning from multiple data sources is to embed the data source during training. This allows the model to learn generalizable features as well as distinguishing features between datasets. However, these dataset embeddings have mostly been used before contextualized transformer-based embeddings were introduced in the field of natural language processing. In this work, we compare two methods to embed datasets in a transformer-based multilingual dependency parser, and perform an extensive evaluation. We show that: 1) embedding the dataset is still beneficial with these models 2) performance increases are highest when embedding the dataset at the encoder level 3) unsurprisingly, we confirm that performance increases are highest for small datasets and datasets with a low baseline score. 4) we show that training on the combination of all datasets performs similarly to designing smaller clusters based on language-relatedness. © TLT 2021 International workshop on Treebanks and Linguistic Theories

关键词： Embeddings

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language-independent features for authorship attribution on Ukrainian texts 7

Language-independent features for authorship attribution on ...

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7th International Conference "Information Technology and Interactions", IT and I 2020

作者： Hlavcheva, Yuliia Glavchev, Maksym Bobicev, Victoria Kanishcheva, Olga National Technical University KhPI 2 Kyrpychova str. Kharkiv61002 Ukraine Technical University of Moldova Bd. Stefan cel Mare 168 ChişinauMD-2004 Moldova

Authorship attribution is the natural language processing task of the author identification of an input text. The main goal of this task is to define the salient characteristics of documents that capture the author's writing style. In this paper, we analyze language-independent features for authorship attribution. All experiments were realized on the corpus of Ukrainian scientific papers. For the experiments we used Bayes based Algorithms (Naive Bayes Multinomial), Support Vector Machine (SMO) and Decision Trees (LMT, J48) methods. The experimental results of the scientific text classification demonstrated that Decision Trees method in most cases outperforms other machine learning methods, and the proposed in the paper language-independent features are appropriate for the Ukrainian scientific documents authorship attribution. © 2020 Copyright for this paper by its authors.

关键词： Decision trees

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A Pilot Study for BERT language Modelling and Morphological Analysis for Ancient and Medieval Greek 5

A Pilot Study for BERT Language Modelling and Morphological ...

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5th Joint SIGHUM workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, LaTeCHCLfL 2021

作者： Singh, Pranaydeep Rutten, Gorik Lefever, Els LT3 Language and Translation Technology Team Department of Translation Interpreting and Communication Ghent University Groot-Brittanniëlaan 45 Ghent9000 Belgium

ISBN: (纸本)9781954085916

This paper presents a pilot study to automatic linguistic preprocessing of Ancient and Byzantine Greek, and morphological analysis more specifically. To this end, a novel subword-based BERT language model was trained on the basis of a varied corpus of Modern, Ancient and Post-classical Greek texts. Consequently, the obtained BERT embeddings were incorporated to train a fine-grained Part-of-Speech tagger for Ancient and Byzantine Greek. In addition, a corpus of Greek Epigrams was manually annotated and the resulting gold standard was used to evaluate the performance of the morphological analyser on Byzantine Greek. The experimental results show very good perplexity scores (4.9) for the BERT language model and state-of-the-art performance for the fine-grained Part-of-Speech tagger for in-domain data (treebanks containing a mixture of Classical and Medieval Greek), as well as for the newly created Byzantine Greek gold standard data set. The language models and associated code are made available for use at https://***/pranaydeeps/Ancient-Greek-BERT. © LaTeCHCLfL,EMNLP 2021 - *** rights reserved.

关键词： Modeling languages

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Deep Learning and Film History: Model Explanation Techniques in the Analysis of Temporality in Finnish Fiction Film Metadata 6

Deep Learning and Film History: Model Explanation Techniques...

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6th Digital Humanities in the Nordic and Baltic Countries Conference, DHNB 2022

作者： Ginter, Filip Kiiskinen, Harri Kanerva, Jenna Chang, Li-Hsin Salmi, Hannu TurkuNLP Department of Computing University of Turku Finland Department of Cultural History University of Turku Finland

We demonstrate the application of a deep-learning -based regressor, on a case study of predicting movie production year based on its plot summary. We show how the Integrated Gradients (IG) model explanation method can be used to attribute the predictions to individual input features and compare these to human-assigned attributions. Our purpose is to provide an insight into the application of modern NLP methods in the scope of a digital humanities research question, and test the model explanation techniques on a problem that is easy to understand, yet non-trivial for both humans and machine learning algorithms alike. We find that the model clearly outperforms non-expert human annotators, being able to date the movies well within the correct decade on average. We also demonstrate that the model-assigned attributions agree with those assigned by humans, especially for correct predictions. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： natural language processing systems

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Sequence-to-Sequence Lexical Normalization with Multilingual Transformers 7

Sequence-to-Sequence Lexical Normalization with Multilingual...

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7th workshop on Noisy User-Generated Text, W-NUT 2021

作者： Bucur, Ana-Maria Cosma, Adrian Dinu, Liviu P. University of Bucharest Romania University Politehnica of Bucharest Romania

ISBN: (纸本)9781954085909

Current benchmark tasks for natural language processing contain text that is qualitatively different from the text used in informal day to day digital communication. This discrepancy has led to severe performance degradation of state-of-the-art NLP models when fine-tuned on real-world data. One way to resolve this issue is through lexical normalization, which is the process of transforming non-standard text, usually from social media, into a more standardized form. In this work, we propose a sentence-level sequence-to-sequence model based on mBART, which frames the problem as a machine translation problem. As the noisy text is a pervasive problem across languages, not just English, we leverage the multilingual pre-training of mBART to fine-tune it to our data. While current approaches mainly operate at the word or subword level, we argue that this approach is straightforward from a technical standpoint and builds upon existing pre-trained transformer networks. Our results show that while word-level, intrinsic, performance evaluation is behind other methods, our model improves performance on extrinsic, downstream tasks through normalization compared to models operating on raw, unprocessed, social media text. © 2021 Association for Computational Linguistics.

关键词： natural language processing systems

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Attention vs non-attention for a Shapley-based explanation method 2

Attention vs non-attention for a Shapley-based explanation m...

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2nd workshop on Knowledge Extraction and Integration for Deep Learning Architectures: Deep Learning Inside Out, DeeLIO 2021

作者： Kersten, Tom Wong, Hugh Mee Jumelet, Jaap Hupkes, Dieuwke University of Amsterdam Netherlands ILLC University of Amsterdam Netherlands Facebook AI Research

ISBN: (纸本)9781954085305

The field of explainable AI has recently seen an explosion in the number of explanation methods for highly non-linear deep neural networks. The extent to which such methods – that are often proposed and tested in the domain of computer vision – are appropriate to address the explainability challenges in NLP is yet relatively unexplored. In this work, we consider Contextual Decomposition (CD) – a Shapley-based input feature attribution method that has been shown to work well for recurrent NLP models – and we test the extent to which it is useful for models that contain attention operations. To this end, we extend CD to cover the operations necessary for attention-based models. We then compare how long distance subject-verb relationships are processed by models with and without attention, considering a number of different syntactic structures in two different languages: English and Dutch. Our experiments confirm that CD can successfully be applied for attention-based models as well, providing an alternative Shapley-based attribution method for modern neural networks. In particular, using CD, we show that the English and Dutch models demonstrate similar processing behaviour, but that under the hood there are consistent differences between our attention and non-attention models. © 2021 Association for Computational Linguistics.

关键词： natural language processing systems

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The Content Analysis Method for the Information Resources Formation in Electronic Content Commerce Systems 5

The Content Analysis Method for the Information Resources Fo...

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5th International Conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Berko, Andrii Andrunyk, Vasyl Chyrun, Lyubomyr Sorokovskyy, Mykhailo Oborska, Oksana Oryshchyn, Oksana Luchkevych, Mykhailo Brodovska, Oksana Lviv Polytech Natl Univ S Bandera St 12 UA-79013 Lvov Ukraine Ivan Franko Natl Univ Lviv Univ St 1 UA-79000 Lvov Ukraine Drohobych Ivan Franko State Pedag Univ Ivan Franko St 24 UA-82100 Drogobych Ukraine Ltd Liabil Co Financial Co MAGNAT E Konovaltsya St 36-5 UA-01133 Kiev Ukraine

The article on developing the unified method of processing information resources in electronic content commerce systems (ECCS) is devoted. A model and a typical generalized architecture of an ECCS are developed. Strategies for designing and implementing ECCS using an on-line journal and newspaper that reflects the results of theoretical studies are developed. The general approaches for creating these systems, typical architecture and standardized methods for content formation/management/support. The on-line journal and newspaper is a typical example of an ECCS, which refers to managed cybernetic systems and has a certain properties set. Articles content is based on the on-line newspaper/journal, according to which the user searches to obtain the necessary data. However, texts filled with keywords are not always enough to get the required content by the user. Highlighting keywords in each article is a laborious and lengthy process. Using a formalized method of content analysis, the process is fully automated and occurs when the author adds a new article. The method defines similar articles to topics viewed by the user. The using content analysis advantage is to determine the content availability for a specific request, for example, to determine the lack of content on a specific topic and the authors' focus on this issue development.

关键词： Electronic content commerce system information resource information technology intelligent system content commerce commercial content content analysis processing information resource text analysis on-line magazine natural language textual content

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methods for automatic sentiment detection 10

Methods for automatic sentiment detection

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10th Annual International Scientific and Practical Conference named after A. I. Kitov Information Technologies and Mathematical methods in Economics and Management, IT and MM 2020

作者： Gurin, Anatoly A. Plekhanov Russian University of Economics 36 Stremyanny lane Moscow115998 Russia

Semantic analysis has great potential applications in various fields of science and the national economy. Much of the information in the world is not structured, so there is the problem of processing and extracting useful data. natural language processing is a very complex process. On November 23, 2017, as part of the 2017 Runet Prize award ceremony, the HOT-LIST 2018 was presented, that identified the main digital trends of 2018 and presented a list of trendsetters in 10 technology, innovation and business *** were included 10 leading companies in 10 areas and the first area is AI technologies. AI technology has become a key technology trend in 2018, and the volume of global investment in these technologies and products based on them in excess of $ 1 billion.[25] More than 180 private companies working on projects in the field of AI technologies have been purchased during the period 2011-2018. According to IDC Customer Insights & Analysis.[25] During the period 2011-2018 it was purchased more than 180 private companies working on projects of AI technologies. According to forecasts of Frost & Sullivan, by 2022 artificial intelligence market will grow to $ 10 billion using machine learning technologies and natural language recognition in advertising, retail, finance and health.[25]. © 2021 Copyright for this paper by its authors.

关键词： Sentiment analysis

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