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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Xia Zeng; Arkaitz Zubiaga;

    As part of an automated fact-checking pipeline, the claim verification task consists in determining if a claim is supported by an associated piece of evidence. The complexity of gathering labelled claim-evidence pairs leads to a scarcity of datasets, particularly when dealing with new domains. In this article, we introduce Semantic Embedding Element-wise Difference (SEED), a novel vector-based method to few-shot claim verification that aggregates pairwise semantic differences for claim-evidence pairs. We build on the hypothesis that we can simulate class representative vectors that capture average semantic differences for claim-evidence pairs in a class, which can then be used for classification of new instances. We compare the performance of our method with competitive baselines including fine-tuned Bidirectional Encoder Representations from Transformers (BERT)/Robustly Optimized BERT Pre-training Approach (RoBERTa) models, as well as the state-of-the-art few-shot claim verification method that leverages language model perplexity. Experiments conducted on the Fact Extraction and VERification (FEVER) and SCIFACT datasets show consistent improvements over competitive baselines in few-shot settings. Our code is available.

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    PeerJ Computer Science
    Article . 2022 . Peer-reviewed
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      PeerJ Computer Science
      Article . 2022 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Xingwei Tan; Gabriele Pergola; Yulan He;

    This is the code of EMNLP 2021 main track long paper "Extracting Event Temporal Relations via Hyperbolic Geometry". The paper proposed two hyperbolic-based approaches for the event temporal relation extraction task, which is an Event-centric Natural Language Understanding task.

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    ZENODO
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    Authors: Zhu, Lixing; He, Yulan; Zhou, Deyu;

    topical_wordvec_models You first need to create a save folder for training. Download the [saved model](https://topicvecmodels.s3.eu-west-2.amazonaws.com/save/47/model) and place it in ./save/47/ to run the trained model. To construct the training set, refer to https://github.com/somethingx02/topical_wordvec_model please. Trained [wordvecs](https://topicvecmodels.s3.eu-west-2.amazonaws.com/save/47/aggrd_all_wordrep.txt).

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    ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Kocijan, V;

    With appropriate pre-training on unstructured text, larger and more accurate neural network models can be trained. Unfortunately, unstructured pre-training data may contain undesired societal biases, which a model may mimic and amplify. This thesis focuses on both improving unsupervised pre-training and developing diagnostics of obtained pre-trained models for potential undesired behaviour. Pre-training and diagnostics are done on two tasks: coreference resolution and knowledge base completion. For both of them, a novel task-specific method for unsupervised pre-training is introduced. Then, the obtained models are analysed for potential undesired behaviour by evaluating them on relevant datasets, focusing on gender bias in particular. Two novel pre-training datasets for coreference resolution are introduced, MaskedWiki and WikiCREM. By fine-tuning on these datasets, state-of-the-art performance on multiple benchmarks is achieved, including on the Winograd Schema Challenge, a commonsense reasoning benchmark that requires a lot of background knowledge. The obtained pre-trained models are then evaluated on the Gap benchmark. On this benchmark, potentially problematic patterns in the test set are demonstrated. To remove these undesired patterns, a novel test sample weighting method and a proof of its correctness are introduced. A method for pre-training in knowledge base completion is introduced, the first of its kind, significantly improving the results on multiple smaller datasets. The obtained models outperform much larger and highly trained models, which are trained on more general language-modelling tasks. To better understand the behaviour of the obtained models for knowledge base completion, the first diagnostic dataset for pre-trained knowledge base completion models is introduced, demonstrating how stereotypes in the pre-training data can affect the predictions of a model on the target knowledge base. The future developments of both task-specific pre-training and bias detection are discussed, motivating future research directions in the field.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Oxford University Re...arrow_drop_down
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Oxford University Re...arrow_drop_down
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    Authors: Lixing Zhu; Pergola, Gabriele; Gui, Lin; Deyu Zhou; +1 Authors

    Transformer encoder-decoder for emotion detection in dialogues

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Software . 2022
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Authors: Shoemark, Philippa; Ferdousi Liza, Farhana; Hale, Scott A.; Nguyen, Dong; +1 Authors

    This dataset contains monthly word embeddings created from the tweets available via the statuses/sample endpoint of the Twitter Streaming API from 2012 to 2018. Full details of the creation of the dataset are given in Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings. The md5sum of the gzipped tarball file is a76888ffec8cc7aebba09d365ca55ace .

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    ZENODO
    Dataset . 2019
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    Dataset . 2019
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY
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      ZENODO
      Dataset . 2019
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      Data sources: Datacite
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    Authors: Nyhan, Julianne; Vlachidis, Andreas; Flinn, Andrew; Pearlman, Nina; +2 Authors

    The founding collection of the British Museum is a rich area to explore how we can reconnect dispersed heritage connections using state of the art technologies. This is because the British Museum's original 1753 founding collection of Sir Hans Sloane is now split across three different institutions (the British Museum (BM), Natural History Museum (NHM) and the British Library (BL)) and the digital information that describes this founding collection sits in the different institutions in a range of different systems that are not currently set up to talk to one another. By focusing on catalogue records, and the vast, remaining collections of Sir Hans Sloane, the Sloane Lab project is researching how we can work with interested communities and heritage organisations to link the present with the past so as to allow the currently broken links between Sloane's collections and catalogues to be re-established across the NHM, BL, BM (plus others that have relevant material). The main outcome of our project will be a freely available, online digital lab (the Sloane Lab) that will offer researchers, curators and the interested public new opportunities to search, explore, and critically and creatively use and reuse digital cultural heritage.

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    ZENODO
    Report . 2023
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    Authors: Rimell, Laura; Maillard, Jean; Polajnar, Tamara; Clark, Stephen;

    RELPRON is a dataset of subject and object relative clauses, for the evaluation of methods in compositional distributional semantics. ERC (DisCoTex 306920) EPSRC (EP/I037512/1)

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    Apollo
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    Apollo
    Dataset . 2016
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      Apollo
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      Apollo
      Dataset . 2016
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    Authors: Arana-Catania, Miguel; Kochkina, Elena; Zubiaga, Arkaitz; Liakata, Maria; +2 Authors

    The peer-reviewed publication for this dataset has been presented in the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), and can be accessed here: https://arxiv.org/abs/2205.02596. Please cite this when using the dataset. This dataset contains a heterogeneous set of True and False COVID claims and online sources of information for each claim. The claims have been obtained from online fact-checking sources, existing datasets and research challenges. It combines different data sources with different foci, thus enabling a comprehensive approach that combines different media (Twitter, Facebook, general websites, academia), information domains (health, scholar, media), information types (news, claims) and applications (information retrieval, veracity evaluation). The processing of the claims included an extensive de-duplication process eliminating repeated or very similar claims. The dataset is presented in a LARGE and a SMALL version, accounting for different degrees of similarity between the remaining claims (excluding respectively claims with a 90% and 99% probability of being similar, as obtained through the MonoT5 model). The similarity of claims was analysed using BM25 (Robertson et al., 1995; Crestani et al., 1998; Robertson and Zaragoza, 2009) with MonoT5 re-ranking (Nogueira et al., 2020), and BERTScore (Zhang et al., 2019). The processing of the content also involved removing claims making only a direct reference to existing content in other media (audio, video, photos); automatically obtained content not representing claims; and entries with claims or fact-checking sources in languages other than English. The claims were analysed to identify types of claims that may be of particular interest, either for inclusion or exclusion depending on the type of analysis. The following types were identified: (1) Multimodal; (2) Social media references; (3) Claims including questions; (4) Claims including numerical content; (5) Named entities, including: PERSON − People, including fictional; ORGANIZATION − Companies, agencies, institutions, etc.; GPE − Countries, cities, states; FACILITY − Buildings, highways, etc. These entities have been detected using a RoBERTa base English model (Liu et al., 2019) trained on the OntoNotes Release 5.0 dataset (Weischedel et al., 2013) using Spacy. The original labels for the claims have been reviewed and homogenised from the different criteria used by each original fact-checker into the final True and False labels. The data sources used are: - The CoronaVirusFacts/DatosCoronaVirus Alliance Database. https://www.poynter.org/ifcn-covid-19-misinformation/ - CoAID dataset (Cui and Lee, 2020) https://github.com/cuilimeng/CoAID - MM-COVID (Li et al., 2020) https://github.com/bigheiniu/MM-COVID - CovidLies (Hossain et al., 2020) https://github.com/ucinlp/covid19-data - TREC Health Misinformation track https://trec-health-misinfo.github.io/ - TREC COVID challenge (Voorhees et al., 2021; Roberts et al., 2020) https://ir.nist.gov/covidSubmit/data.html The LARGE dataset contains 5,143 claims (1,810 False and 3,333 True), and the SMALL version 1,709 claims (477 False and 1,232 True). The entries in the dataset contain the following information: - Claim. Text of the claim. - Claim label. The labels are: False, and True. - Claim source. The sources include mostly fact-checking websites, health information websites, health clinics, public institutions sites, and peer-reviewed scientific journals. - Original information source. Information about which general information source was used to obtain the claim. - Claim type. The different types, previously explained, are: Multimodal, Social Media, Questions, Numerical, and Named Entities. Funding. This work was supported by the UK Engineering and Physical Sciences Research Council (grant no. EP/V048597/1, EP/T017112/1). ML and YH are supported by Turing AI Fellowships funded by the UK Research and Innovation (grant no. EP/V030302/1, EP/V020579/1). References - Arana-Catania M., Kochkina E., Zubiaga A., Liakata M., Procter R., He Y.. Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims. NAACL 2022 https://arxiv.org/abs/2205.02596 - Stephen E Robertson, Steve Walker, Susan Jones, Micheline M Hancock-Beaulieu, Mike Gatford, et al. 1995. Okapi at trec-3. Nist Special Publication Sp,109:109. - Fabio Crestani, Mounia Lalmas, Cornelis J Van Rijsbergen, and Iain Campbell. 1998. “is this document relevant?. . . probably” a survey of probabilistic models in information retrieval. ACM Computing Surveys (CSUR), 30(4):528–552. - Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc. - Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020. Document ranking with a pre-trained sequence-to-sequence model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pages 708–718. - Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations. - Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. - Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, et al. 2013. Ontonotes release 5.0 ldc2013t19. Linguistic Data Consortium, Philadelphia, PA, 23. - Limeng Cui and Dongwon Lee. 2020. Coaid: Covid-19 healthcare misinformation dataset. arXiv preprint arXiv:2006.00885. - Yichuan Li, Bohan Jiang, Kai Shu, and Huan Liu. 2020. Mm-covid: A multilingual and multimodal data repository for combating covid-19 disinformation. - Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, and Sameer Singh. 2020. COVIDLies: Detecting COVID-19 misinformation on social media. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics. - Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, and Lucy Lu Wang. 2021. Trec-covid: constructing a pandemic information retrieval test collection. In ACM SIGIR Forum, volume 54, pages 1–12. ACM New York, NY, USA.

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    ZENODO
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    ZENODO
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      ZENODO
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      ZENODO
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    Authors: van Strien, Daniel;

    Model description This model is intended to predict, from the title of a book, whether it is 'fiction' or 'non-fiction'. This model was trained on data created from the Digitised printed books (18th-19th Century) book collection. The datasets in this collection are comprised and derived from 49,455 digitised books (65,227 volumes), mainly from the 19th Century. This dataset is dominated by English language books and includes books in several other languages in much smaller numbers. This model was originally developed for use as part of the Living with Machines project to be able to 'segment' this large dataset of books into different categories based on a 'crude' classification of genre i.e. whether the title was `fiction` or `non-fiction`. The model's training data (discussed more below) primarily consists of 19th Century book titles from the British Library Digitised printed books (18th-19th century) collection. These books have been catalogued according to British Library cataloguing practices. The model is likely to perform worse on any book titles from earlier or later periods. While the model is multilingual, it has training data in non-English book titles; these appear much less frequently. How to use To use this within fastai, first install version 2 of the fastai library. Following the documentation instructions. Once you have fastai installed, you can use the model as follows: from fastai.text.all import load_learner learn = load_learner("20210928-model.pkl") learn.predict("Oliver Twist") Limitations and bias The model was developed based on data from the British Library's Digitised printed books (18th-19th Century) collection. This dataset is not representative of books from the period covered with biases towards certain types (travel) and a likely absence of books that were difficult to digitise. The formatting of the British Library books corpus titles may differ from other collections, resulting in worse performance on other collections. It is recommended to evaluate the performance of the model before applying it to your own data. Likely, this model won't perform well for contemporary book titles without further fine-tuning. Training data The training data for this model will be available from the British Libary Research Repository shortly. The training data was created using the Zooniverse platform. British Library cataloguers carried out the majority of the annotations used as training data. More information on the process of creating the training data will be available soon. Training procedure Model training was carried out using the fastai library version 2.5.2. The notebook using for training the model will be available at: https://github.com/Living-with-machines/bl-books-genre-prediction Eval result The model was evaluated on a held out test set: precision recall f1-score support Fiction 0.91 0.88 0.90 296 Non-fiction 0.94 0.95 0.95 554 accuracy 0.93 850 macro avg 0.93 0.92 0.92 850 weighted avg 0.93 0.93 0.93 850

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    ZENODO
    Dataset . 2021
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    Dataset . 2021
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    Authors: Xia Zeng; Arkaitz Zubiaga;

    As part of an automated fact-checking pipeline, the claim verification task consists in determining if a claim is supported by an associated piece of evidence. The complexity of gathering labelled claim-evidence pairs leads to a scarcity of datasets, particularly when dealing with new domains. In this article, we introduce Semantic Embedding Element-wise Difference (SEED), a novel vector-based method to few-shot claim verification that aggregates pairwise semantic differences for claim-evidence pairs. We build on the hypothesis that we can simulate class representative vectors that capture average semantic differences for claim-evidence pairs in a class, which can then be used for classification of new instances. We compare the performance of our method with competitive baselines including fine-tuned Bidirectional Encoder Representations from Transformers (BERT)/Robustly Optimized BERT Pre-training Approach (RoBERTa) models, as well as the state-of-the-art few-shot claim verification method that leverages language model perplexity. Experiments conducted on the Fact Extraction and VERification (FEVER) and SCIFACT datasets show consistent improvements over competitive baselines in few-shot settings. Our code is available.

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    PeerJ Computer Science
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    Authors: Xingwei Tan; Gabriele Pergola; Yulan He;

    This is the code of EMNLP 2021 main track long paper "Extracting Event Temporal Relations via Hyperbolic Geometry". The paper proposed two hyperbolic-based approaches for the event temporal relation extraction task, which is an Event-centric Natural Language Understanding task.

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    Authors: Zhu, Lixing; He, Yulan; Zhou, Deyu;

    topical_wordvec_models You first need to create a save folder for training. Download the [saved model](https://topicvecmodels.s3.eu-west-2.amazonaws.com/save/47/model) and place it in ./save/47/ to run the trained model. To construct the training set, refer to https://github.com/somethingx02/topical_wordvec_model please. Trained [wordvecs](https://topicvecmodels.s3.eu-west-2.amazonaws.com/save/47/aggrd_all_wordrep.txt).

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    Authors: Kocijan, V;

    With appropriate pre-training on unstructured text, larger and more accurate neural network models can be trained. Unfortunately, unstructured pre-training data may contain undesired societal biases, which a model may mimic and amplify. This thesis focuses on both improving unsupervised pre-training and developing diagnostics of obtained pre-trained models for potential undesired behaviour. Pre-training and diagnostics are done on two tasks: coreference resolution and knowledge base completion. For both of them, a novel task-specific method for unsupervised pre-training is introduced. Then, the obtained models are analysed for potential undesired behaviour by evaluating them on relevant datasets, focusing on gender bias in particular. Two novel pre-training datasets for coreference resolution are introduced, MaskedWiki and WikiCREM. By fine-tuning on these datasets, state-of-the-art performance on multiple benchmarks is achieved, including on the Winograd Schema Challenge, a commonsense reasoning benchmark that requires a lot of background knowledge. The obtained pre-trained models are then evaluated on the Gap benchmark. On this benchmark, potentially problematic patterns in the test set are demonstrated. To remove these undesired patterns, a novel test sample weighting method and a proof of its correctness are introduced. A method for pre-training in knowledge base completion is introduced, the first of its kind, significantly improving the results on multiple smaller datasets. The obtained models outperform much larger and highly trained models, which are trained on more general language-modelling tasks. To better understand the behaviour of the obtained models for knowledge base completion, the first diagnostic dataset for pre-trained knowledge base completion models is introduced, demonstrating how stereotypes in the pre-training data can affect the predictions of a model on the target knowledge base. The future developments of both task-specific pre-training and bias detection are discussed, motivating future research directions in the field.

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