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","date":"05/26/2022","readTime":"7 minute read","image":{"image":{"url":"https://webassets.rws.com/images/other-1200x610_tcm228-195137.jpg","fileSize":0,"id":"195137"},"mobileImage":{"url":"https://webassets.rws.com/images/other-1200x610_tcm228-195137.jpg","fileSize":0,"id":"195137"}},"author":"Kyle Shaffer","topic":"Research","tags":["other-miscellaneous"],"tagTranslations":["Other / Miscellaneous"],"dateMonth":"5","dateYear":"2022","displayDate":"26 May 2022","id":"tcm:228-205106-64","linkUrl":{"url":"/language-weaver/blog/issue-182-DYLE/","isExternal":false,"isVideo":false},"publicationDate":"2022-12-20T09:31:20.159Z","title":"Issue #182 - DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization","publicationId":"228","itemType":"blog","itemTypeTranslation":"Blog"},{"articleTitle":"Issue #181 - Incorporating Source text in Automatic Post-Editing ","subTitle":"Karen looks at work by Pal et al., 2019, which incorporates the source text in an Automatic Post-Editing (APE) architecture, and also looks at how Wang et al., 2021 apply it to train a context-aware APE system.","date":"05/19/2022","readTime":"5 minute read","image":{"image":{"url":"https://webassets.rws.com/images/other-1200x610_tcm228-195137.jpg","fileSize":0,"id":"195137"},"mobileImage":{"url":"https://webassets.rws.com/images/other-1200x610_tcm228-195137.jpg","fileSize":0,"id":"195137"}},"author":"Dr. Karin Sim","topic":"Research","tags":["other-miscellaneous"],"tagTranslations":["Other / Miscellaneous"],"dateMonth":"5","dateYear":"2022","displayDate":"19 May 2022","id":"tcm:228-204729-64","linkUrl":{"url":"/language-weaver/blog/issue-181-incorporating-source-text-in-APE/","isExternal":false,"isVideo":false},"publicationDate":"2022-12-20T09:31:19.779Z","title":"Issue #181 - Incorporating Source text in Automatic Post-Editing","publicationId":"228","itemType":"blog","itemTypeTranslation":"Blog"},{"articleTitle":"Issue #180 - Detecting Over- and Under-translations with Contrastive Conditioning ","subTitle":"This week, Danielle looks at work by Vamvas & Sennrich (2022) who explore two ways to evaluate MT for over- and under-translation (where a translation has either added or omitted information). ","date":"05/12/2022","readTime":"7 minute read","image":{"image":{"url":"https://webassets.rws.com/images/evaluation-1200x610_tcm228-195132.jpg","fileSize":0,"id":"195132"},"mobileImage":{"url":"https://webassets.rws.com/images/evaluation-1200x610_tcm228-195132.jpg","fileSize":0,"id":"195132"}},"author":"Dr. Danielle Saunders","topic":"Research","tags":["evaluation"],"tagTranslations":["Evaluation"],"dateMonth":"5","dateYear":"2022","displayDate":"12 May 2022","id":"tcm:228-204453-64","linkUrl":{"url":"/language-weaver/blog/issue-180-detecting-over-and-under-translations/","isExternal":false,"isVideo":false},"publicationDate":"2022-12-20T09:31:19.452Z","title":"Issue #180 - Detecting Over- and Under-translations with Contrastive Conditioning","publicationId":"228","itemType":"blog","itemTypeTranslation":"Blog"},{"articleTitle":"Issue #179 - Charformer: Fast Character Transformers via Gradient-based Subwords","subTitle":"This week, Patrik looks at the Charformer model, proposed by Tay et al. (2022), in which subword tokenization is learned as a gradient-based, end-to-end task. ","date":"05/05/2022","readTime":"5 minute read","image":{"image":{"url":"https://webassets.rws.com/images/model-improvement-1200x610_tcm228-195135.jpg","fileSize":0,"id":"195135"},"mobileImage":{"url":"https://webassets.rws.com/images/model-improvement-1200x610_tcm228-195135.jpg","fileSize":0,"id":"195135"}},"author":"Dr. Patrik Lambert","topic":"Research","tags":["model-improvement","robustness"],"tagTranslations":["Model improvement","Robustness"],"dateMonth":"5","dateYear":"2022","displayDate":"05 May 2022","id":"tcm:228-204336-64","linkUrl":{"url":"/language-weaver/blog/Issue-179-Charformer-model/","isExternal":false,"isVideo":false},"publicationDate":"2022-12-20T09:31:18.159Z","title":"Issue #179 - Charformer: Fast Character Transformers via Gradient-based Subwords","publicationId":"228","itemType":"blog","itemTypeTranslation":"Blog"},{"articleTitle":"Issue #178 - Length-based Overfitting in Transformer Models","subTitle":"This week, Jingyi reviews a paper by Variš and Bojar (2021) which suggests that the reason neural-based MT systems normally perform worse on very long sequences might be due to the mismatch between the length distributions of training data and evaluation set.","date":"04/28/2022","readTime":"7 minute read","image":{"image":{"url":"https://webassets.rws.com/images/model-improvement-1200x610_tcm228-195135.jpg","fileSize":0,"id":"195135"},"mobileImage":{"url":"https://webassets.rws.com/images/model-improvement-1200x610_tcm228-195135.jpg","fileSize":0,"id":"195135"}},"author":"Dr. Jingyi Han","topic":"Research","tags":["model-improvement"],"tagTranslations":["Model improvement"],"dateMonth":"4","dateYear":"2022","displayDate":"28 Apr 2022","id":"tcm:228-204180-64","linkUrl":{"url":"/language-weaver/blog/Issue-178-Length-based-Overfitting-in-Transformer/","isExternal":false,"isVideo":false},"publicationDate":"2022-12-20T09:31:17.874Z","title":"Issue #178 - Length-based Overfitting in Transformer Models","publicationId":"228","itemType":"blog","itemTypeTranslation":"Blog"},{"articleTitle":"Issue #177 - Parallel Corpus Filtering via Pre-trained Language Models ","subTitle":"In today’s blog post, we will take a look at the work of Zhang et al. (2020) who propose a novel approach for parallel corpus filtering. ","date":"04/21/2022","readTime":"7 minute read","image":{"image":{"url":"https://webassets.rws.com/images/other-1200x610_tcm228-195137.jpg","fileSize":0,"id":"195137"},"mobileImage":{"url":"https://webassets.rws.com/images/other-1200x610_tcm228-195137.jpg","fileSize":0,"id":"195137"}},"author":"Akshai Ramesh","topic":"Research","tags":["other-miscellaneous"],"tagTranslations":["Other / Miscellaneous"],"dateMonth":"4","dateYear":"2022","displayDate":"21 Apr 2022","id":"tcm:228-203923-64","linkUrl":{"url":"/language-weaver/blog/Issue-177-Parallel-Corpus-Filtering-LMs/","isExternal":false,"isVideo":false},"publicationDate":"2022-12-20T09:31:17.281Z","title":"Issue #177 - Parallel Corpus Filtering via Pre-trained Language Models","publicationId":"228","itemType":"blog","itemTypeTranslation":"Blog"},{"articleTitle":"Issue #176 - Improving Machine Translation of Rare and Unseen Word Senses","subTitle":"Katrina discusses a framework proposed by Hangya et al., 2021 that contextually mines back-translations of multi-sense words to increase frequency of rare sense words in training data and improve their translation. ","date":"04/14/2022","readTime":"7 minute read","image":{"image":{"url":"https://webassets.rws.com/images/terminology-vocabulary-1200x610_tcm228-195140.jpg","fileSize":0,"id":"195140"},"mobileImage":{"url":"https://webassets.rws.com/images/terminology-vocabulary-1200x610_tcm228-195140.jpg","fileSize":0,"id":"195140"}},"author":"Katrina Olsen","topic":"Research","tags":["terminology-vocabulary-coverage"],"tagTranslations":["Terminology / Vocabulary coverage"],"dateMonth":"4","dateYear":"2022","displayDate":"14 Apr 2022","id":"tcm:228-203644-64","linkUrl":{"url":"/language-weaver/blog/Issue-176-Improving-MT-of-Rare-and-Unseen-Word-Senses/","isExternal":false,"isVideo":false},"publicationDate":"2022-12-20T09:31:15.782Z","title":"Issue #176 - Improving MT of Rare and Unseen Word Senses","publicationId":"228","itemType":"blog","itemTypeTranslation":"Blog"},{"articleTitle":"Issue #175 - Twenty Years of Neural Language Models and Counting ","subTitle":"Melissa looks at work by Sun & Iyyer (2021) that reconsiders the original design of neural LMs from almost 20 years ago and examines its relevance to current state-of-the-art LMs. ","date":"04/07/2022","readTime":"7 minute read","image":{"image":{"url":"https://webassets.rws.com/images/language-modelling-1200x610_tcm228-195133.jpg","fileSize":0,"id":"195133"},"mobileImage":{"url":"https://webassets.rws.com/images/language-modelling-1200x610_tcm228-195133.jpg","fileSize":0,"id":"195133"}},"author":"Dr. Melissa Roemmele","topic":"Research","tags":["language-modelling"],"tagTranslations":["Language modelling"],"dateMonth":"4","dateYear":"2022","displayDate":"07 Apr 2022","id":"tcm:228-203320-64","linkUrl":{"url":"/language-weaver/blog/Issue-175-20-Years-of-Neural-Language-Models/","isExternal":false,"isVideo":false},"publicationDate":"2022-12-20T09:31:15.48Z","title":"Issue #175 - Twenty Years of Neural Language Models and Counting","publicationId":"228","itemType":"blog","itemTypeTranslation":"Blog"},{"articleTitle":"Issue #174 - Are Pre-trained Convolutions Better than Pre-trained Transformers? ","subTitle":"Kyle looks at the paper “Are Pre-trained Convolutions Better Than Pre-trained Transformers?” by Tay et al, (2021), where they seek to better understand tradeoffs between neural architectures and pre-training regimes by investigating the effect of large-scale pre-training of convolutional NLP models. ","date":"03/31/2022","readTime":"7 minute read","image":{"image":{"url":"https://webassets.rws.com/images/other-1200x610_tcm228-195137.jpg","fileSize":0,"id":"195137"},"mobileImage":{"url":"https://webassets.rws.com/images/other-1200x610_tcm228-195137.jpg","fileSize":0,"id":"195137"}},"author":"Kyle Shaffer","topic":"Research","tags":["other-miscellaneous"],"tagTranslations":["Other / Miscellaneous"],"dateMonth":"3","dateYear":"2022","displayDate":"31 Mar 2022","id":"tcm:228-202871-64","linkUrl":{"url":"/language-weaver/blog/Issue-174-Pre-trained-convolutions-vs-Pre-trained-transformers/","isExternal":false,"isVideo":false},"publicationDate":"2022-12-20T09:31:14.856Z","title":"Issue #174 - Are Pre-trained Convolutions Better than Pre-trained Transformers?","publicationId":"228","itemType":"blog","itemTypeTranslation":"Blog"}]},"searchEndpoint":"https://siteapi.rws.com/api/search/v1.0/query/","blogTopicUrl":"/blog/categories","blogTagUrl":"/blog/tags"},"template":{"content":{},"id":"FilteredBlogList","extendedProperties":{}},"id":"191056","extendedProperties":{}},{"content":{"marketoForm":{"activityCampaignInfo":{},"activityLanguageCode":"en-US","activityLanguage":"en","activityType":"Campaign Form","activitySubject":"Campaign Form: NM Weekly Blog Newsletter","formId":"1a847cfd-0c09-4c28-a72f-47251ca56dbf","cookieExpirationDays":"30","thankYouMessage":"Thank you for filling out the form.","formProvider":"HubSpot"},"title":"Subscribe to Language Weaver Blog","portalId":"147991","pleaseSelectLabel":"Please Select"},"template":{"content":{},"id":"HubspotFormBlock","extendedProperties":{}},"id":"191186","extendedProperties":{}}],"id":"Main","extendedProperties":{}},{"regions":[{"regions":[],"fragments":[],"id":"Header","extendedProperties":{}}],"fragments":[],"id":"Header","extendedProperties":{}},{"regions":[{"regions":[],"fragments":[{"content":{"linkList":[{"title":"RWS","description":"\n

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