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Introduction 트랜스포머는 긴 시퀀스는 처리하지 못한다는 한계를 가지고 있음 이유는 시퀀스 길이에 O(n^…","frontmatter":{"title":"Sooftware NLP - Longformer Paper Review","excerpt":null,"tags":["nlp","paper"],"date":"2021-02-06T23:46:37.121Z","image":{"childImageSharp":{"gatsbyImageData":{"layout":"fullWidth","backgroundColor":"#f8f8f8","images":{"fallback":{"src":"/static/ef32af94bd92c05cbfebe0cb00c3966e/597e6/longformer.png","srcSet":"/static/ef32af94bd92c05cbfebe0cb00c3966e/597e6/longformer.png 512w","sizes":"100vw"},"sources":[{"srcSet":"/static/ef32af94bd92c05cbfebe0cb00c3966e/3d2a6/longformer.webp 512w","type":"image/webp","sizes":"100vw"}]},"width":1,"height":0.8046875000000001}}},"author":[{"id":"Soohwan Kim","bio":"Co-founder/A.I. engineer at TUNiB.","avatar":{"childImageSharp":{"gatsbyImageData":{"layout":"fullWidth","backgroundColor":"#282838","images":{"fallback":{"src":"/static/a9e6b445142b247ee4cfa66155398bb2/0d6f4/soohwan.png","srcSet":"/static/a9e6b445142b247ee4cfa66155398bb2/248f9/soohwan.png 40w,\n/static/a9e6b445142b247ee4cfa66155398bb2/fd435/soohwan.png 80w,\n/static/a9e6b445142b247ee4cfa66155398bb2/0d6f4/soohwan.png 120w","sizes":"100vw"},"sources":[{"srcSet":"/static/a9e6b445142b247ee4cfa66155398bb2/e7f45/soohwan.webp 40w,\n/static/a9e6b445142b247ee4cfa66155398bb2/589ec/soohwan.webp 80w,\n/static/a9e6b445142b247ee4cfa66155398bb2/71a38/soohwan.webp 120w","type":"image/webp","sizes":"100vw"}]},"width":1,"height":0.6833333333333333}}}}]},"fields":{"readingTime":{"text":"4 min read"},"layout":"post","slug":"/longformer/"}}},{"node":{"excerpt":"EMNLP Paper Review: Speech Adaptive Feature Selection for End-to-End Speech Translation (Biao Zhang et al) Incremental Text-to-Speech…","frontmatter":{"title":"Sooftware Speech - EMNLP Paper Review: Speech","excerpt":null,"tags":["speech","paper"],"date":"2020-12-08T10:00:00.000Z","image":{"childImageSharp":{"gatsbyImageData":{"layout":"fullWidth","backgroundColor":"#f8f8f8","images":{"fallback":{"src":"/static/3020a90c23b0e5a906d9e9d75523071a/5a68f/2020-emnlp.png","srcSet":"/static/3020a90c23b0e5a906d9e9d75523071a/1206c/2020-emnlp.png 750w,\n/static/3020a90c23b0e5a906d9e9d75523071a/c1998/2020-emnlp.png 1080w,\n/static/3020a90c23b0e5a906d9e9d75523071a/c6087/2020-emnlp.png 1366w,\n/static/3020a90c23b0e5a906d9e9d75523071a/5a68f/2020-emnlp.png 1920w","sizes":"100vw"},"sources":[{"srcSet":"/static/3020a90c23b0e5a906d9e9d75523071a/3e1c3/2020-emnlp.webp 750w,\n/static/3020a90c23b0e5a906d9e9d75523071a/bbc54/2020-emnlp.webp 1080w,\n/static/3020a90c23b0e5a906d9e9d75523071a/72682/2020-emnlp.webp 1366w,\n/static/3020a90c23b0e5a906d9e9d75523071a/97f4c/2020-emnlp.webp 1920w","type":"image/webp","sizes":"100vw"}]},"width":1,"height":0.41875}}},"author":[{"id":"Soohwan Kim","bio":"Co-founder/A.I. engineer at TUNiB.","avatar":{"childImageSharp":{"gatsbyImageData":{"layout":"fullWidth","backgroundColor":"#282838","images":{"fallback":{"src":"/static/a9e6b445142b247ee4cfa66155398bb2/0d6f4/soohwan.png","srcSet":"/static/a9e6b445142b247ee4cfa66155398bb2/248f9/soohwan.png 40w,\n/static/a9e6b445142b247ee4cfa66155398bb2/fd435/soohwan.png 80w,\n/static/a9e6b445142b247ee4cfa66155398bb2/0d6f4/soohwan.png 120w","sizes":"100vw"},"sources":[{"srcSet":"/static/a9e6b445142b247ee4cfa66155398bb2/e7f45/soohwan.webp 40w,\n/static/a9e6b445142b247ee4cfa66155398bb2/589ec/soohwan.webp 80w,\n/static/a9e6b445142b247ee4cfa66155398bb2/71a38/soohwan.webp 120w","type":"image/webp","sizes":"100vw"}]},"width":1,"height":0.6833333333333333}}}}]},"fields":{"readingTime":{"text":"4 min read"},"layout":"","slug":"/2020 EMNLP Speech Paper Review/"}}},{"node":{"excerpt":"Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism ​ Mohammad Shoeybi et al. 2019. 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One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech Paper Review","excerpt":null,"tags":["speech","tts","paper"],"date":"2020-10-14T10:00:00.000Z","image":{"childImageSharp":{"gatsbyImageData":{"layout":"fullWidth","backgroundColor":"#f8f8f8","images":{"fallback":{"src":"/static/deca33714347f50cf9f1b33b2db865ef/7189c/multilingual-tts.png","srcSet":"/static/deca33714347f50cf9f1b33b2db865ef/cefb5/multilingual-tts.png 750w,\n/static/deca33714347f50cf9f1b33b2db865ef/c1615/multilingual-tts.png 1080w,\n/static/deca33714347f50cf9f1b33b2db865ef/7189c/multilingual-tts.png 1280w","sizes":"100vw"},"sources":[{"srcSet":"/static/deca33714347f50cf9f1b33b2db865ef/da87f/multilingual-tts.webp 750w,\n/static/deca33714347f50cf9f1b33b2db865ef/bd382/multilingual-tts.webp 1080w,\n/static/deca33714347f50cf9f1b33b2db865ef/2dc0b/multilingual-tts.webp 1280w","type":"image/webp","sizes":"100vw"}]},"width":1,"height":0.328125}}},"author":[{"id":"Soohwan Kim","bio":"Co-founder/A.I. engineer at TUNiB.","avatar":{"childImageSharp":{"gatsbyImageData":{"layout":"fullWidth","backgroundColor":"#282838","images":{"fallback":{"src":"/static/a9e6b445142b247ee4cfa66155398bb2/0d6f4/soohwan.png","srcSet":"/static/a9e6b445142b247ee4cfa66155398bb2/248f9/soohwan.png 40w,\n/static/a9e6b445142b247ee4cfa66155398bb2/fd435/soohwan.png 80w,\n/static/a9e6b445142b247ee4cfa66155398bb2/0d6f4/soohwan.png 120w","sizes":"100vw"},"sources":[{"srcSet":"/static/a9e6b445142b247ee4cfa66155398bb2/e7f45/soohwan.webp 40w,\n/static/a9e6b445142b247ee4cfa66155398bb2/589ec/soohwan.webp 80w,\n/static/a9e6b445142b247ee4cfa66155398bb2/71a38/soohwan.webp 120w","type":"image/webp","sizes":"100vw"}]},"width":1,"height":0.6833333333333333}}}}]},"fields":{"readingTime":{"text":"4 min read"},"layout":"","slug":"/one-model-many-langs/"}}},{"node":{"excerpt":"RoBERTa paper / code Abstract BERT를 제대로 학습시키는 법을 제안 BERT는 엄청난 모델이지만, Original BERT 논문에서 하이퍼파라미터에 대한 실험이 제대로 진행되지 않음 BERT…","frontmatter":{"title":"Sooftware NLP - 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Google Inc. INTERSPEECH, 2020 Reference Conformer…","frontmatter":{"title":"Sooftware Speech - Conformer Paper Review","excerpt":null,"tags":["speech","paper"],"date":"2020-08-30T10:00:00.000Z","image":{"childImageSharp":{"gatsbyImageData":{"layout":"fullWidth","backgroundColor":"#f8f8f8","images":{"fallback":{"src":"/static/5ca9b355b1dc4393c360f527478a3ba2/503d5/conformer.png","srcSet":"/static/5ca9b355b1dc4393c360f527478a3ba2/bfaac/conformer.png 750w,\n/static/5ca9b355b1dc4393c360f527478a3ba2/abf2b/conformer.png 1080w,\n/static/5ca9b355b1dc4393c360f527478a3ba2/6c19a/conformer.png 1366w,\n/static/5ca9b355b1dc4393c360f527478a3ba2/503d5/conformer.png 1890w","sizes":"100vw"},"sources":[{"srcSet":"/static/5ca9b355b1dc4393c360f527478a3ba2/1e5e2/conformer.webp 750w,\n/static/5ca9b355b1dc4393c360f527478a3ba2/77f81/conformer.webp 1080w,\n/static/5ca9b355b1dc4393c360f527478a3ba2/b9a60/conformer.webp 1366w,\n/static/5ca9b355b1dc4393c360f527478a3ba2/2837d/conformer.webp 1890w","type":"image/webp","sizes":"100vw"}]},"width":1,"height":0.6888888888888889}}},"author":[{"id":"Soohwan Kim","bio":"Co-founder/A.I. engineer at TUNiB.","avatar":{"childImageSharp":{"gatsbyImageData":{"layout":"fullWidth","backgroundColor":"#282838","images":{"fallback":{"src":"/static/a9e6b445142b247ee4cfa66155398bb2/0d6f4/soohwan.png","srcSet":"/static/a9e6b445142b247ee4cfa66155398bb2/248f9/soohwan.png 40w,\n/static/a9e6b445142b247ee4cfa66155398bb2/fd435/soohwan.png 80w,\n/static/a9e6b445142b247ee4cfa66155398bb2/0d6f4/soohwan.png 120w","sizes":"100vw"},"sources":[{"srcSet":"/static/a9e6b445142b247ee4cfa66155398bb2/e7f45/soohwan.webp 40w,\n/static/a9e6b445142b247ee4cfa66155398bb2/589ec/soohwan.webp 80w,\n/static/a9e6b445142b247ee4cfa66155398bb2/71a38/soohwan.webp 120w","type":"image/webp","sizes":"100vw"}]},"width":1,"height":0.6833333333333333}}}}]},"fields":{"readingTime":{"text":"4 min read"},"layout":"","slug":"/conformer/"}}},{"node":{"excerpt":"ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact Centers image 논문링크 2020-04-2…","frontmatter":{"title":"Sooftware Speech - 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SpecAugment Paper Review","excerpt":null,"tags":["speech","paper"],"date":"2020-01-12T10:00:00.000Z","image":{"childImageSharp":{"gatsbyImageData":{"layout":"fullWidth","backgroundColor":"#f8f8f8","images":{"fallback":{"src":"/static/a5b61e2900f2360216061fe9e8f8f640/4df46/specaugment.png","srcSet":"/static/a5b61e2900f2360216061fe9e8f8f640/4df46/specaugment.png 620w","sizes":"100vw"},"sources":[{"srcSet":"/static/a5b61e2900f2360216061fe9e8f8f640/cd871/specaugment.webp 620w","type":"image/webp","sizes":"100vw"}]},"width":1,"height":0.5919354838709677}}},"author":[{"id":"Soohwan Kim","bio":"Co-founder/A.I. engineer at TUNiB.","avatar":{"childImageSharp":{"gatsbyImageData":{"layout":"fullWidth","backgroundColor":"#282838","images":{"fallback":{"src":"/static/a9e6b445142b247ee4cfa66155398bb2/0d6f4/soohwan.png","srcSet":"/static/a9e6b445142b247ee4cfa66155398bb2/248f9/soohwan.png 40w,\n/static/a9e6b445142b247ee4cfa66155398bb2/fd435/soohwan.png 80w,\n/static/a9e6b445142b247ee4cfa66155398bb2/0d6f4/soohwan.png 120w","sizes":"100vw"},"sources":[{"srcSet":"/static/a9e6b445142b247ee4cfa66155398bb2/e7f45/soohwan.webp 40w,\n/static/a9e6b445142b247ee4cfa66155398bb2/589ec/soohwan.webp 80w,\n/static/a9e6b445142b247ee4cfa66155398bb2/71a38/soohwan.webp 120w","type":"image/webp","sizes":"100vw"}]},"width":1,"height":0.6833333333333333}}}}]},"fields":{"readingTime":{"text":"20 min read"},"layout":"","slug":"/specaugment/"}}},{"node":{"excerpt":"Deep Speech: Scaling up end-to-end speech recognition title https://arxiv.org/pdf/1412.5567.pdf (Awni Hannun et al. 2014) Abstract…","frontmatter":{"title":"Sooftware Speech - DeepSpeech Paper Review","excerpt":null,"tags":["speech","paper"],"date":"2019-11-11T10:00:00.000Z","image":{"childImageSharp":{"gatsbyImageData":{"layout":"fullWidth","backgroundColor":"#f8f8f8","images":{"fallback":{"src":"/static/360f6a5bb171f9136086a6bf108b401d/2241d/deepspeech.png","srcSet":"/static/360f6a5bb171f9136086a6bf108b401d/2241d/deepspeech.png 541w","sizes":"100vw"},"sources":[{"srcSet":"/static/360f6a5bb171f9136086a6bf108b401d/1edf8/deepspeech.webp 541w","type":"image/webp","sizes":"100vw"}]},"width":1,"height":0.9149722735674677}}},"author":[{"id":"Soohwan Kim","bio":"Co-founder/A.I. engineer at TUNiB.","avatar":{"childImageSharp":{"gatsbyImageData":{"layout":"fullWidth","backgroundColor":"#282838","images":{"fallback":{"src":"/static/a9e6b445142b247ee4cfa66155398bb2/0d6f4/soohwan.png","srcSet":"/static/a9e6b445142b247ee4cfa66155398bb2/248f9/soohwan.png 40w,\n/static/a9e6b445142b247ee4cfa66155398bb2/fd435/soohwan.png 80w,\n/static/a9e6b445142b247ee4cfa66155398bb2/0d6f4/soohwan.png 120w","sizes":"100vw"},"sources":[{"srcSet":"/static/a9e6b445142b247ee4cfa66155398bb2/e7f45/soohwan.webp 40w,\n/static/a9e6b445142b247ee4cfa66155398bb2/589ec/soohwan.webp 80w,\n/static/a9e6b445142b247ee4cfa66155398bb2/71a38/soohwan.webp 120w","type":"image/webp","sizes":"100vw"}]},"width":1,"height":0.6833333333333333}}}}]},"fields":{"readingTime":{"text":"10 min read"},"layout":"","slug":"/deepspeech/"}}},{"node":{"excerpt":"「Listen, Attend and Spell」 Review title https://arxiv.org/abs/1508.01211  (William Chan et al. 2015)  Introduction 어텐션 기반 Seq2seq…","frontmatter":{"title":"Sooftware Speech - 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