A Paper List for Speech Translation

Overview

A Paper List for Speech Translation

This is a paper list for speech translation.

Keyword: Speech Translation, Spoken Language Processing, Natural Language Processing

Paper List

Survey

  • Speech Translation and the End-to-End Promise:Taking Stock of Where We Are, ACL-2020 theme track, [paper]
  • Multimodal Machine Translation through Visuals and Speech, Machine Translation journal-2020 (Springer), [paper]

Codebase

  • ESPnet-ST: All-in-One Speech Translation Toolkit, ACL-2020 Demo, [paper], [code]
  • FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ, AACL-2020 demo, [paper], [code]
  • NeurST: Neural Speech Translation Toolkit, Arxiv-2020, [paper], [code]

Dataset

  • Construction and Utilization of Bilingual Speech Corpus for Simultaneous Machine Interpretation Research, InterSpeech-2005,[paper]
  • Approach to Corpus-based Interpreting Studies: Developing EPIC (European Parliament Interpreting Corpus), MuTra-2005, [paper]
  • Automatic Translation from Parallel Speech: Simultaneous Interpretation as MT Training Data, ASRU-2009, [paper]
  • The KIT Lecture Corpus for Speech Translation, LREC-2012, [paper]
  • Improved Speech-to-Text Translation with the Fisher and Callhome Spanish–English Speech Translation Corpus, IWSLT-2013, [paper]
  • Collection of a Simultaneous Translation Corpus for Comparative Analysis, LREC-2014, [paper]
  • Microsoft Speech Language Translation (MSLT) Corpus: The IWSLT 2016 release for English, French and German, IWSLT-2016, [paper]
  • The Microsoft Speech Language Translation (MSLT) Corpus for Chinese and Japanese: Conversational Test data for Machine Translation and Speech Recognition, Machine_Translation-2017, [paper]
  • Amharic-English Speech Translation in Tourism Domain, SCNLP-2017, [paper]
  • A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiment, LREC-2018, [paper]
  • Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation, LREC-2018, [paper]
  • A Small Griko-Italian Speech Translation Corpus, SLTU-2019, [paper]
  • MuST-C: a Multilingual Speech Translation Corpus, NAACL-2019, [paper]
  • MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible, Arxiv-2019, [paper]
  • How2: A Large-scale Dataset for Multimodal Language Understanding, NIPS-2018, [paper]
  • LibriVoxDeEn: A Corpus for German-to-English Speech Translation and Speech Recognition, LREC-2020, [paper]
  • Clotho: An Audio Captioning Dataset, Arxiv-2019, [paper]
  • Europarl-St: A Multilingual Corpus For Speech Translation Of Parliamentary Debates, ICASSP-2020, [paper]
  • CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus, Arxiv-2020, [paper]
  • MuST-Cinema: a Speech-to-Subtitles corpus, Arxiv-2020, [paper]
  • CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus, Arxiv-2020, [paper], [code]
  • The Multilingual TEDx Corpus for Speech Recognition and Translation, Arxiv-2021, [paper]

Pipeline ST

  • Phonetically-Oriented Word Error Alignment for Speech Recognition Error Analysis in Speech Translation, ASRU-2015,[paper]
  • Learning a Translation Model from Word Lattices, InterSpeech-2016, [paper]
  • Learning a Lexicon and Translation Model from Phoneme Lattices, EMNLP-2016, [paper]
  • Neural Lattice-to-Sequence Models for Uncertain Inputs, EMNLP-2017, [paper]
  • Using Spoken Word Posterior Features in Neural Machine Translation, IWSLT-2018, [paper]
  • Towards robust neural machine translation, ACL-2018, [paper]
  • Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors, InterSpeech-2019, [paper]
  • Lattice Transformer for Speech Translation, ACL-2019, [paper]
  • Self-Attentional Models for Lattice Inputs, ACL-2019, [paper]
  • Breaking the Data Barrier: Towards Robust Speech Translation via Adversarial Stability Training, IWSLT-2019, [paper]
  • Neural machine translation with acoustic embedding, ASRU-2019
  • Machine Translation in Pronunciation Space, Arxiv-2020, [paper]
  • Diversity by Phonetics and its Application in Neural Machine Translation, AAAI-2020, [paper]
  • Robust Neural Machine Translation for Clean and Noisy Speech Transcripts, IWSLT-2019, [paper]
  • ELITR Non-Native Speech Translation at IWSLT 2020, IWSLT-2020, [paper]
  • Subtitles to Segmentation: Improving Low-Resource Speech-to-Text Translation Pipelines, [email protected] 2020, [paper]
  • Cascaded Models With Cyclic Feedback For Direct Speech Translation, Arxiv-2020, [paper]
  • Sentence Boundary Augmentation For Neural Machine Translation Robustness, Arxiv-2020, [paper]
  • A Technical Report: But Speech Translation Systems, Arxiv-2020, [paper]
  • Direct Segmentation Models for Streaming Speech Translation, EMNLP-2020, [paper]

End-to-end ST

  • Towards Speech Translation of Non Written Languages, IEEE-2006, [paper]
  • Towards speech-to-text translation without speech recognition, EACL-2017, [paper]
  • Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation, NIPS-2016, [paper]
  • An Attentional Model for Speech Translation Without Transcription, NAACL-2016, [paper]
  • An Unsupervised Probability Model for Speech-to-Translation Alignment of Low-Resource Languages, EMNLP-2016, [paper]
  • A Case Study on Using Speech-to-translation Alignments for Language Documentation, ComputEL-2017, [paper]
  • Spoken Term Discovery for Language Documentation Using Translations, SCNLP-2017, [paper]
  • Sequence-to-sequence Models Can Directly Translate Foreign Speech, InterSpeech-2017, [paper]
  • Structured-based Curriculum Learning for End-to-end English-Japanese Speech Translation, InterSpeech-2017, [paper]
  • End-to-End Speech Translation with the Transformer, IberSPEECH-2018, [paper]
  • Towards Fluent Translations from Disfluent Speech, SLT-2018, [paper]
  • Low-resource Speech-to-text Translation, InterSpeech-2018, [paper]
  • End-to-End Automatic Speech Translation of Audiobooks, ICASSP-2018, [paper]
  • Tied Multitask Learning for Neural Speech Translation, NAACL-2018, [paper]
  • Towards Unsupervised Speech to Text Translation, ICASSP-2019, [paper]
  • Leveraging Weakly Supervised Data to Improve End-to-End Speech-to-Text Translation, ICASSP-2019, [paper]
  • Towards End-to-end Speech-to-text Translation with Two-pass Decoding, ICASSP-2019, [paper]
  • Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation, TACL-2019, [paper]
  • Direct speech-to-speech translation with a sequence-to-sequence model, InterSpeech-2019, [paper]
  • Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation, TACL-2019, [paper]
  • End-to-End Speech Translation with Knowledge Distillation, InterSpeech-2019, [paper]
  • Fluent Translations from Disfluent Speech in End-to-End Speech Translation, NAACL-2019, [paper]
  • Pre-Training On High-Resource Speech Recognition Improves Low-Resource Speech-To-Text Translation, NAACL-2019, [[paper]
  • Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation, ACL-2019, [paper]
  • Leveraging Out-of-Task Data for End-to-End Automatic Speech Translation, Arxiv-2019, [paper]
  • Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation, AAAI-2020, [paper]
  • Adapting Transformer to End-to-end Spoken Language Translation, InterSpeech-2019, [paper]
  • Unsupervised phonetic and word level discovery for speech to speech translation for unwritten languages, InterSpeech-2019, [paper]
  • Speech-To-Speech Translation Between Untranscribed Unknown Languages, ASRU-2019, [paper]
  • A comparative study on end-to-end speech to text translation, ASRU-2019, [paper]
  • Instance-Based Model Adaptation For Direct Speech Translation, ICASSP-2020, [paper]
  • Analyzing Asr Pretraining For Low-Resource Speech-To-Text Translation, ICASSP-2020, [paper]
  • ON-TRAC Consortium End-to-End Speech Translation Systems for the IWSLT 2019 Shared Task, IWSLT-2019, [paper]
  • Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade, IWSLT-2019, [paper]
  • Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning, ICASSP-2020, [paper]
  • Enhancing Transformer for End-to-end Speech-to-Text Translation, EAMT-2019, [paper]
  • On Using SpecAugment for End-to-End Speech Translation, IWSLT-2019, [paper]
  • Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding, AAAI-2020, [paper]
  • From Speech-To-Speech Translation To Automatic Dubbing, Arxiv-2020, [paper]
  • Skinaugment: Auto-Encoding Speaker Conversions For Automaticspeech Translation, ICASSP-2020, [paper]
  • Curriculum Pre-training for End-to-End Speech Translation, ACL-2020, [paper]
  • Jointly Trained Transformers models for Spoken Language Translation, Arxiv-2020, [paper]
  • Relative Positional Encoding for Speech Recognition and Direct Translation, Arxiv-2020, [paper]
  • Worse WER, but Better BLEU? Leveraging Word Embedding asIntermediate in Multitask End-to-End Speech Translation, ACL-2020, [paper]
  • Phone Features Improve Speech Translation, ACL-2020, [paper]
  • Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection, Arxiv-2020, [paper]
  • End-to-End Speech-Translation with Knowledge Distillation: [email protected], IWSLT2020, [paper]
  • Self-Training for End-to-End Speech Translation, INTERSPEECH2020 (submitted), [paper]
  • CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning, INTERSPEECH2020 (submitted), [paper]
  • Is 42 the Answer to Everything in Subtitling-oriented Speech Translation?, IWSLT2020, [paper]
  • End-To-End Speech Translation With Self-Contained Vocabulary Manipulation, ICASSP2020
  • End-to-End Speech Translation With Transcoding by Multi-Task Learning for Distant Language Pairs, TASLP-2020, [paper]
  • UWSpeech: Speech to Speech Translation for Unwritten Languages, Arxiv-2020, [paper]
  • Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus, ACL-2020, [paper]
  • Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation, INTERSPEECH2020 (submitted), [paper]
  • Self-Supervised Representations Improve End-to-End Speech Translation, Arxiv-2020, [paper]
  • Consistent Transcription and Translation of Speech, TACL-2020, [paper]
  • Contextualized Translation of Automatically Segmented Speech, INTERSPEECH-2020, [paper]
  • On Target Segmentation for Direct Speech Translation, AMTA-2020, [paper]
  • End-to-End Speech Translation with Adversarial Training, WAST-2020, [paper]
  • SDST: Successive Decoding for Speech-to-text Translation, Arxiv-2020, [paper]
  • TED: Triple Supervision Decouples End-to-end Speech-to-text Translation, Arxiv-2020, [paper]
  • Investigating Self-supervised Pre-training for End-to-end Speech Translation, ICML-2020 workshop, [paper], [code]
  • Adaptive Feature Selection for End-to-End Speech Translation, EMNLP2020 Findings, [paper], [code]
  • A General Multi-Task Learning Framework To Leverage Text Data For Speech To Text Tasks, Arxiv-2020, [paper]
  • MAM: Masked Acoustic Modeling for End-to-End Speech-to-Text Translation, Arxiv-2020, [paper]
  • Evaluating Gender Bias In Speech Translation, ICASSP-2021 (submitted), [paper]
  • Cross-Modal Transfer Learning For Multilingual Speech-To-Text Translation, Arxiv-2020, [paper]
  • Bridging the Modality Gap for Speech-to-Text Translation, Arxiv-2020, [paper]
  • Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech Translation, COLING-2020, [paper], [code]
  • Effectively pretraining a speech translation decoder with Machine Translation data, EMNLP-2020, [paper]
  • Tight Integrated End-to-End Training for Cascaded Speech Translation, SLT-2021, [paper]
  • Breeding Gender-aware Direct Speech Translation Systems, COLING-2020, [paper]
  • On Knowledge Distillation for Direct Speech Translation, CLiC-IT-2020, [paper]
  • Streaming Models for Joint Speech Recognition and Translation, EACL-2021, [paper]
  • CTC-based Compression for Direct Speech Translation, EACL-2021, [paper]
  • Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation, Arxiv-2021, [paper]
  • Transformer-Based Direct Speech-To-Speech Translation With Transcoder, SLT-2021, [paper]

End-to-end Streaming ST

  • Simuls2s: End-to-end Simultaneous Speech To Speech Translation, ICLR-2019(under review), [paper]
  • ON-TRAC Consortium for End-to-End and Simultaneous SpeechTranslation Challenge Tasks at IWSLT 2020, IWSLT-2020, [paper]
  • SimulSpeech: End-to-End Simultaneous Speech to Text Translation, ACL-2020, [paper]
  • Streaming Simultaneous Speech Translation With Augmented Memory Transformer, ICASSP-2021(submitted), [paper]
  • SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation, Arxiv-2020, [paper]
  • Simultaneous Speech-To-Speech Translation System With Neural Incremental Asr, Mt, And Tts, Arxiv-2020, [paper]
  • An Empirical Study Of End-To-End Simultaneous Speech Translation Decoding Strategies, ICASSP 2021, [paper]

End-to-end NA ST

  • Orthros: Non-Autoregressive End-To-End Speech Translation With Dual-Decoder, Arxiv-2020, [paper]

Multilingual ST

  • Multilingual End-To-End Speech Translation, ASRU-2019, [paper]
  • One-To-Many Multilingual End-To-End Speech Translation, ASRU-2019, [paper]

Multimodal MT

  • Transformer-based Cascaded Multimodal Speech Translation, Arxiv-2019, [paper]
  • Towards Multimodal Simultaneous Neural Machine Translation, Arxiv-2020, [paper]
  • Towards Automatic Face-to-Face Translation, Arxiv-2020, [paper], [code]
  • Keyframe Segmentation and Positional Encoding for Video-guided Machine Translation Challenge 2020, ALVR-2020, [paper]
  • DeepFuse: HKU’s Multimodal Machine Translation System for VMT’20, ALVR-2020, [paper]
  • Team RUC AI·M3 Technical Report at VMT Challenge 2020: Enhancing Neural Machine Translation with Multimodal Rewards, ALVR-2020, [paper]
  • Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation,EACL-2021,[paper]

Streaming MT

  • Simultaneous translation of lectures and speeches, Machine Translation-2007, [paper]
  • Real-time incremental speech-to-speech translation of dialogs, NAACL-2012, [paper]
  • Incremental segmentation and decoding strategies for simultaneous translation, IJCNLP-2013, [paper]
  • Don't Until the Final Verb Wait: Reinforcement learning for simultaneous machine translation, EMNLP-2014, [paper]
  • Segmentation strategies for streaming speech translation, NAACL-2013, [paper]
  • Optimizing segmentation strategies for simultaneous speech translation, ACL-2014, [paper]
  • Syntax-based simultaneous translation through prediction of unseen syntactic constituents, ACL-IJCNLP-2015, [paper]
  • Simultaneous machine translation using deep reinforcement learning, ICML-2016, [paper]
  • Interpretese vs. translationese: The uniqueness of human strategies in simultaneous interpretation, NAACL-2016, [paper]
  • Can neural machine translation do simultaneous translation?, Arxiv-2016, [paper]
  • Learning to translate in real-time with neural machine translation, EACL-2017, [paper]
  • Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation, NAACL-2018, [paper]
  • Prediction Improves Simultaneous Neural Machine Translation, EMNLP-2018, [paper]
  • STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework, ACL-2019, [paper]
  • Simultaneous Translation with Flexible Policy via Restricted Imitation Learning, ACL-2019, [paper]
  • Monotonic Infinite Lookback Attention for Simultaneous Machine Translation, ACL-2019, [paper]
  • Thinking Slow about Latency Evaluation for Simultaneous Machine Translation, Arxiv-2019, [paper]
  • DuTongChuan: Context-aware Translation Model for Simultaneous Interpreting, Arxiv-2019, [paper]
  • Monotonic Multihead Attention, ICLR-2020(under review), [paper]
  • How To Do Simultaneous Translation Better With Consecutive Neural Machine Translation, Arxiv-2019, [paper]
  • Simultaneous Neural Machine Translation using Connectionist Temporal Classification, Arxiv-2019, [paper]
  • Re-Translation Strategies For Long Form, Simultaneous, Spoken Language Translation, ICASSP-2020, [paper]
  • Learning Coupled Policies for Simultaneous Machine Translation, Arxiv-2020, [paper]
  • Re-translation versus Streaming for Simultaneous Translation, Arxiv-2020, [paper]
  • Efficient Wait-k Models for Simultaneous Machine Translation, Arxiv-2020, [paper]
  • Opportunistic Decoding with Timely Correction for Simultaneous Translation, ACL-2020, [paper]
  • Neural Simultaneous Speech Translation Using Alignment-Based Chunking, IWSLT2020, [paper]
  • Dynamic Masking for Improved Stability in Spoken Language Translation, Arxiv-2020, [paper]
  • Learn to Use Future Informationin Simultaneous Translation, Arxiv-2020, [paper]
  • Presenting Simultaneous Translation in Limited Space, ITAT WAFNL 2020, [paper]
  • Fluent and Low-latency Simultaneous Speech-to-Speech Translation with Self-adaptive Training, EMNLP2020 Findings, [paper]
  • Improving Simultaneous Translation with Pseudo References, Arxiv-2020, [paper]
  • Future-Guided Incremental Transformer for Simultaneous Translation, AAAI-2021, [paper]
  • Faster Re-translation Using Non-Autoregressive Model For Simultaneous Neural Machine Translation, Arxiv-2021, [paper]

Related Works

Automated audio captioning (AAC)

  • Effects Of Word-Frequency Based Pre- Annd Post- Processings For Audio Captioning, DCASE-2020, [paper]

Named Entity Recognition

  • End-to-end Named Entity Recognition from English Speech, INTERSPEECH2020(submitted), [paper]

Text Normalization

  • A Hybrid Text Normalization System Using Multi-Head Self-Attention For Mandarin, ICASSP-2020, [paper]
  • A Unified Sequence-To-Sequence Front-End Model For Mandarin Text-To-Speech Synthesis, ICASSP-2020, [paper]
  • Naturalization of Text by the Insertion of Pauses and Filler Words, Arxiv-2020, [paper]

Disfluency Detection

  • Semi-Supervised Disfluency Detection, COLING-2018, [paper]
  • Adapting Translation Models for Transcript Disfluency Detection, AAAI-2019, [paper]
  • Giving Attention to the Unexpected:Using Prosody Innovations in Disfluency Detection, Arxiv-2019, [paper]
  • Multi-Task Self-Supervised Learning for Disfluency Detection, AAAI-2020, [paper]
  • Improving Disfluency Detection by Self-Training a Self-Attentive Model, Arxiv-2020, [paper]
  • Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection, EMNLP-2020, [paper], [code]
  • Auxiliary Sequence Labeling Tasks For Disfluency Detection, Arxiv-2020, [paper]

Punctuation Prediction

  • Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detection, ICASSP-2020,[paper]
  • Punctuation Prediction in Spontaneous Conversations: Can We Mitigate ASR Errors with Retrofitted Word Embeddings, INTERSPEECH-2020 (submitted), [paper]
  • Multimodal Semi-supervised Learning Framework for Punctuation Prediction in Conversational Speech, INTERSPEECH-2020, [paper]

Workshop

Copyright

By volunteers from Institute of Automation,Chinese Academy of Sciences.
Welcome to open an issue or make a pull request!

Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Pattern Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM par

Computational Linguistics Research Group 8.4k Dec 30, 2022
nlpcommon is a python Open Source Toolkit for text classification.

nlpcommon nlpcommon, Python Text Tool. Guide Feature Install Usage Dataset Contact Cite Reference Feature nlpcommon is a python Open Source

xuming 3 May 29, 2022
The code for two papers: Feedback Transformer and Expire-Span.

transformer-sequential This repo contains the code for two papers: Feedback Transformer Expire-Span The training code is structured for long sequentia

Meta Research 125 Dec 25, 2022
This is a simple item2vec implementation using gensim for recbole

recbole-item2vec-model This is a simple item2vec implementation using gensim for recbole( https://recbole.io ) Usage When you want to run experiment f

Yusuke Fukasawa 2 Oct 06, 2022
Automated question generation and question answering from Turkish texts using text-to-text transformers

Turkish Question Generation Offical source code for "Automated question generation & question answering from Turkish texts using text-to-text transfor

Open Business Software Solutions 29 Dec 14, 2022
Snips Python library to extract meaning from text

Snips NLU Snips NLU (Natural Language Understanding) is a Python library that allows to extract structured information from sentences written in natur

Snips 3.7k Dec 30, 2022
This repository details the steps in creating a Part of Speech tagger using Trigram Hidden Markov Models and the Viterbi Algorithm without using external libraries.

POS-Tagger This repository details the creation of a Part-of-Speech tagger using Trigram Hidden Markov Models to predict word tags in a word sequence.

Raihan Ahmed 1 Dec 09, 2021
This library is testing the ethics of language models by using natural adversarial texts.

prompt2slip This library is testing the ethics of language models by using natural adversarial texts. This tool allows for short and simple code and v

9 Dec 28, 2021
MiCECo - Misskey Custom Emoji Counter

MiCECo Misskey Custom Emoji Counter Introduction This little script counts custo

7 Dec 25, 2022
English loanwords in the world's languages

Wiktionary as CLDF Content cldf1 and cldf2 contain cldf-conform data sets with a total of 2 377 756 entries about the vocabulary of all 1403 languages

Viktor Martinović 3 Jan 14, 2022
[AAAI 21] Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning

◥ Curriculum Labeling ◣ Revisiting Pseudo-Labeling for Semi-Supervised Learning Paola Cascante-Bonilla, Fuwen Tan, Yanjun Qi, Vicente Ordonez. In the

UVA Computer Vision 113 Dec 15, 2022
Large-scale Knowledge Graph Construction with Prompting

Large-scale Knowledge Graph Construction with Prompting across tasks (predictive and generative), and modalities (language, image, vision + language, etc.)

ZJUNLP 161 Dec 28, 2022
IndoBERTweet is the first large-scale pretrained model for Indonesian Twitter. Published at EMNLP 2021 (main conference)

IndoBERTweet 🐦 🇮🇩 1. Paper Fajri Koto, Jey Han Lau, and Timothy Baldwin. IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effe

IndoLEM 40 Nov 30, 2022
American Sign Language (ASL) to Text Converter

Signterpreter American Sign Language (ASL) to Text Converter Recommendations Although there is grayscale and gaussian blur, we recommend that you use

0 Feb 20, 2022
This codebase facilitates fast experimentation of differentially private training of Hugging Face transformers.

private-transformers This codebase facilitates fast experimentation of differentially private training of Hugging Face transformers. What is this? Why

Xuechen Li 73 Dec 28, 2022
Pytorch-Named-Entity-Recognition-with-BERT

BERT NER Use google BERT to do CoNLL-2003 NER ! Train model using Python and Inference using C++ ALBERT-TF2.0 BERT-NER-TENSORFLOW-2.0 BERT-SQuAD Requi

Kamal Raj 1.1k Dec 25, 2022
This is the writeup of all the challenges from Advent-of-cyber-2019 of TryHackMe

Advent-of-cyber-2019-writeup This is the writeup of all the challenges from Advent-of-cyber-2019 of TryHackMe https://tryhackme.com/shivam007/badges/c

shivam danawale 5 Jul 17, 2022
Reproducing the Linear Multihead Attention introduced in Linformer paper (Linformer: Self-Attention with Linear Complexity)

Linear Multihead Attention (Linformer) PyTorch Implementation of reproducing the Linear Multihead Attention introduced in Linformer paper (Linformer:

Kui Xu 58 Dec 23, 2022
Python package for performing Entity and Text Matching using Deep Learning.

DeepMatcher DeepMatcher is a Python package for performing entity and text matching using deep learning. It provides built-in neural networks and util

461 Dec 28, 2022
NLP-Project - Used an API to scrape 2000 reddit posts, then used NLP analysis and created a classification model to mixed succcess

Project 3: Web APIs & NLP Problem Statement How do r/Libertarian and r/Neoliberal differ on Biden post-inaguration? The goal of the project is to see

Adam Muhammad Klesc 2 Mar 29, 2022