machine_translation.md

Machine Translation and Language Transfer, Generation, Summarization and Dialogue

Machine Translation

• [2014 SSST] On the properties of neural machine Translation Encoder-Decoder Approaches, [paper], [bibtex].
• [2015 ICLR] Neural Machine Translation by Jointly Learning to Align and Translate, [paper], [bibtex], sources: [lisa-groundhog/GroundHog], [tensorflow/nmt].
• [2015 EMNLP] Effective Approaches to Attention-based Neural Machine Translation, [paper], [bibtex], [HarvardNLP homepage], sources: [dillonalaird/Attention], [tensorflow/nmt].
• [2016 ACL] Neural Machine Translation of Rare Words with Subword Units, [paper], [bibtex], [software], sources: [rsennrich/subword-nmt], [soaxelbrooke/python-bpe].
• [2016 NeurIPS] Professor Forcing: A New Algorithm for Training Recurrent Networks, [paper], [bibtex], sources: [anirudh9119/LM_GANS].
• [2017 ACL] A Convolutional Encoder Model for Neural Machine Translation, [paper], [bibtex], sources: [facebookresearch/fairseq].
• [2017 NIPS] Attention is All You Need, [paper], [bibtex], [Chinses blog], sources: [Kyubyong/transformer], [jadore801120/attention-is-all-you-need-pytorch], [DongjunLee/transformer-tensorflow].
• [2017 ICML] Convolutional Sequence to Sequence Learning, [paper], [bibtex], sources: [pytorch/fairseq].
• [2017 EMNLP] Neural Machine Translation with Word Predictions, [paper], [bibtex].
• [2017 EMNLP] Massive Exploration of Neural Machine Translation Architectures, [paper], [bibtex], [homepage], sources: [google/seq2seq].
• [2017 EMNLP] Efficient Attention using a Fixed-Size Memory Representation, [paper], [bibtex].
• [2018 AMTA] Context Models for OOV Word Translation in Low-Resource Language, [paper], [bibtex].
• [2018 ACL] Modeling Localness for Self-Attention Networks, [paper], [bibtex].
• [2018 NAACL] Self-Attention with Relative Position Representations, [paper], [bibtex].
• [2018 COLING] Double Path Networks for Sequence to Sequence Learning, [paper], [bibtex].
• [2018 EMNLP] Meta-Learning for Low-Resource Neural Machine Translation, [paper], [bibtex].
• [2019 NAACL] Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation, [paper], [bibtex], sources: [jiaweiw/Extract-Edit-Unsupervised-NMT].
• [2019 NAACL] Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation, [paper], [bibtex].
• [2019 ACL] From Bilingual to Multilingual Neural Machine Translation by Incremental Training, [paper], [bibtex].
• [2019 ACL] Bridging the Gap between Training and Inference for Neural Machine Translation, [paper], [bibtex], [论文解释1], [论文解释2], sources: [ictnlp/OR-NMT].
  • Gumbel-Max Technique: [2014 NIPS] A* Sampling, [paper], [bibtex], [Gumbel-max trick], [Gumbel trick], [Gumbel-Max Trick], [The Gumbel trick], [Gumbel Distribution].
• [2019 EMNLP] Mask-Predict: Parallel Decoding of Conditional Masked Language Models, [paper], [bibtex], sources: [facebookresearch/Mask-Predict].
• [2020 ICLR] Neural Machine Translation with Universal Visual Representation, [paper], [bibtex], sources: [cooelf/UVR-NMT].
• [2020 ICLR] Incorporating BERT into Neural Machine Translation, [paper], [bibtex], sources: [bert-nmt/bert-nmt].
• [2020 ArXiv] Unsupervised Domain Adaptation for Neural Machine Translation with Iterative Back Translation, [paper], [bibtex].
• [2020 ACL] Multimodal Transformer for Multimodal Machine Translation, [paper], [bibtex].
• [2021 ACL Findings] On the Language Coverage Bias for Neural Machine Translation, [paper], [bibtex].
• [2021 ICML] Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation, [paper], [bibtex], sources: [tencent-ailab/ICML21_OAXE].

Language Style Transfer

• [2018 ArXiv] Style Transfer as Unsupervised Machine Translation, [paper], [bibtex], [homepage].
• [2019 ICLR] Multiple-Attribute Text Rewriting, [paper], [bibtex].

Dialogue, Chatbot and Language Generation

• [2013 IEEE] POMDP-based Statistical Spoken Dialogue Systems: a Review, [paper], [bibtex].
• [2014 NIPS] Sequence to Sequence Learning with Neural Networks, [paper], [bibtex], sources: [farizrahman4u/seq2seq], [ma2rten/seq2seq], [JayParks/tf-seq2seq], [macournoyer/neuralconvo].
• [2015 CIKM] A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion, [paper], [bibtex], sources: [sordonia/hred-qs].
• [2015 EMNLP] Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems, [paper], [bibtex], sources: [shawnwun/RNNLG], [hit-computer/SC-LSTM].
• [2015 ArXiv] Attention with Intention for a Neural Network Conversation Model, [paper], [bibtex].
• [2015 ACL] Neural Responding Machine for Short-Text Conversation, [paper], [bibtex].
• [2016 AAAI] Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models, [paper], [bibtex], sources: [suriyadeepan/augmented_seq2seq], [julianser/hed-dlg], [sordonia/hed-dlg], [julianser/hred-latent-piecewise], [julianser/hed-dlg-truncated].
• [2016 ACL] On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems, [paper], [bibtex].
• [2016 EMNLP] Deep Reinforcement Learning for Dialogue Generation, [paper], [bibtex], sources: [liuyuemaicha/Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow].
• [2016 EMNLP] Multi-view Response Selection for Human-Computer Conversation, [paper], [bibtex].
• [2017 KDD Explorations Newsletter] A Survey on Dialogue Systems: Recent Advances and New Frontiers, [paper], [bibtex], sources: [shawnspace/survey-in-dialog-system].
• [2017 EMNLP] Adversarial Learning for Neural Dialogue Generation, [paper], [bibtex], sources: [jiweil/Neural-Dialogue-Generation], [liuyuemaicha/Adversarial-Learning-for-Neural-Dialogue-Generation-in-Tensorflow].
• [2017 ACL] Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots, [paper], [bibtex], sources: [MarkWuNLP/MultiTurnResponseSelection], [krayush07/sequential-match-network].
• [2018 COLING] Sequence-to-sequence Data Augmentation for Dialogue Language Understanding, [paper], [bibtex], sources: [AtmaHou/Seq2SeqDataAugmentationForLU].
• [2018 IEEE] Neural Approaches to Conversational AI: Question Answering, Task-Oriented Dialogues and Social Chatbots, [paper], [slides], [bibtex].
• [2018 EMNLP] Spherical Latent Spaces for Stable Variational Autoencoders, [paper], [bibtex], sources: [jiacheng-xu/vmf_vae_nlp].
• [2019 ACL] Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models, [paper], [bibtex].
• [2019 ACL] Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems, [paper], [bibtex], sources: [jasonwu0731/trade-dst].
• [2019 IJCAI] Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset, [paper], [bibtex], [slides].
• [2020 NeurIPS] Zero-Resource Knowledge-Grounded Dialogue Generation, [paper], [bibtex], sources: [nlpxucan/ZRKGC].

Text Summarization

• [2016 ACL] Incorporating Copying Mechanism in Sequence-to-Sequence Learning, [paper], [bibtex], sources: [mjc92/CopyNet], [lspvic/CopyNet], [MultiPath/CopyNet], [google/text2text], [jaredwei01/CopyNet].
• [2017 ACL] Get To The Point: Summarization with Pointer-Generator Networks, [paper], [notes], [slides], [bibtex], sources: [abisee/pointer-generator], [abisee/cnn-dailymail], [JafferWilson/Process-Data-of-CNN-DailyMail].
• [2018 COLING] Structure-Infused Copy Mechanisms for Abstractive Summarization, [paper], [bibtex], sources: [KaiQiangSong/struct_infused_summ].
• [2018 ICLR] Generating Wikipedia by Summarizing Long Sequences, [paper], [bibtex], sources: [tensorflow/tensor2tensor · wikisum].
• [2018 ACML] A Self-Attentive Hierarchical Model for Jointly Improving Text Summarization and Sentiment Classification, [paper], [bibtex].
• [2019 ArXiv] On Extractive and Abstractive Neural Document Summarization with Transformer Language Models, [paper], [bibtex].