❤️ ‼️ 07/18/2023: Check our latest updates on DialogStudio, a meticulously curated collection of dialogue datasets. These datasets are unified under a consistent format while retaining their original information. We incorporate domain-aware prompts and identify dataset licenses, making DialogStudio an exceptionally rich and diverse resource for dialogue research and model training.
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Deep Generative Models in
IJCAL 2018 -
Conversational AI in
ACL、SIGIR 2018 -
Deep Learning for Dialogue Systems in
COLING 2018 -
Unified View of Deep Generative Models in
ICML 2018 -
DRL for Natural Language Processing in
ACL 2018 -
Deep+Learning+for+ChatBots in
EMNLP 2018 -
Transfer Learning for NLP released by Sebastian Ruder on Feb 26, 2019
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Zero-shot Learning for CV IN CVPR 2017
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Deep Adversarial Learning for NLP on NAACL, June 2, 2019
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Transfer Learning in NLP in NAACL, June 2, 2019
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Neural Approaches to Conversational AI, a recent survey for dialog systems
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The Bright Future of ACL/NLP in ACL, July 2019.
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Building Dialog System With Less Supervision in West Coast NLP workshop, Sep 7, 2019
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Knowledge-Aware Natural Language Understanding, the PhD thesis of Pradeep Dasigi in CMU, 2019.
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Towards scalable multi-domain conversational agents in the 4th International Workshop on Search-Oriented Conversational AI.
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Deeper Conversational AI in NeurIPS, Dec 10, 2020
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Interactive Learning for Conversational Understanding by Gokhan Tur from Amazon Alexa AI, 2020 (Stanford NLP Seminar)
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GPT-3-Few-Shot Learning with a Giant Language Model by Melanie Subbiah from OpenAI, 2020 (Stanford NLP Seminar)
NIPS 2018 Workshops: Around Octomber 25, 2018
CVPR-2019: November 16, 2018 (including rebuttal)
NAACL-2019: Abastract: December 3, 2018, Full paper: December 10, 2018
KDD-2019: February 3, 2019
ICML-2019: January, 18 2019
IJCAI-2019: February 25, 2019
ACL-2019: March 4, 2019
EMNLP-2019: May 21, 2019
More can be found at the Conference Deadline
- The Study of Language
- Semantics Third Edition
- Reinforcement Learning for Adaptive Dialogue Systems
- Neural Network Methods in Natural Language Processing
Multilingual:
- Cross-lingual dataset for intent and slot detection, Cross-lingual dialog state tracking dataset. Papers: XLM, XLM-R, mBert, mBart
Intent Detection:
- BANKING77: A single domain intent dataset with 77 intents; CLINC150: A dataset with 15 domains and 150 intents in total, and this dataset is mainly designed for Out-of-Scope (OOS) predictions.
- SeqGAN - SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
- MaliGAN - Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
- RankGAN - Adversarial ranking for language generation
- LeakGAN - Long Text Generation via Adversarial Training with Leaked Information
- TextGAN - Adversarial Feature Matching for Text Generation
- GSGAN - GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution
- (*****) Deep Reinforcement Learning For Sequence to Sequence Models
- Maximum Entropy Inverse Reinforcement Learning
- (*****) Towards Diverse Text Generation with Inverse Reinforcement Learning
- (*****) Reinforcement Learning and Control as Probabilistic Inference-Tutorial and Review
- (*****) Policy gradient methods for reinforcement learning with function approximation
- (*****) Proximal policy optimization algorithms
Change the on-policy gradient method to off-policy gradient method to improve the data efficiency. And use trust region optimization method to search for the better result.
- (*****) Continuous control with deep reinforcement learning
Use the deterministic policy instead of stochastic policy to deal with the continuous action space. The architecture of framework is Actor-Critic.
- (*****) Model-Agnostic Meta-Learning
- A spect-augmented Adversarial Networks for Domain Adaptation
- (*****) Natural Language to Structured Query Generation via Meta-Learning
- (*****) Learning a Prior over Intent via Meta-Inverse Reinforcement Learning
- Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
- Multi-agent cooperation and the emergence of (natural) language
- (*****)Counterfactual Multi-Agent Policy Gradients (AAAI best paper) ==========
- A Hierarchical Latent Structure for Variational Conversation Modeling
- A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
- (*****) Improving Variational Encoder-Decoders in Dialogue Generation
- (*****) DialogWAE- Multimodal Response Generation with Conditional Wasserstein Auto-Encoder
- Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders
- (**) Visual Question Answering- A Survey of Methods and Datasets
- (****) Visual Question Answering as a Meta Learning Task
- Cross-Dataset Adaptation for Visual Question Answering
- Joint Image Captioning and Question Answering
- Learning Answer Embeddings for Visual Question Answering
- Question Answering through Transfer Learning from Large Fine-grained Supervision Data
- (**) Visual Dialog
- (****) Are You Talking to Me-Reasoned Visual Dialog Generation through Adversarial Learning
- (****)Two can play this Game-Visual Dialog with Discriminative Question Generation
- Zero-Shot Dialog Generation with Cross-Domain Latent Actions
- Adversarial Learning of Task-Oriented Neural Dialog Models
- (*****)Best of Both Worlds-Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model
- (*****)Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
- (****)Mind Your Language-Learning Visually Grounded Dialog in a Multi-Agent Setting
- (*****)Embodied Question Answering (https://embodiedqa.org/)
- Open-Ended Long-form Video Question Answering via Adaptive Hierarchical Reinforced Networks.
- Multi-Turn Video Question Answering via Multi-Stream Hierarchical Attention Context Network.
- TVQA-Localized, Compositional Video Question Answering
- Dialog-based Interactive Image Retrieval （NIPS2018）
- Improving Search through A3C Reinforcement Learning based Conversational Agent
- A New Dataset: Audio-Visual Scene-Aware Dialog
- End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features
- Audio Visual Scene-Aware Dialog (AVSD) Challenge at DSTC7
- More details on the webpage