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Visual Dialog
CVPR 2017
• facebookresearch/ParlAI
• 
We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content.
End-to-End Task-Completion Neural Dialogue Systems
One of the major drawbacks of modularized task-completion dialogue systems is that each module is trained individually, which presents several challenges.
Deep Learning Based Chatbot Models
• ricsinaruto/Seq2seqChatbots
• 
Finally, I conduct a detailed analysis of how the vanilla model performs on conversational data by comparing it to previous chatbot models and how the additional features affect the quality of the generated responses.
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network
Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context.
#8 best model for
Conversational Response Selection
on Ubuntu Dialogue (v1, Ranking)
Deep Reinforcement Learning for Dialogue Generation
EMNLP 2016
• liuyuemaicha/Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow
•
Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes.
Subword Semantic Hashing for Intent Classification on Small Datasets
In this paper, we introduce the use of Semantic Hashing as embedding for the task of Intent Classification and achieve state-of-the-art performance on three frequently used benchmarks.
CHATBOT INTENT CLASSIFICATION TEXT CLASSIFICATION WORD EMBEDDINGS
Jointly Optimizing Diversity and Relevance in Neural Response Generation
In this paper, we propose a SpaceFusion model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms.
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions.
CHATBOT MACHINE READING COMPREHENSION QUESTION ANSWERING QUESTION GENERATION
Stacked DeBERT: All Attention in Incomplete Data for Text Classification
This is due to the fact that current approaches are built for and trained with clean and complete data, and thus are not able to extract features that can adequately represent incomplete data.
CHATBOT DENOISING INTENT CLASSIFICATION NATURAL LANGUAGE UNDERSTANDING TEXT CLASSIFICATION
XPersona: Evaluating Multilingual Personalized Chatbot
Experimental results show that the multilingual trained models outperform the translation-pipeline and that they are on par with the monolingual models, with the advantage of having a single model across multiple languages.