Chatbot
50 papers with code • 1 benchmarks • 6 datasets
Chatbot or conversational AI is a language model designed and implemented to have conversations with humans.
Source: Open Data Chatbot
Benchmarks
| Trend | Dataset | Best Model | Paper Title | Paper | Code | Compare |
|---|---|---|---|---|---|---|
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PERSONA-CHAT
Blended Skill Talk
Taiga Corpus
Greatest papers with code
Recipes for building an open-domain chatbot
huggingface/transformers
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EACL 2021
Building open-domain chatbots is a challenging area for machine learning research.
Visual Dialog
facebookresearch/ParlAI
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CVPR 2017
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.
Ranked #15 on
Visual Dialog
on VisDial v0.9 val
Sequence Labeling Approach to the Task of Sentence Boundary Detection
One of the keys to enable chatbots to communicate with human in a more natural way is the ability to handle long and complex user's utterances.
PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning
To build a high-quality open-domain chatbot, we introduce the effective training process of PLATO-2 via curriculum learning.
Ranked #1 on
Chatbot
on 10 Monkey Species
(using extra training data)
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
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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.
A Unified Pre-training Framework for Conversational AI
In this work, we explore the application of PLATO-2 on various dialogue systems, including open-domain conversation, knowledge grounded dialogue, and task-oriented conversation.
Deep Reinforcement Learning for Dialogue Generation
liuyuemaicha/Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow
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EMNLP 2016
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.
