Open-Domain Dialog
31 papers with code • 1 benchmarks • 10 datasets
Benchmarks
These leaderboards are used to track progress in Open-Domain Dialog
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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Datasets
KILT
MultiDoc2Dial
CPED
ProsocialDialog
MMDialog
DuLeMon
MMChat
Most implemented papers
KILT: a Benchmark for Knowledge Intensive Language Tasks
We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance.
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems
natashamjaques/neural_chat
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NeurIPS 2019
To investigate the strengths of this novel metric and interactive evaluation in comparison to state-of-the-art metrics and human evaluation of static conversations, we perform extended experiments with a set of models, including several that make novel improvements to recent hierarchical dialog generation architectures through sentiment and semantic knowledge distillation on the utterance level.
Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References
The aim of this paper is to mitigate the shortcomings of automatic evaluation of open-domain dialog systems through multi-reference evaluation.
Hurdles to Progress in Long-form Question Answering
martiansideofthemoon/hurdles-longform-qa
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NAACL 2021
The task of long-form question answering (LFQA) involves retrieving documents relevant to a given question and using them to generate a paragraph-length answer.
RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems
Open-domain human-computer conversation has been attracting increasing attention over the past few years.
Augmenting Neural Response Generation with Context-Aware Topical Attention
Our model is built upon the basic Seq2Seq model by augmenting it with a hierarchical joint attention mechanism that incorporates topical concepts and previous interactions into the response generation.
Evaluating Coherence in Dialogue Systems using Entailment
nouhadziri/DialogEntailment
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NAACL 2019
Evaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers.
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
natashamjaques/neural_chat
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Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment.
Large-Scale Transfer Learning for Natural Language Generation
atselousov/transformer_chatbot_experiments
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ACL 2019
Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks.
A Multi-Turn Emotionally Engaging Dialog Model
yuboxie/meed
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Open-domain dialog systems (also known as chatbots) have increasingly drawn attention in natural language processing.
