A Survey of Natural Language Generation in Task-Oriented Dialogue System (TOD): Recent Advances and New Frontiers

This repository contains a list of papers, open-sourced codes, datasets and leaderboards in NLG field which is carefully and comprehensively organized. If you found any error, please don't hesitate to open an issue or pull request.

Contributor

Contributed by Libo Qin, Zhouyang Li, Jieming Lou, Qiying Yu, Wanxiang Che.

Thanks for supports from our adviser Wanxiang Che!

Introduction

Natural Language Generation (NLG) in task-oriented dialogue system is a critical component in task-oriented dialogue systems, which has attracted increasing research attention.

NLG aims to convert dialogue acts into natural language responses. The example is shown in the table below: the input contains dialogue act inform and slot value pairs (name=Blue Spice, priceRange=low, familyFriendly=yes), and the task of NLG is to transform the input into the corresponding natural language reply: "The Blue Spice is a low cost venue. It is a family friendly location."

Input	inform （name = Blue Spice, priceRange =low, familyFriendly = yes）
Output	The Blue Spice is a low cost venue. It is a family friendly location.

For the purpose of alleviating pressure in article/dataset collation, we worked on sorting out the relevant data sets, papers, codes and lists of NLG in this project.

At present, the project has been completely open source, including:

NLG domain dataset sorting table: we sorted out the dataset used in NLG field. You can index in it and get the message of general scale, basic structure, content, characteristics, source and acquisition method of the dataset you want to know.
Articles and infos in different directions in the field of NLG: we classified and arranged the papers according to the current mainstream frontiers. Each line of the list contains not only the title of the paper, but also the year of publication, the source of publication, the paper link and code link for quick indexing, as well as the dataset used.
Leaderboard list on the mainstream datasets of NLG: we sorted out the leaderboard on the mainstream datasets. In addition to the paper/model/method name and related scores, each line also has links to year, paper and code if it has.

The taxonomy of deeplearning-based models in TOD NLG can be summarized into this picture below.

Quick path

Resources
- Classic methods
- Deeplearning-based Methods
Dataset
LeaderBoard

Resources

1. Classic methods of task-oriented natural language generation

1.1 Templete-based

[1] NLG vs. TemplatesComputational Linguistics 1995 [pdf]

[2] A versatile system for language generation in conversational system applicationICSLP 2000 [pdf]

[3] Natural language generation in the ibm flight information systemANLP-NAACL 2000 Workshop [pdf]

1.2 Plan-based

[1] SPoT: A trainable sentence plannerNAACL2001 [pdf]

[2] Training a sentence planner for spoken dialogue using boostingComputer Speech & Language 2002 [pdf]

[3] Response planning and generation in the MERCURY flight reservation systemComputer Speech & Language 2002 [pdf]

[4] Acquiring correct knowledge for natural language generationJAIR 2003 [pdf]

[5] Trainable sentence planning for complex information presentation in spoken dialog systemsACL2004 [pdf]

[6] Trainable sentence planning system [pdf]

[7] A probabilistic framework for dialog simulation and optimal strategy learningIEEE Transactions on Audio, Speech and Language Processing 2006[pdf]

[8] An investigation into the validity of some metrics for automatically evaluating natural language generation systems Computational Linguistics 2009[pdf]

[9] Individual and domain adaptation in sentence planning for dialoguearXiv 2011 [pdf]

[10] Controlling user perceptions of linguistic style: Trainable generation of personality traitsACL 2011 [pdf]

[11] Towards personality-based user adaptation: psychologically informed stylistic language generation [pdf]

1.3 Class-based

[1] Stochastic language generation for spoken dialogue systems NAACL 2000 [pdf]

[2] Bootstrapping lexical choice via multiple-sequence alignmentEMNLP 2002 [pdf]

[3] Automatic generation of weather forecast texts using comprehensive probabilistic generation-space modelsNatural Language Engineering 2008 [pdf]

1.4 Phrase-based

[1] Phrase-based statistical language generation using graphical models and active learningACL 2010 [pdf]

[2] Training a natural language generator from unaligned data ACL 2015 [pdf]

[3] Imitation learning for language generation from unaligned dataCOLING 2016 [pdf]

2. Deeplearning-based methods of task-oriented natural language generation

2.1 RNN-based

[1] Stochastic Language Generation in Dialogue using RNN with Convolutional Sentence Reranking(Restaurant dataset) Sigdial 2015[pdf]

[2] Semantically Conditioned LSTM-based NLG for spoken dialogue systems(Restaurant/Hotel dataset)EMNLP 2015[pdf]

[3] What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment(WeatherGov/RoboCup dataset)[pdf]

[4] Toward multi-domain language generation using recurrent neural networks(Restaurant/Hotel dataset)NIPS Workshop 2015[pdf]

[5] Multi-domain Neural Network Language Generation for Spoken Dialogue Systems(Restaurant/Hotel/Television/Laptop dataset)NAACL-HLT 2016 [pdf]

[6] Neural-based Natural Language Generation in Dialogue using RNN Encoder-Decoder with Semantic Aggregation(Restaurant/Hotel/Television/Laptop dataset) SIGDIAL 2017[pdf]

2.2 Seq2Seq-based

[1] Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings(Restaurant dataset)ACL 2016[pdf][code]

[2] A Context-aware Natural Language Generator for Dialogue Systems(alex context nlg dataset) SIGDIAL 2016[pdf][code]

[3] A Network-based End-to-End Trainable Task-oriented Dialogue System(Woz dataset)EACL 2017[pdf]

[4] Adversarial Domain Adaptation for Variational Neural Language Generation in Dialogue Systems (Restaurant/Hotel/Television/Laptop dataset) COLING 2018[pdf]

[5] Dual Latent Variable Model for Low-Resource Natural Language Generation in Dialogue Systems (Restaurant/Hotel/Television/Laptop dataset) CoNLL 2018[pdf]

[6] Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems(Restaurant/Hotel/Television/Laptop dataset) Sigdial 2018[pdf]

[7] A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation (Restaurant/Television/Laptop dataset) NAACL 2018[pdf]

[8] A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation(E2E challenge dataset)ACL 2018[pdf]

[9] Char2char Generation with Reranking for the E2E NLG Challenge(E2E challenge dataset) cs.CL 2018[pdf]

[10] Multi-task Learning for Natural Language Generation in Task-Oriented Dialogue(E2E challenge dataset) EMNLP|IJCNLP 2019[pdf]

[11] Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue(E2E challenge dataset) ACL 2019[pdf]

[12] Retrospective and Prospective Mixture-of-Generators for Task-Oriented Dialogue Response Generation(Multi-Domain-Woz dataset)arXiv 2020[pdf][code]

[13] Template Guided Text Generation for Task-Oriented Dialogue(E2E challenge /SGD/Multi-Domain-Woz/Multi-Domain-Woz-2.1 dataset)EMNLP 2020[pdf][code]

[14] How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue(E2E challenge dataset)EMNLP 2020|ACL 2020[pdf][code]

[15] Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines (Restaurant/Hotel/Television/Laptop/E2E challenge dataset)AAAI 2021[pdf]

2.3 Transformer-based

[1] Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention(Multi-Domain-Woz dataset) ACL 2019[pdf][code]

[2] Few-shot Natural Language Generation for Task-Oriented Dialog(Few-Shot-Woz/Multi-Domain-Woz dataset) arXiv 2020[pdf][code]

[3] Efficient Retrieval Augmented Generation from Unstructured Knowledge for Task-Oriented Dialog(Multi-Domain-Woz-2.1 dataset)Workshop of AAAI 2021[pdf][code]

[4] Unstructured Knowledge Access in Task-oriented Dialog Modeling using Language Inference, Knowledge Retrieval and Knowledge-Integrative Response Generation(Multi-Domain-Woz-2.1 dataset)arXiv 2021[pdf]

Dataset

Name	Intro	Links	Detail	Size & Stats
`Restaurant dataset`	Collected by a spoken dialogue system providing information about certain venues in San Francisco	Download： https://github.com/shawnwun/RNNLG Paper： https://arxiv.org/pdf/1508.01745.pdf	Restaurant Information;Dialogue act types: 8 Slots: 12 Dialogue acts: 248	Dialogues: around 1k Utterances: around 5192 the ratio of training, validation, and testing set: 3:1:1
`Hotel dataset`	Collected by a spoken dialogue system providing information about certain venues in San Francisco	Download： https://github.com/shawnwun/RNNLG Paper：https://arxiv.org/pdf/1508.01745.pdf	Hotel Information;Dialogue act types: 8 Slots: 12 Dialogue acts: 164	Dialogues: around 1k Utterances: 5373 the ratio of training, validation, and testing set: 3:1:1
`Laptop dataset`	1. Created by workers recruited by Amazon Mechanical Turk (AMT) by asking them to propose an appropriate natural language realisation corresponding to each system dialogue act actually generated by a dialogue system 2. Enumerated all possible combinations of dialogue act types and slots based on the ontology	Download： https://github.com/shawnwun/RNNLG Paper：https://arxiv.org/pdf/1603.01232.pdf	Laptop Information;Dialogue act types: 14 Slots: 19 Dialogue acts: about 13k	Dialogues: around 3k Utterances: 13242 the ratio of training, validation, and testing set: 3:1:1
`Television dataset`	1. Created by workers recruited by Amazon Mechanical Turk (AMT) by asking them to propose an appropriate natural language realisation corresponding to each system dialogue act actually generated by a dialogue system 2. Enumerated all possible combinations of dialogue act types and slots based on the ontologyo	Download： https://github.com/shawnwun/RNNLG Paper： https://arxiv.org/pdf/1603.01232.pdf	Laptop Information;Dialogue act types: 14 Slots: 16 Dialogue acts: about 7k	Dialogues: around 2k Utterances: 7035 the ratio of training, validation, and testing set: 3:1:1
`E2E challenge dataset`	1. Collected using the Crowd-Flower platform. 2. For training end-to-end, data-driven natural language generation systems in the restaurant domain. 3. Homepage: http://www.macs.hw.ac.uk/InteractionLab/E2E/	Download: data(original version):https://github.com/tuetschek/e2e-dataset data(cleaned version):https://github.com/tuetschek/e2e-cleaning evaluator:https://github.com/tuetschek/e2e-metrics ; Paper: https://arxiv.org/pdf/1706.09254.pdf	Restaurant Information ;However, this dataset has shown more lexical richness and syntactic variation, including discourse phenomena and to generate from this set requires content selection; Dialogue act types: 1 Slots: 8	Train：4862 MRs，42061 References Dev:547 MRs, 4672 References Teest: 630 MRs, 4693 References
`Multi-Domain-Woz dataset`	1. a fully-labeled multi-domain collection of human-human written conversations 2. for Task-Oriented Dialogue Modelling	Download: https://github.com/budzianowski/multiwoz/blob/master/data/MultiWOZ_2.0.zip Paper: https://aclanthology.org/D18-1547.pdf	With dialogues spanning across 7 domains(Attraction, Hospital, Police, Hotel, Restaurant, Taxi, Train) and several topics. Each dialogue is annotated with a sequence of dialogue states and corresponding system dialogue acts	domains: 7 Dialogues: 8438 Turns: 115424 Slots: 25 Values: 4510
`Multi-Domain-Woz-2.1 dataset`	1. Correct some mistakes in the Multi-Domain-Woz dataset 2. Re-annotate state and utterances based on the original utterances in the dataset	Download: https://github.com/budzianowski/multiwoz/blob/master/data/MultiWOZ_2.1.zip Paper: https://arxiv.org/pdf/1907.01669.pdf	1. correction process results in changes to over 32% of state annotations across 40% of the dialogue turns 2. fix 146 dialogue utterances by canonicalizing slot values in the utterances to the values in the dataset ontology	domains: 7 Dialogues: 10438 Slots: 25 Values: 4510

LeaderBoard

Restaurant dataset

Model	Slot-err rate	BELU	Paper / Source	Code link	Conference
`IRN(+KNN)`	0.11	0.807	Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network[[pdf]](https://aclanthology.org/2020.acl-main.10.pdf)	-	Sigdial
`RALSTM`	0.16	0.779	Paper: Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks[[pdf]](https://arxiv.org/pdf/1706.00139.pdf)	-	CoNLL
`Softmax`	0.20	0.812	Paper: Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines[[pdf]](https://www.aaai.org/AAAI21Papers/AAAI-3767.LiY.pdf)	-	AAAI
`VQ-VAE`	0.18	0.789	Paper: Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines[[pdf]](https://www.aaai.org/AAAI21Papers/AAAI-3767.LiY.pdf)	-	AAAI
`NLG-LM`	-	0.795	Paper: Multi-task Learning for Natural Language Generation in Task-Oriented Dialogue[[pdf]](https://aclanthology.org/D19-1123.pdf)	-	EMNLP\|IJCNLP
`Gumbel-softmax`	0.21	0.776	Paper: Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines[[pdf]](https://www.aaai.org/AAAI21Papers/AAAI-3767.LiY.pdf)	-	AAAI
`ARoA`	0.30	0.776	Neural-based Natural Language Generation in Dialogue using RNN Encoder-Decoder with Semantic Aggregation[[pdf]](https://arxiv.org/pdf/1706.06714.pdf)	-	Sigdial
`SCLSTM`	0.38	0.753	Paper： Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems[[pdf]](https://arxiv.org/pdf/1508.01745.pdf)	https://github.com/shawnwun/RNNLG	EMNLP
`HLSTM`	0.74	0.747	Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking[[pdf]](https://arxiv.org/pdf/1508.01755.pdf)	https://github.com/shawnwun/RNNLG	Sigdial

Hotel dataset

Model	Slot-err rate	BELU	Paper / Source	Code link	Conference
`NLG-LM`	-	0.939	Paper: Multi-task Learning for Natural Language Generation in Task-Oriented Dialogue[[pdf]](https://aclanthology.org/D19-1123.pdf)	-	EMNLP\|IJCNLP
`Softmax`	0.46	0.923	Paper: Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines[[pdf]](https://www.aaai.org/AAAI21Papers/AAAI-3767.LiY.pdf)	-	AAAI
`VQ-VAE`	0.55	0.921	Paper: Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines[[pdf]](https://www.aaai.org/AAAI21Papers/AAAI-3767.LiY.pdf)	-	AAAI
`IRN(+KNN)`	0.32	0.911	Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network[[pdf]](https://aclanthology.org/2020.acl-main.10.pdf)	-	Sigdial
`Gumbel-softmax`	0.77	0.903	Paper: Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines[[pdf]](https://www.aaai.org/AAAI21Papers/AAAI-3767.LiY.pdf)	-	AAAI
`RALSTM`	0.43	0.898	Paper: Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks[[pdf]](https://arxiv.org/pdf/1706.00139.pdf)	-	CoNLL
`ARoA`	1.13	0.892	Neural-based Natural Language Generation in Dialogue using RNN Encoder-Decoder with Semantic Aggregation[[pdf]](https://arxiv.org/pdf/1706.06714.pdf)	-	Sigdial
`HLSTM`	2.67	0.850	Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking[[pdf]](https://arxiv.org/pdf/1508.01755.pdf)	https://github.com/shawnwun/RNNLG	Sigdial
`SCLSTM`	3.07	0.848	Paper： Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems[[pdf]](https://arxiv.org/pdf/1508.01745.pdf)	https://github.com/shawnwun/RNNLG	EMNLP

Laptop dataset

Model	Slot-err rate	BELU	Paper / Source	Code link	Conference
`Softmax`	0.31	0.591	Paper: Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines[[pdf]](https://www.aaai.org/AAAI21Papers/AAAI-3767.LiY.pdf)	-	AAAI
`NLG-LM`	-	0.586	Paper: Multi-task Learning for Natural Language Generation in Task-Oriented Dialogue[[pdf]](https://aclanthology.org/D19-1123.pdf)	-	EMNLP\|IJCNLP
`Gumbel-softmax`	0.63	0.561	Paper: Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines[[pdf]](https://www.aaai.org/AAAI21Papers/AAAI-3767.LiY.pdf)	-	AAAI
`VQ-VAE`	0.65	0.554	Paper: Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines[[pdf]](https://www.aaai.org/AAAI21Papers/AAAI-3767.LiY.pdf)	-	AAAI
`IRN(+KNN)`	0.29	0.537	Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network[[pdf]](https://aclanthology.org/2020.acl-main.10.pdf)	-	Sigdial
`RALSTM`	0.42	0.525	Paper: Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks[[pdf]](https://arxiv.org/pdf/1706.00139.pdf)	-	CoNLL
`Slug2Slug`	1.55	0.524	Paper: Slug2Slug: A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation[[pdf]](http://www.macs.hw.ac.uk/InteractionLab/E2E/final_papers/E2E-Slug2Slug.pdf)	-	-
`ARoA`	0.50	0.522	Neural-based Natural Language Generation in Dialogue using RNN Encoder-Decoder with Semantic Aggregation[[pdf]](https://arxiv.org/pdf/1706.06714.pdf)	-	Sigdial
`HLSTM`	1.10	0.513	Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking[[pdf]](https://arxiv.org/pdf/1508.01755.pdf)	https://github.com/shawnwun/RNNLG	Sigdial
`SCLSTM`	0.79	0.512	Paper： Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems[[pdf]](https://arxiv.org/pdf/1508.01745.pdf)	https://github.com/shawnwun/RNNLG	EMNLP

Television dataset

Model	Slot-err rate	BELU	Paper / Source	Code link	Conference
`NLG-LM`	-	0.617	Paper: Multi-task Learning for Natural Language Generation in Task-Oriented Dialogue[[pdf]](https://aclanthology.org/D19-1123.pdf)	-	EMNLP\|IJCNLP
`Softmax`	0.51	0.610	Paper: Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines[[pdf]](https://www.aaai.org/AAAI21Papers/AAAI-3767.LiY.pdf)	-	AAAI
`VQ-VAE`	0.70	0.598	Paper: Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines[[pdf]](https://www.aaai.org/AAAI21Papers/AAAI-3767.LiY.pdf)	-	AAAI
`Gumbel-softmax`	0.79	0.581	Paper: Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines[[pdf]](https://www.aaai.org/AAAI21Papers/AAAI-3767.LiY.pdf)	-	AAAI
`IRN(+KNN)`	0.35	0.559	Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network[[pdf]](https://aclanthology.org/2020.acl-main.10.pdf)	-	Sigdial
`RALSTM`	0.63	0.541	Paper: Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks[[pdf]](https://arxiv.org/pdf/1706.00139.pdf)	-	CoNLL
`ARoA`	0.60	0.539	Neural-based Natural Language Generation in Dialogue using RNN Encoder-Decoder with Semantic Aggregation[[pdf]](https://arxiv.org/pdf/1706.06714.pdf)	-	Sigdial
`SCLSTM`	2.31	0.527	Paper： Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems[[pdf]](https://arxiv.org/pdf/1508.01745.pdf)	https://github.com/shawnwun/RNNLG	EMNLP
`HLSTM`	2.50	0.525	Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking[[pdf]](https://arxiv.org/pdf/1508.01755.pdf)	https://github.com/shawnwun/RNNLG	Sigdial
`Slug2Slug`	1.67	0.523	Paper: Slug2Slug: A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation[[pdf]](http://www.macs.hw.ac.uk/InteractionLab/E2E/final_papers/E2E-Slug2Slug.pdf)	-	-

E2E Challenge dataset

Model	BELU	NIST	Paper / Source	Code link	Conference
`OpenNMT`	0.681	8.748	Paper： How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue[[pdf]](http://doras.dcu.ie/25957/1/2020.emnlp-main.230%20(1).pdf)	https://github.com/Henry-E/reliable_neural_nlg	EMNLP\|ACL
`TUDA(Model-D)`	0.713	8.502	Paper： E2E NLG Challenge: Neural Models vs. Templates[[pdf]](https://aclanthology.org/W18-6557.pdf)	https://github.com/UKPLab/e2e-nlg-challenge-2017	ACL
`Softmax`	0.697	-	Paper: Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines[[pdf]](https://www.aaai.org/AAAI21Papers/AAAI-3767.LiY.pdf)	-	AAAI
`Slug2Slug`	0.662	8.613	Paper: Slug2Slug: A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation[[pdf]](http://www.macs.hw.ac.uk/InteractionLab/E2E/final_papers/E2E-Slug2Slug.pdf)	-	-
`NLG-LM`	0.684	-	Paper: Multi-task Learning for Natural Language Generation in Task-Oriented Dialogue[[pdf]](https://aclanthology.org/D19-1123.pdf)	-	EMNLP\|IJCNLP
`VQ-VAE`	0.681	-	Paper: Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines[[pdf]](https://www.aaai.org/AAAI21Papers/AAAI-3767.LiY.pdf)	-	AAAI
`TGen`	0.659	8.609	Paper: Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings[[pdf]](https://arxiv.org/pdf/1606.05491.pdf)	https://github.com/UFAL-DSG/tgen	ACL
`OpenNMT+Surface Forms`	0.628	8.311	Paper： How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue[[pdf]](http://doras.dcu.ie/25957/1/2020.emnlp-main.230%20(1).pdf)	https://github.com/Henry-E/reliable_neural_nlg	EMNLP\|ACL
`Gumbel-softmax`	0.667	-	Paper: Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines[[pdf]](https://www.aaai.org/AAAI21Papers/AAAI-3767.LiY.pdf)	-	AAAI
`TUDA(Model-T)`	0.605	7.526	Paper： E2E NLG Challenge: Neural Models vs. Templates[[pdf]](https://aclanthology.org/W18-6557.pdf)	https://github.com/UKPLab/e2e-nlg-challenge-2017	ACL

Multi-Domain-Woz dataset

Model	Entity F1	BLEU	Paper / Source	Code link	Conference
`SC-GPT`	88.37	30.76	Paper: Few-shot Natural Language Generation for Task-Oriented Dialog[[pdf]](https://arxiv.org/pdf/2002.12328.pdf)	https://github.com/pengbaolin/ SC-GPT	arXiv
`GPT-2`	87.70	30.71	Paper: Few-shot Natural Language Generation for Task-Oriented Dialog[[pdf]](https://arxiv.org/pdf/2002.12328.pdf)	https://github.com/pengbaolin/ SC-GPT	arXiv
`HDSA`	87.30	26.48	Paper： Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention[[pdf]](https://arxiv.org/pdf/1905.12866.pdf)	https://github. com/wenhuchen/HDSA- Dialog	ACL
`SCLSTM`	80.42	21.6	Paper： Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems[[pdf]](https://arxiv.org/pdf/1508.01745.pdf)	https://github.com/shawnwun/RNNLG	EMNLP
`MoGNet`	78.85	20.13	Paper: Retrospective and Prospective Mixture-of-Generators for Task-oriented Dialogue Response Generation[[pdf]](https://arxiv.org/pdf/1911.08151.pdf)	https://github.com/Jiahuan-Pei/multiwoz-mdrg	arXiv
`LaRLAttnGRU`	80.95	12.80	Paper: Global-locally self-attentive encoder for dialogue state tracking[[pdf]](https://aclanthology.org/P18-1135.pdf)	-	ACL
`Structured Fusion`	77.04	16.34	Paper: Structured fusion networks for dialog[[pdf]](https://arxiv.org/pdf/1907.10016.pdf)	-	Sigdial

Multi-Domain-Woz-2.1 dataset

Model	Entity F1	BLEU	Paper / Source	Code link	Conference
`HDNO`	87.63	18.97	Paper: MODELLING HIERARCHICAL STRUCTURE BETWEEN DIALOGUE POLICY AND NATURAL LANGUAGE GEN- ERATOR WITH OPTION FRAMEWORK FOR TASK- ORIENTED DIALOGUE SYSTEM[[pdf]](https://arxiv.org/pdf/2006.06814.pdf)	https://github.com/mikezhang95/HDNO	ICLR
`MarCo`	84.51	19.54	Paper: Multi-Domain Dialogue Acts and Response Co-Generation[[pdf]](https://arxiv.org/pdf/2004.12363.pdf)	https://github.com/InitialBug/ MarCo-Dialog	ACL
`LAVA`	89.52	14.02	Paper: LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization[[pdf]](https://www.aclweb.org/anthology/2020.coling-main.41.pdf)	https://github.com/budzianowski/multiwoz	COLING
`UBAR`	86.45	16.70	Paper: UBAR: Towards Fully End-to-End Task-Oriented Dialog System with GPT-2[[pdf]](https://arxiv.org/pdf/2012.03539.pdf)	https://github.com/ TonyNemo/UBAR- MultiWOZ	AAAI
`DoTs`	79.93	15.9	Paper: Domain State Tracking for a Simplified Dialogue System[[pdf]](https://arxiv.org/pdf/2103.06648.pdf)	-	arXiv
`SimpleTOD`	78.87	16.22	Paper: A Simple Language Model for Task-Oriented Dialogue[[pdf]](https://arxiv.org/pdf/2005.00796.pdf)	https://github.com/ salesforce/simpletod	arXiv
`LABES-S2S`	72.14	18.3	Paper: A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning[[pdf]](https://arxiv.org/pdf/2009.08115.pdf)	https://github.com/thu-spmi/LABES	EMNLP

A Survey of Natural Language Generation in Task-Oriented Dialogue System (TOD): Recent Advances and New Frontiers

Related tags

Overview

A Survey of Natural Language Generation in Task-Oriented Dialogue System (TOD): Recent Advances and New Frontiers

Contributor

Introduction

Quick path

Resources

1. Classic methods of task-oriented natural language generation

1.1 Templete-based

1.2 Plan-based

1.3 Class-based

1.4 Phrase-based

2. Deeplearning-based methods of task-oriented natural language generation

2.1 RNN-based

2.2 Seq2Seq-based

2.3 Transformer-based

Dataset

LeaderBoard

Owner

Libo Qin

The implementation of Parameter Differentiation based Multilingual Neural Machine Translation

Correctly generate plurals, ordinals, indefinite articles; convert numbers to words

An implementation of WaveNet with fast generation

Implementation of COCO-LM, Correcting and Contrasting Text Sequences for Language Model Pretraining, in Pytorch

Research code for the paper "Fine-tuning wav2vec2 for speaker recognition"

Source code for the paper "TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations"

TunBERT is the first release of a pre-trained BERT model for the Tunisian dialect using a Tunisian Common-Crawl-based dataset.

Simple text to phones converter for multiple languages

A Chinese to English Neural Model Translation Project

An IVR Chatbot which can exponentially reduce the burden of companies as well as can improve the consumer/end user experience.

Hierarchical unsupervised and semi-supervised topic models for sparse count data with CorEx

Journey is a NLP-Powered Developer assistant

Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge. Proceedings of EMNLP 2021

vits chinese, tts chinese, tts mandarin

A pytorch implementation of the ACL2019 paper "Simple and Effective Text Matching with Richer Alignment Features".

Korean Simple Contrastive Learning of Sentence Embeddings using SKT KoBERT and kakaobrain KorNLU dataset

Graph Coloring - Weighted Vertex Coloring Problem

Python functions for summarizing and improving voice dictation input.

🏆 • 5050 most frequent words in 109 languages

Installation, test and evaluation of Scribosermo speech-to-text engine