---
title: "What makes a good conversation? How controllable — See 2019"
description: "The paper asks which properties of open-domain chitchat drive human ratings of whole conversations, rather than single replies."
authors: ["Abigail See", "Stephen Roller", "Douwe Kiela", "Jason Weston"]
year: 2019
venue: "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)"
source: "https://aclanthology.org/N19-1170/"
doi: "10.18653/v1/n19-1170"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/see-2019-what-makes-good-conversation-how/"
---
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# What makes a good conversation? How controllable attributes affect human judgments

**Authors:** [Abigail See](https://md.research.elamur.ai/free/en/authors/abigail-see/), [Stephen Roller](https://md.research.elamur.ai/free/en/authors/stephen-roller/), [Douwe Kiela](https://md.research.elamur.ai/free/en/authors/douwe-kiela/), [Jason Weston](https://md.research.elamur.ai/free/en/authors/jason-weston/) · **Year:** 2019 · **Venue:** Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/N19-1170/) · [PDF](https://aclanthology.org/N19-1170.pdf) · [DOI 10.18653/v1/n19-1170](https://doi.org/10.18653/v1/n19-1170) · [OpenAlex](https://openalex.org/W2916772188)

**Citations:** 241 citations (openalex-snapshot · 2026-10-14) · [128](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper asks which properties of open-domain chitchat drive human ratings of whole conversations, rather than single replies. The authors use PersonaChat and a baseline 2-layer LSTM sequence-to-sequence model with attention, pretrained on Twitter data. They then steer four low-level attributes: repetition, specificity, response-relatedness and question-asking. Two general control techniques are compared. Conditional Training (CT) feeds a control embedding to the decoder during fine-tuning. Weighted Decoding (WD) adds weighted word-level features to the beam-search score at inference only. Control settings apply to an entire dialogue rather than to each utterance. A large crowdsourced study of multi-turn interactive chats rates the models on eight conversational aspects plus overall quality. Combining controls for repetition, specificity and question-asking clearly raised engagingness over the baseline. The best system matched the ConvAI2 winner, which had far more training data. Engagingness came close to human level, but humanness stayed well below it. This shows that the two quality metrics diverge and that multi-turn evaluation is essential.

## Key points
- The baseline uses GloVe embeddings and 2.5M Twitter message-response pairs, and is built in ParlAI. It reaches perplexity 26.83 and F1 17.02 on PersonaChat validation, which would rank about 4th of 26 entries in ConvAI2.
- PersonaChat training data contains 8939 conversations and 955 personas.
- Engagingness peaked at a question rate of 65.7% (CT, z = 7). This is well above the 28.8% found in human gold dialogues.
- Controlling response-relatedness with WD gave no gain over the uncontrolled model in crowdworker ratings.
- In A/B interestingness tests, specificity-controlled models beat the repetition-controlled baseline. Specificity WD with weight 6 won 84.1% of comparisons. Code, models and chatlogs are released via ParlAI.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

128 papers of our library cite it (of the 140884 whose bibliographies we read); 14 of them have a page here, 114 not yet.

- [Towards a Human-like Open-Domain Chatbot](https://md.research.elamur.ai/free/en/papers/adiwardana-2020-towards-human-like-open-domain/)
- [Automatic Evaluate Dialogue Appropriateness by Using Dialogue Act](https://md.research.elamur.ai/free/en/papers/chen-2023-automatic-evaluate-dialogue-appropriateness-dialogue/)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://md.research.elamur.ai/free/en/papers/dathathri-2019-plug-play-language-models-simple/)
- [Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation](https://md.research.elamur.ai/free/en/papers/dinan-2020-queens-are-powerful-too-mitigating/)
- [Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness](https://md.research.elamur.ai/free/en/papers/kim-2020-will-i-sound-like-me/)
- [SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization](https://md.research.elamur.ai/free/en/papers/kim-2023-soda-million-scale-dialogue-distillation/)
- [Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training](https://md.research.elamur.ai/free/en/papers/li-2020-don-t-say-that-making/)
- [DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts](https://md.research.elamur.ai/free/en/papers/liu-2021-dexperts-decoding-time-controlled-text/)
- [Unsupervised Evaluation of Interactive Dialog with DialoGPT](https://md.research.elamur.ai/free/en/papers/mehri-2020-unsupervised-evaluation-interactive-dialog-dialogpt/)
- [Release Strategies and the Social Impacts of Language Models](https://md.research.elamur.ai/free/en/papers/solaiman-2019-release-strategies-social-impacts-language/)
- [LaMDA: Language Models for Dialog Applications](https://md.research.elamur.ai/free/en/papers/thoppilan-2022-lamda-language-models-dialog-applications/)
- [Neural Text Generation with Unlikelihood Training](https://md.research.elamur.ai/free/en/papers/welleck-2019-neural-text-generation-unlikelihood-training/)
- [FUDGE: Controlled Text Generation With Future Discriminators](https://md.research.elamur.ai/free/en/papers/yang-2021-fudge-controlled-text-generation-future/)
- [Knowledge-Grounded Dialogue Generation with Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2020-knowledge-grounded-dialogue-generation-pre/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{see2019what,
  title = {{What makes a good conversation? How controllable attributes affect human judgments}},
  author = {See, Abigail and Roller, Stephen and Kiela, Douwe and Weston, Jason},
  booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
  year = {2019},
  doi = {10.18653/v1/n19-1170},
  url = {https://doi.org/10.18653/v1/n19-1170}
}
```

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## References

41 references, 17 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Sanjeev Arora, Yingyu Liang, and Tengyu Ma. 2017. A simple but tough-to-beat baseline for sentence embeddings. In Proceedings of the International Conference on Learning Representations (ICLR). [find](https://scholar.google.com/scholar?q=Sanjeev+Arora%2C+Yingyu+Liang%2C+and+Tengyu+Ma%2E+2017%2E+A+simple+but+tough%2Dto%2Dbeat+baseline+for+sentence+embeddings%2E+In+Proceedings+of+the+International+Conference+on+Learning+Representations+%28ICLR%29%2E)
2. Ashutosh Baheti, Alan Ritter, Jiwei Li, and Bill Dolan. 2018. Generating more interesting responses in neural conversation models with distributional constraints. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3970–3980. Association for Computational… [find](https://scholar.google.com/scholar?q=Ashutosh+Baheti%2C+Alan+Ritter%2C+Jiwei+Li%2C+and+Bill+Dolan%2E+2018%2E+Generating+more+interesting+responses+in+neural+conversation+models+with+distributional+constraints%2E+In+Proceedings+of+the+2018+Conference)
3. Antoine Bordes, Y-Lan Boureau, and Jason Weston. 2017. Learning end-to-end goal-oriented dialog. In Proceedings of the International Conference on Learning Representations (ICLR). [find](https://scholar.google.com/scholar?q=Antoine+Bordes%2C+Y%2DLan+Boureau%2C+and+Jason+Weston%2E+2017%2E+Learning+end%2Dto%2Dend+goal%2Doriented+dialog%2E+In+Proceedings+of+the+International+Conference+on+Learning+Representations+%28ICLR%29%2E)
4. Emily Dinan, Varvara Logacheva, Valentin Malykh, Alexander Miller, Kurt Shuster, Jack Urbanek, Douwe Kiela, Arthur Szlam, Iulian Serban, Ryan Lowe, et al. 2019. The second conversational intelligence challenge (convai2). arXiv preprint arXiv:1902.00098. [find](https://scholar.google.com/scholar?q=Emily+Dinan%2C+Varvara+Logacheva%2C+Valentin+Malykh%2C+Alexander+Miller%2C+Kurt+Shuster%2C+Jack+Urbanek%2C+Douwe+Kiela%2C+Arthur+Szlam%2C+Iulian+Serban%2C+Ryan+Lowe%2C+et+al%2E+2019%2E+The+second+conversational+intelligence+) [our card](https://md.research.elamur.ai/free/en/papers/dinan-2019-second-conversational-intelligence-challenge-convai2/)
5. Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2018. Wizard of Wikipedia: Knowledge-powered conversational agents. arXiv preprint arXiv:1811.01241. [find](https://scholar.google.com/scholar?q=Emily+Dinan%2C+Stephen+Roller%2C+Kurt+Shuster%2C+Angela+Fan%2C+Michael+Auli%2C+and+Jason+Weston%2E+2018%2E+Wizard+of+Wikipedia%3A+Knowledge%2Dpowered+conversational+agents%2E+arXiv+preprint+arXiv%3A1811%2E01241%2E)
6. Layla El Asri, Hannes Schulz, Shikhar Sharma, Jeremie Zumer, Justin Harris, Emery Fine, Rahul Mehrotra, and Kaheer Suleman. 2017. Frames: a corpus for adding memory to goal-oriented dialogue systems. In Proceedings of the 18th Annual SIGDIAL Meeting on Discourse and Dialogue, pages 207–219, Saarbrü… [find](https://scholar.google.com/scholar?q=Layla+El+Asri%2C+Hannes+Schulz%2C+Shikhar+Sharma%2C+Jeremie+Zumer%2C+Justin+Harris%2C+Emery+Fine%2C+Rahul+Mehrotra%2C+and+Kaheer+Suleman%2E+2017%2E+Frames%3A+a+corpus+for+adding+memory+to+goal%2Doriented+dialogue+systems%2E+) [our card](https://md.research.elamur.ai/free/en/papers/asri-2017-frames-corpus-adding-memory-goal/)
7. Angela Fan, David Grangier, and Michael Auli. 2018. Controllable abstractive summarization. In Proceedings of the 2nd Workshop on Neural Machine Translation and Generation, pages 45–54. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Angela+Fan%2C+David+Grangier%2C+and+Michael+Auli%2E+2018%2E+Controllable+abstractive+summarization%2E+In+Proceedings+of+the+2nd+Workshop+on+Neural+Machine+Translation+and+Generation%2C+pages+45%E2%80%9354%2E+Association+fo) [our card](https://md.research.elamur.ai/free/en/papers/fan-2018-controllable-abstractive-summarization/)
8. Jessica Ficler and Yoav Goldberg. 2017. Controlling linguistic style aspects in neural language generation. In Proceedings of the Workshop on Stylistic Variation, pages 94–104. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Jessica+Ficler+and+Yoav+Goldberg%2E+2017%2E+Controlling+linguistic+style+aspects+in+neural+language+generation%2E+In+Proceedings+of+the+Workshop+on+Stylistic+Variation%2C+pages+94%E2%80%93104%2E+Association+for+Computa) [our card](https://md.research.elamur.ai/free/en/papers/ficler-2017-controlling-linguistic-style-aspects-neural/)
9. Marjan Ghazvininejad, Xing Shi, Jay Priyadarshi, and Kevin Knight. 2017. Hafez: an interactive poetry generation system. In Proceedings of ACL 2017, System Demonstrations, pages 43– 48. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Marjan+Ghazvininejad%2C+Xing+Shi%2C+Jay+Priyadarshi%2C+and+Kevin+Knight%2E+2017%2E+Hafez%3A+an+interactive+poetry+generation+system%2E+In+Proceedings+of+ACL+2017%2C+System+Demonstrations%2C+pages+43%E2%80%93+48%2E+Association+fo)
10. Fenfei Guo, Angeliki Metallinou, Chandra Khatri, Anirudh Raju, Anu Venkatesh, and Ashwin Ram. 2018. Topic-based evaluation for conversational bots. Advances in Neural Information Processing Systems, Conversational AI Workshop. [find](https://scholar.google.com/scholar?q=Fenfei+Guo%2C+Angeliki+Metallinou%2C+Chandra+Khatri%2C+Anirudh+Raju%2C+Anu+Venkatesh%2C+and+Ashwin+Ram%2E+2018%2E+Topic%2Dbased+evaluation+for+conversational+bots%2E+Advances+in+Neural+Information+Processing+Systems%2C+C)
11. Helen Hastie. 2012. Metrics and evaluation of spoken dialogue systems, pages 131–150. Springer. [find](https://scholar.google.com/scholar?q=Helen+Hastie%2E+2012%2E+Metrics+and+evaluation+of+spoken+dialogue+systems%2C+pages+131%E2%80%93150%2E+Springer%2E)
12. Matthew Henderson, Blaise Thomson, and Jason D Williams. 2014. The second dialog state tracking challenge. In Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pages 263–272. [find](https://scholar.google.com/scholar?q=Matthew+Henderson%2C+Blaise+Thomson%2C+and+Jason+D+Williams%2E+2014%2E+The+second+dialog+state+tracking+challenge%2E+In+Proceedings+of+the+15th+Annual+Meeting+of+the+Special+Interest+Group+on+Discourse+and+Dial)
13. Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing. 2017. Toward controlled generation of text. In Thirty-fourth International Conference on Machine Learning. [find](https://scholar.google.com/scholar?q=Zhiting+Hu%2C+Zichao+Yang%2C+Xiaodan+Liang%2C+Ruslan+Salakhutdinov%2C+and+Eric+P+Xing%2E+2017%2E+Toward+controlled+generation+of+text%2E+In+Thirty%2Dfourth+International+Conference+on+Machine+Learning%2E) [our card](https://md.research.elamur.ai/free/en/papers/hu-2017-toward-controlled-generation-text/)
14. Yuta Kikuchi, Graham Neubig, Ryohei Sasano, Hiroya Takamura, and Manabu Okumura. 2016. Controlling output length in neural encoder-decoders. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1328–1338. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Yuta+Kikuchi%2C+Graham+Neubig%2C+Ryohei+Sasano%2C+Hiroya+Takamura%2C+and+Manabu+Okumura%2E+2016%2E+Controlling+output+length+in+neural+encoder%2Ddecoders%2E+In+Proceedings+of+the+2016+Conference+on+Empirical+Methods+) [our card](https://md.research.elamur.ai/free/en/papers/kikuchi-2016-controlling-output-length-neural-encoder/)
15. Ilya Kulikov, Alexander H Miller, Kyunghyun Cho, and Jason Weston. 2018. Importance of a search strategy in neural dialogue modelling. arXiv preprint arXiv:1811.00907. — [arXiv:1811.00907](https://arxiv.org/abs/1811.00907) [find](https://scholar.google.com/scholar?q=Ilya+Kulikov%2C+Alexander+H+Miller%2C+Kyunghyun+Cho%2C+and+Jason+Weston%2E+2018%2E+Importance+of+a+search+strategy+in+neural+dialogue+modelling%2E+arXiv+preprint+arXiv%3A1811%2E00907%2E+%E2%80%94+%5BarXiv%3A1811%2E00907%5D%28https%3A%2F%2Farx)
16. Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016a. A diversity-promoting objective function for neural conversation models. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pa… [find](https://scholar.google.com/scholar?q=Jiwei+Li%2C+Michel+Galley%2C+Chris+Brockett%2C+Jianfeng+Gao%2C+and+Bill+Dolan%2E+2016a%2E+A+diversity%2Dpromoting+objective+function+for+neural+conversation+models%2E+In+Proceedings+of+the+2016+Conference+of+the+Nort) [our card](https://md.research.elamur.ai/free/en/papers/li-2016-diversity-promoting-objective-function-neural/)
17. Jiwei Li, Will Monroe, and Dan Jurafsky. 2017a. Learning to decode for future success. arXiv preprint arXiv:1701.06549. — [arXiv:1701.06549](https://arxiv.org/abs/1701.06549) [find](https://scholar.google.com/scholar?q=Jiwei+Li%2C+Will+Monroe%2C+and+Dan+Jurafsky%2E+2017a%2E+Learning+to+decode+for+future+success%2E+arXiv+preprint+arXiv%3A1701%2E06549%2E+%E2%80%94+%5BarXiv%3A1701%2E06549%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1701%2E06549%29)
18. Jiwei Li, Will Monroe, Alan Ritter, Dan Jurafsky, Michel Galley, and Jianfeng Gao. 2016b. Deep reinforcement learning for dialogue generation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1192–1202, Austin, Texas. Association for Computational Ling… [find](https://scholar.google.com/scholar?q=Jiwei+Li%2C+Will+Monroe%2C+Alan+Ritter%2C+Dan+Jurafsky%2C+Michel+Galley%2C+and+Jianfeng+Gao%2E+2016b%2E+Deep+reinforcement+learning+for+dialogue+generation%2E+In+Proceedings+of+the+2016+Conference+on+Empirical+Method) [our card](https://md.research.elamur.ai/free/en/papers/li-2016-deep-reinforcement-learning-dialogue-generation/)
19. Jiwei Li, Will Monroe, Tianlin Shi, Sébastien Jean, Alan Ritter, and Dan Jurafsky. 2017b. Adversarial learning for neural dialogue generation. arXiv preprint arXiv:1701.06547. [find](https://scholar.google.com/scholar?q=Jiwei+Li%2C+Will+Monroe%2C+Tianlin+Shi%2C+Se%CC%81bastien+Jean%2C+Alan+Ritter%2C+and+Dan+Jurafsky%2E+2017b%2E+Adversarial+learning+for+neural+dialogue+generation%2E+arXiv+preprint+arXiv%3A1701%2E06547%2E) [our card](https://md.research.elamur.ai/free/en/papers/li-2017-adversarial-learning-neural-dialogue-generation/)
20. Chia-Wei Liu, Ryan Lowe, Iulian Serban, Mike Noseworthy, Laurent Charlin, and Joelle Pineau. 2016. How not to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation. pages 2122–2132. [find](https://scholar.google.com/scholar?q=Chia%2DWei+Liu%2C+Ryan+Lowe%2C+Iulian+Serban%2C+Mike+Noseworthy%2C+Laurent+Charlin%2C+and+Joelle+Pineau%2E+2016%2E+How+not+to+evaluate+your+dialogue+system%3A+An+empirical+study+of+unsupervised+evaluation+metrics+for+d)
21. Ryan Lowe, Michael Noseworthy, Iulian Vlad Serban, Nicolas Angelard-Gontier, Yoshua Bengio, and Joelle Pineau. 2017. Towards an automatic turing test: Learning to evaluate dialogue responses. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long P… [find](https://scholar.google.com/scholar?q=Ryan+Lowe%2C+Michael+Noseworthy%2C+Iulian+Vlad+Serban%2C+Nicolas+Angelard%2DGontier%2C+Yoshua+Bengio%2C+and+Joelle+Pineau%2E+2017%2E+Towards+an+automatic+turing+test%3A+Learning+to+evaluate+dialogue+responses%2E+In+Proce) [our card](https://md.research.elamur.ai/free/en/papers/lowe-2017-towards-automatic-turing-test-learning/)
22. Ryan Lowe, Nissan Pow, Iulian Serban, and Joelle Pineau. 2015. The Ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems. In Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 285– 294, Prague, Czech Republ… [find](https://scholar.google.com/scholar?q=Ryan+Lowe%2C+Nissan+Pow%2C+Iulian+Serban%2C+and+Joelle+Pineau%2E+2015%2E+The+Ubuntu+dialogue+corpus%3A+A+large+dataset+for+research+in+unstructured+multi%2Dturn+dialogue+systems%2E+In+Proceedings+of+the+16th+Annual+M) [our card](https://md.research.elamur.ai/free/en/papers/lowe-2015-ubuntu-dialogue-corpus-large-dataset/)
23. Alexander Miller, Will Feng, Dhruv Batra, Antoine Bordes, Adam Fisch, Jiasen Lu, Devi Parikh, and Jason Weston. 2017. ParlAI: A dialog research software platform. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 79–84, Copenhagen… [find](https://scholar.google.com/scholar?q=Alexander+Miller%2C+Will+Feng%2C+Dhruv+Batra%2C+Antoine+Bordes%2C+Adam+Fisch%2C+Jiasen+Lu%2C+Devi+Parikh%2C+and+Jason+Weston%2E+2017%2E+ParlAI%3A+A+dialog+research+software+platform%2E+In+Proceedings+of+the+2017+Conference) [our card](https://md.research.elamur.ai/free/en/papers/miller-2017-parlai-dialog-research-software-platform/)
24. Jekaterina Novikova, Ondřej Dušek, Amanda Cercas Curry, and Verena Rieser. 2017. Why we need new evaluation metrics for nlg. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2241–2252. [find](https://scholar.google.com/scholar?q=Jekaterina+Novikova%2C+Ondr%CC%8Cej+Dus%CC%8Cek%2C+Amanda+Cercas+Curry%2C+and+Verena+Rieser%2E+2017%2E+Why+we+need+new+evaluation+metrics+for+nlg%2E+In+Proceedings+of+the+2017+Conference+on+Empirical+Methods+in+Natural+Lan)
25. Prasanna Parthasarathi and Joelle Pineau. 2018. Extending neural generative conversational model using external knowledge sources. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 690–695, Brussels, Belgium. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Prasanna+Parthasarathi+and+Joelle+Pineau%2E+2018%2E+Extending+neural+generative+conversational+model+using+external+knowledge+sources%2E+In+Proceedings+of+the+2018+Conference+on+Empirical+Methods+in+Natural)
26. Nanyun Peng, Marjan Ghazvininejad, Jonathan May, and Kevin Knight. 2018. Towards controllable story generation. In Proceedings of the First Workshop on Storytelling, pages 43– 49. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Nanyun+Peng%2C+Marjan+Ghazvininejad%2C+Jonathan+May%2C+and+Kevin+Knight%2E+2018%2E+Towards+controllable+story+generation%2E+In+Proceedings+of+the+First+Workshop+on+Storytelling%2C+pages+43%E2%80%93+49%2E+Association+for+Comp)
27. Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. GloVe: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532– 1543, Doha, Qatar. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Jeffrey+Pennington%2C+Richard+Socher%2C+and+Christopher+Manning%2E+2014%2E+GloVe%3A+Global+vectors+for+word+representation%2E+In+Proceedings+of+the+2014+Conference+on+Empirical+Methods+in+Natural+Language+Process)
28. Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. [find](https://scholar.google.com/scholar?q=Alec+Radford%2C+Karthik+Narasimhan%2C+Tim+Salimans%2C+and+Ilya+Sutskever%2E+2018%2E+Improving+language+understanding+by+generative+pre%2Dtraining%2E)
29. Iulian Vlad Serban, Ryan Lowe, Laurent Charlin, and Joelle Pineau. 2016a. Generative deep neural networks for dialogue: A short review. Advances in Neural Information Processing Systems workshop on Learning Methods for Dialogue. [find](https://scholar.google.com/scholar?q=Iulian+Vlad+Serban%2C+Ryan+Lowe%2C+Laurent+Charlin%2C+and+Joelle+Pineau%2E+2016a%2E+Generative+deep+neural+networks+for+dialogue%3A+A+short+review%2E+Advances+in+Neural+Information+Processing+Systems+workshop+on+Le)
30. Iulian Vlad Serban, Alessandro Sordoni, Yoshua Bengio, Aaron C Courville, and Joelle Pineau. 2016b. Building end-to-end dialogue systems using generative hierarchical neural network models. In AAAI, volume 16, pages 3776–3784. [find](https://scholar.google.com/scholar?q=Iulian+Vlad+Serban%2C+Alessandro+Sordoni%2C+Yoshua+Bengio%2C+Aaron+C+Courville%2C+and+Joelle+Pineau%2E+2016b%2E+Building+end%2Dto%2Dend+dialogue+systems+using+generative+hierarchical+neural+network+models%2E+In+AAAI%2C+v) [our card](https://md.research.elamur.ai/free/en/papers/serban-2015-building-end-end-dialogue-systems/)
31. Xiaoyu Shen, Hui Su, Yanran Li, Wenjie Li, Shuzi Niu, Yang Zhao, Akiko Aizawa, and Guoping Long. 2017. A conditional variational framework for dialog generation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 504–509. Associ… [find](https://scholar.google.com/scholar?q=Xiaoyu+Shen%2C+Hui+Su%2C+Yanran+Li%2C+Wenjie+Li%2C+Shuzi+Niu%2C+Yang+Zhao%2C+Akiko+Aizawa%2C+and+Guoping+Long%2E+2017%2E+A+conditional+variational+framework+for+dialog+generation%2E+In+Proceedings+of+the+55th+Annual+Meet)
32. Anu Venkatesh, Chandra Khatri, Ashwin Ram, Fenfei Guo, Raefer Gabriel, Ashish Nagar, Rohit Prasad, Ming Cheng, Behnam Hedayatnia, Angeliki Metallinou, et al. 2017. On evaluating and comparing conversational agents. Advances in Neural Information Processing Systems, Conversational AI Workshop. [find](https://scholar.google.com/scholar?q=Anu+Venkatesh%2C+Chandra+Khatri%2C+Ashwin+Ram%2C+Fenfei+Guo%2C+Raefer+Gabriel%2C+Ashish+Nagar%2C+Rohit+Prasad%2C+Ming+Cheng%2C+Behnam+Hedayatnia%2C+Angeliki+Metallinou%2C+et+al%2E+2017%2E+On+evaluating+and+comparing+conversa)
33. Oriol Vinyals and Quoc Le. 2015. A neural conversational model. In Proceedings of the 31st International Conference on Machine Learning, Deep Learning Workshop, Lille, France. [find](https://scholar.google.com/scholar?q=Oriol+Vinyals+and+Quoc+Le%2E+2015%2E+A+neural+conversational+model%2E+In+Proceedings+of+the+31st+International+Conference+on+Machine+Learning%2C+Deep+Learning+Workshop%2C+Lille%2C+France%2E) [our card](https://md.research.elamur.ai/free/en/papers/vinyals-2015-neural-conversational-model/)
34. Marilyn A. Walker, Diane J. Litman, Candace A. Kamm, and Alicia Abella. 1997. PARADISE: A framework for evaluating spoken dialogue agents. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, pages 271–280, Madrid, Spain. Association for Computational Linguisti… [find](https://scholar.google.com/scholar?q=Marilyn+A%2E+Walker%2C+Diane+J%2E+Litman%2C+Candace+A%2E+Kamm%2C+and+Alicia+Abella%2E+1997%2E+PARADISE%3A+A+framework+for+evaluating+spoken+dialogue+agents%2E+In+Proceedings+of+the+35th+Annual+Meeting+of+the+Association+) [our card](https://md.research.elamur.ai/free/en/papers/walker-1997-paradise-framework-evaluating-spoken-dialogue/)
35. Di Wang, Nebojsa Jojic, Chris Brockett, and Eric Nyberg. 2017. Steering output style and topic in neural response generation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2140– 2150. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Di+Wang%2C+Nebojsa+Jojic%2C+Chris+Brockett%2C+and+Eric+Nyberg%2E+2017%2E+Steering+output+style+and+topic+in+neural+response+generation%2E+In+Proceedings+of+the+2017+Conference+on+Empirical+Methods+in+Natural+Lang)
36. Tsung-Hsien Wen, David Vandyke, Nikola Mrkšić, Milica Gasic, Lina M. Rojas Barahona, Pei-Hao Su, Stefan Ultes, and Steve Young. 2017. A network-based end-to-end trainable task-oriented dialogue system. In Proceedings of the 15th Conference of the European Chapter of the Association for Computation… [find](https://scholar.google.com/scholar?q=Tsung%2DHsien+Wen%2C+David+Vandyke%2C+Nikola+Mrks%CC%8Cic%CC%81%2C+Milica+Gasic%2C+Lina+M%2E+Rojas+Barahona%2C+Pei%2DHao+Su%2C+Stefan+Ultes%2C+and+Steve+Young%2E+2017%2E+A+network%2Dbased+end%2Dto%2Dend+trainable+task%2Doriented+dialogue+syst)
37. Chen Xing, Wei Wu, Yu Wu, Jie Liu, Yalou Huang, Ming Zhou, and Wei-Ying Ma. 2017. Topic aware neural response generation. In AAAI, volume 17, pages 3351–3357. [find](https://scholar.google.com/scholar?q=Chen+Xing%2C+Wei+Wu%2C+Yu+Wu%2C+Jie+Liu%2C+Yalou+Huang%2C+Ming+Zhou%2C+and+Wei%2DYing+Ma%2E+2017%2E+Topic+aware+neural+response+generation%2E+In+AAAI%2C+volume+17%2C+pages+3351%E2%80%933357%2E)
38. Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Jun Xu, and Xueqi Cheng. 2018a. Learning to control the specificity in neural response generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1108–1117, Melbourne, Austra… [find](https://scholar.google.com/scholar?q=Ruqing+Zhang%2C+Jiafeng+Guo%2C+Yixing+Fan%2C+Yanyan+Lan%2C+Jun+Xu%2C+and+Xueqi+Cheng%2E+2018a%2E+Learning+to+control+the+specificity+in+neural+response+generation%2E+In+Proceedings+of+the+56th+Annual+Meeting+of+the+A)
39. Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018b. Personalizing dialogue agents: I have a dog, do you have pets too? In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2204–2213, Melbo… [find](https://scholar.google.com/scholar?q=Saizheng+Zhang%2C+Emily+Dinan%2C+Jack+Urbanek%2C+Arthur+Szlam%2C+Douwe+Kiela%2C+and+Jason+Weston%2E+2018b%2E+Personalizing+dialogue+agents%3A+I+have+a+dog%2C+do+you+have+pets+too%3F+In+Proceedings+of+the+56th+Annual+Meet) [our card](https://md.research.elamur.ai/free/en/papers/zhang-2018-personalizing-dialogue-agents-i-have/)
40. Ganbin Zhou, Ping Luo, Rongyu Cao, Fen Lin, Bo Chen, and Qing He. 2017. Mechanism-aware neural machine for dialogue response generation. In AAAI, pages 3400–3407. [find](https://scholar.google.com/scholar?q=Ganbin+Zhou%2C+Ping+Luo%2C+Rongyu+Cao%2C+Fen+Lin%2C+Bo+Chen%2C+and+Qing+He%2E+2017%2E+Mechanism%2Daware+neural+machine+for+dialogue+response+generation%2E+In+AAAI%2C+pages+3400%E2%80%933407%2E)
41. Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In Proceedings of the IEEE international conference on computer vision, pages 19–… [find](https://scholar.google.com/scholar?q=Yukun+Zhu%2C+Ryan+Kiros%2C+Rich+Zemel%2C+Ruslan+Salakhutdinov%2C+Raquel+Urtasun%2C+Antonio+Torralba%2C+and+Sanja+Fidler%2E+2015%2E+Aligning+books+and+movies%3A+Towards+story%2Dlike+visual+explanations+by+watching+movies+) [our card](https://md.research.elamur.ai/free/en/papers/zhu-2015-aligning-books-movies-towards-story/)