---
title: "Mixing It Up — paper by Firdaus et al. (2023)"
description: "They define a new task of generating replies guided by one of these behaviours, given the dialogue… 61 references, cited by 1 paper here."
authors: ["Mauajama Firdaus", "Priyanshu Priya", "Asif Ekbal"]
year: 2023
venue: "Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)"
source: "https://aclanthology.org/2023.findings-ijcnlp.30/"
doi: "10.18653/v1/2023.findings-ijcnlp.30"
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/firdaus-2023-mixing-it-up-inducing-empathy/"
---
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# Mixing It Up: Inducing Empathy and Politeness using Multiple Behaviour-aware Generators for Conversational Systems

**Authors:** [Mauajama Firdaus](https://md.research.elamur.ai/free/en/authors/mauajama-firdaus/), [Priyanshu Priya](https://md.research.elamur.ai/free/en/authors/priyanshu-priya/) and [Asif Ekbal](https://md.research.elamur.ai/free/en/authors/asif-ekbal/) · **Year:** 2023 · **Venue:** [Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2023](https://md.research.elamur.ai/free/en/venues/findings/2023/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.findings-ijcnlp.30/) · [PDF](https://aclanthology.org/2023.findings-ijcnlp.30.pdf) · [DOI 10.18653/v1/2023.findings-ijcnlp.30](https://doi.org/10.18653/v1/2023.findings-ijcnlp.30) · [OpenAlex](https://openalex.org/W4392669929)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/firdaus-2023-mixing-it-up-inducing-empathy/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper addresses customer-care dialogue. An agent's reply should be helpful, and its form of politeness should also suit the customer's mood. The authors split politeness into five behaviours: appreciation, empathy, apology, greeting and assurance. They define a new task of generating replies guided by one of these behaviours, given the dialogue history and the user's affective state.

Their model is a Transformer encoder-decoder with three parts:
- **Affective tracker:** a hierarchical context encoder enriched with emotion and sentiment signals. It also predicts emotion and sentiment classes for the context.
- **Behaviour-aware generators:** several separate generators, each producing a behaviour-specific representation.
- **Polite generator:** a final generator that combines those representations through a weighted sum and writes the reply.

Experiments use the CYCCD customer-care corpus, extended with crowd-sourced behaviour labels. They show more relevant and context-appropriate replies than earlier politeness-generation systems and ablated variants. This matters because the same complaint can be answered in several polite ways, and choosing the right one affects user satisfaction.

## Key points
- The five polite behaviours modelled are Appreciation, Empathy, Apology, Greeting and Assurance. Each has its own behaviour-aware generator.
- Emotion features come from the output distribution of DeepMoji, which was pretrained on emoji prediction. Explicit sentiment embeddings are added alongside them.
- The utterance representation is a sum of five parts:
  - the encoder's EOS hidden state;
  - a positional embedding;
  - a speaker (customer vs. agent) embedding;
  - an emotion embedding;
  - a sentiment embedding.
- Words use GloVe vectors with sine-cosine positional encodings. A dialogue-level Transformer, which receives a prepended QRY token, then encodes the whole context.
- Evaluation covers only CYCCD, because no other behaviour-annotated data exists, and no LLMs were tried. The error analysis names two failure types: loss of context information, and choosing the wrong behaviour (e.g., thanking a customer who complained about stale food).

## Interesting ideas
- Instead of treating politeness as one style, the authors break it into named sub-behaviours, so one context can be paired with several valid, distinct replies.
- The user's emotion and sentiment are treated as features attached to every utterance, not as a single label for the whole dialogue. Each turn's affect can then shape what follows.
- Parallel specialised generators are blended by learned weights. This keeps each behaviour's contribution separate and inspectable before the final mix.
- The error taxonomy separates "lost content from context" from "right content, wrong social behaviour". These are two independent axes for judging a reply.
- A speaker embedding marks whose turn is whose, so the encoder can tell the customer's voice from the agent's.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (back): when generating a reply from the meaning graph, the speech-act layer could pick an explicit politeness strategy (apologise, assure, greet) as a controllable, explainable slot rather than a fuzzy style.
- **I2** (frames): prag could store per-utterance affect (negative/frustrated, positive/joy) next to the speech act, giving implicature and irony detection a turn-level signal.
- **I3** (back): separate realisers per communicative function could each propose a surface form, and the system would record which one dominated and why.
- **I4** (learn): evaluation of generated answers could score factual coverage and pragmatic appropriateness separately, with deliberately mismatched behaviours used as negative controls.
- **I5** (world): the world of a text could tag each statement with its speaker, so that holdings and states asserted by different participants are kept apart.

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

## Institutions

- [University of Alberta](https://md.research.elamur.ai/free/en/universities/university-of-alberta/) — University, Edmonton, Canada · 44 works · 3,236 citations
- [Indian Institute of Technology Patna](https://md.research.elamur.ai/free/en/universities/indian-institute-of-technology-patna/) — University, Patna, India · 21 works · 374 citations

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## Cite

Cite as: Mauajama Firdaus, Priyanshu Priya and Asif Ekbal. 2023. Mixing It Up: Inducing Empathy and Politeness using Multiple Behaviour-aware Generators for Conversational Systems. Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings).

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

```bibtex
@inproceedings{firdaus2023mixing,
  title = {{Mixing It Up: Inducing Empathy and Politeness using Multiple Behaviour-aware Generators for Conversational Systems}},
  author = {Mauajama Firdaus, Priyanshu Priya and Asif Ekbal},
  booktitle = {Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)},
  year = {2023},
  doi = {10.18653/v1/2023.findings-ijcnlp.30},
  url = {https://doi.org/10.18653/v1/2023.findings-ijcnlp.30}
}
```

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## References

61 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Jaime Cesar Acosta. 2009. Using emotion to gain rapport in a spoken dialog system. [find](https://scholar.google.com/scholar?q=Jaime+Cesar+Acosta%2E+2009%2E+Using+emotion+to+gain+rapport+in+a+spoken+dialog+system%2E)
2. Russell Beale and Chris Creed. 2009. Affective interaction: How emotional agents affect users. International journal of human-computer studies, 67(9):755– 776. [↗](https://doi.org/10.1016/j.ijhcs.2009.05.001)
3. Timothy W Bickmore, Laura M Pfeifer, and Brian W Jack. 2009. Taking the time to care: empowering low health literacy hospital patients with virtual nurse agents. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 1265– 1274. [find](https://scholar.google.com/scholar?q=Timothy+W+Bickmore%2C+Laura+M+Pfeifer%2C+and+Brian+W+Jack%2E+2009%2E+Taking+the+time+to+care%3A+empowering+low+health+literacy+hospital+patients+with+virtual+nurse+agents%2E+In+Proceedings+of+the+SIGCHI+conferenc)
4. Timothy W Bickmore and Rosalind W Picard. 2005. Establishing and maintaining long-term human-computer relationships. ACM Transactions on Computer-Human Interaction (TOCHI), 12(2):293– 327. [↗](https://doi.org/10.1145/1067860.1067867)
5. Penelope Brown, Stephen C Levinson, and Stephen C Levinson. 1987. Politeness: Some universals in language usage, volume 4. Cambridge university press. [find](https://scholar.google.com/scholar?q=Penelope+Brown%2C+Stephen+C+Levinson%2C+and+Stephen+C+Levinson%2E+1987%2E+Politeness%3A+Some+universals+in+language+usage%2C+volume+4%2E+Cambridge+university+press%2E)
6. Stanley Chen, Douglas H. Beeferman, and Ronald Rosenfeld. 1998. Evaluation metrics for language models. [find](https://scholar.google.com/scholar?q=Stanley+Chen%2C+Douglas+H%2E+Beeferman%2C+and+Ronald+Rosenfeld%2E+1998%2E+Evaluation+metrics+for+language+models%2E)
7. Cristian Danescu-Niculescu-Mizil, Moritz Sudhof, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013. [A computational approach to politeness with application to social factors](https://md.research.elamur.ai/free/en/papers/mizil-2013-computational-approach-politeness-application-social/). arXiv preprint arXiv:1306.6078. [↗](https://arxiv.org/abs/1306.6078)
8. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. [BERT: pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pages 4171–4186.
9. Isabel Dias, Ricardo Rei, Patrícia Pereira, and Luisa Coheur. 2022. Towards a sentiment-aware conversational agent. In Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents, pages 1–3. [find](https://scholar.google.com/scholar?q=Isabel+Dias%2C+Ricardo+Rei%2C+Patr%C3%ADcia+Pereira%2C+and+Luisa+Coheur%2E+2022%2E+Towards+a+sentiment%2Daware+conversational+agent%2E+In+Proceedings+of+the+22nd+ACM+International+Conference+on+Intelligent+Virtual+Agent)
10. Nan Duan, Duyu Tang, Peng Chen, and Ming Zhou. 2017. [Question generation for question answering](https://md.research.elamur.ai/free/en/papers/duan-2017-question-generation-question-answering/). In Proceedings of the 2017 conference on empirical methods in natural language processing, pages 866– 874.
11. Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rah-wan, and Sune Lehmann. 2017. [Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://md.research.elamur.ai/free/en/papers/felbo-2017-millions-emoji-occurrences-learn-any/). In Conference on Empirical Methods in Natural Language Processing (EMNLP).
12. Julia Fink. 2012. Anthropomorphism and human likeness in the design of robots and human-robot interaction. In International conference on social robotics, pages 199–208. Springer. [find](https://scholar.google.com/scholar?q=Julia+Fink%2E+2012%2E+Anthropomorphism+and+human+likeness+in+the+design+of+robots+and+human%2Drobot+interaction%2E+In+International+conference+on+social+robotics%2C+pages+199%E2%80%93208%2E+Springer%2E)
13. Mauajama Firdaus, Hardik Chauhan, Asif Ekbal, and Pushpak Bhattacharyya. 2020a. Emosen: Generating sentiment and emotion controlled responses in a multimodal dialogue system. IEEE Transactions on Affective Computing, 13(3):1555–1566. [↗](https://doi.org/10.1109/taffc.2020.3015491)
14. Mauajama Firdaus, Hardik Chauhan, Asif Ekbal, and Pushpak Bhattacharyya. 2020b. Meisd: A multimodal multi-label emotion, intensity and sentiment dialogue dataset for emotion recognition and sentiment analysis in conversations. In Proceedings of the 28th international conference on computational linguistics, pages 4441–4453. [find](https://scholar.google.com/scholar?q=Mauajama+Firdaus%2C+Hardik+Chauhan%2C+Asif+Ekbal%2C+and+Pushpak+Bhattacharyya%2E+2020b%2E+Meisd%3A+A+multimodal+multi%2Dlabel+emotion%2C+intensity+and+sentiment+dialogue+dataset+for+emotion+recognition+and+sentiment+)
15. Mauajama Firdaus, Hardik Chauhan, Asif Ekbal, and Pushpak Bhattacharyya. 2021a. More the merrier: Towards multi-emotion and intensity controllable response generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 12821–12829. [find](https://scholar.google.com/scholar?q=Mauajama+Firdaus%2C+Hardik+Chauhan%2C+Asif+Ekbal%2C+and+Pushpak+Bhattacharyya%2E+2021a%2E+More+the+merrier%3A+Towards+multi%2Demotion+and+intensity+controllable+response+generation%2E+In+Proceedings+of+the+AAAI+Confe)
16. Mauajama Firdaus, Asif Ekbal, and Pushpak Bhattacharyya. 2020c. Incorporating politeness across languages in customer care responses: Towards building a multi-lingual empathetic dialogue agent. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 4172–4182. [find](https://scholar.google.com/scholar?q=Mauajama+Firdaus%2C+Asif+Ekbal%2C+and+Pushpak+Bhattacharyya%2E+2020c%2E+Incorporating+politeness+across+languages+in+customer+care+responses%3A+Towards+building+a+multi%2Dlingual+empathetic+dialogue+agent%2E+In+Pro)
17. Mauajama Firdaus, Asif Ekbal, and Pushpak Bhattacharyya. 2022a. [Polise: Reinforcing politeness using user sentiment for customer care response generation](https://md.research.elamur.ai/free/en/papers/firdaus-2022-polise-reinforcing-politeness-user-sentiment/). In Proceedings of the 29th International Conference on Computational Linguistics, pages 6165– 6175.
18. Mauajama Firdaus, Umang Jain, Asif Ekbal, and Pushpak Bhattacharyya. 2021b. Seprg: sentiment aware emotion controlled personalized response generation. In Proceedings of the 14th International Conference on Natural Language Generation, pages 353–363. [find](https://scholar.google.com/scholar?q=Mauajama+Firdaus%2C+Umang+Jain%2C+Asif+Ekbal%2C+and+Pushpak+Bhattacharyya%2E+2021b%2E+Seprg%3A+sentiment+aware+emotion+controlled+personalized+response+generation%2E+In+Proceedings+of+the+14th+International+Confere)
19. Mauajama Firdaus, Arunav Shandilya, Asif Ekbal, and Pushpak Bhattacharyya. 2022b. Being polite: Modeling politeness variation in a personalized dialog agent. IEEE Transactions on Computational Social Systems. [↗](https://doi.org/10.1109/tcss.2022.3182986)
20. Mauajama Firdaus, Gopendra Vikram Singh, Asif Ekbal, and Pushpak Bhattacharyya. 2023. Affect-gcn: a multimodal graph convolutional network for multi-emotion with intensity recognition and sentiment analysis in dialogues. Multimedia Tools and Applications, pages 1–22. [↗](https://doi.org/10.1007/s11042-023-14885-1)
21. Mauajama Firdaus, Nidhi Thakur, and Asif Ekbal. 2022c. Sentiment guided aspect conditioned dialogue generation in a multimodal system. In European Conference on Information Retrieval, pages 199–214. Springer. [find](https://scholar.google.com/scholar?q=Mauajama+Firdaus%2C+Nidhi+Thakur%2C+and+Asif+Ekbal%2E+2022c%2E+Sentiment+guided+aspect+conditioned+dialogue+generation+in+a+multimodal+system%2E+In+European+Conference+on+Information+Retrieval%2C+pages+199%E2%80%93214%2E+S)
22. Mauajama Firdaus, Naveen Thangavelu, Asif Ekba, and Pushpak Bhattacharyya. 2020d. Persona aware response generation with emotions. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE. [find](https://scholar.google.com/scholar?q=Mauajama+Firdaus%2C+Naveen+Thangavelu%2C+Asif+Ekba%2C+and+Pushpak+Bhattacharyya%2E+2020d%2E+Persona+aware+response+generation+with+emotions%2E+In+2020+International+Joint+Conference+on+Neural+Networks+%28IJCNN%29%2C+pa)
23. Mauajama Firdaus, Naveen Thangavelu, Asif Ekbal, and Pushpak Bhattacharyya. 2022d. I enjoy writing and playing, do you: A personalized and emotion grounded dialogue agent using generative adversarial network. IEEE Transactions on Affective Computing. [↗](https://doi.org/10.1109/taffc.2022.3155105)
24. Joseph L Fleiss. 1971. Measuring nominal scale agreement among many raters. Psychological bulletin, 76(5):378. [↗](https://doi.org/10.1037/h0031619)
25. Swati Gupta, Marilyn A Walker, and Daniela M Romano. 2007. How rude are you?: Evaluating politeness and affect in interaction. In International Conference on Affective Computing and Intelligent Interaction, pages 203–217. Springer. [find](https://scholar.google.com/scholar?q=Swati+Gupta%2C+Marilyn+A+Walker%2C+and+Daniela+M+Romano%2E+2007%2E+How+rude+are+you%3F%3A+Evaluating+politeness+and+affect+in+interaction%2E+In+International+Conference+on+Affective+Computing+and+Intelligent+Intera)
26. Jonathan Herzig, Guy Feigenblat, Michal Shmueli-Scheuer, David Konopnicki, Anat Rafaeli, Daniel Altman, and David Spivak. 2016. Classifying emotions in customer support dialogues in social media. In SIGDIAL Conference, pages 64–73. [find](https://scholar.google.com/scholar?q=Jonathan+Herzig%2C+Guy+Feigenblat%2C+Michal+Shmueli%2DScheuer%2C+David+Konopnicki%2C+Anat+Rafaeli%2C+Daniel+Altman%2C+and+David+Spivak%2E+2016%2E+Classifying+emotions+in+customer+support+dialogues+in+social+media%2E+In+S)
27. Sathish Reddy Indurthi, Dinesh Raghu, Mitesh M Khapra, and Sachindra Joshi. 2017. Generating natural language question-answer pairs from a knowledge graph using a rnn based question generation model. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 376–385. [find](https://scholar.google.com/scholar?q=Sathish+Reddy+Indurthi%2C+Dinesh+Raghu%2C+Mitesh+M+Khapra%2C+and+Sachindra+Joshi%2E+2017%2E+Generating+natural+language+question%2Danswer+pairs+from+a+knowledge+graph+using+a+rnn+based+question+generation+model%2E+)
28. Mohit Jain, Pratyush Kumar, Ramachandra Kota, and Shwetak N Patel. 2018. Evaluating and informing the design of chatbots. In Proceedings of the 2018 designing interactive systems conference, pages 895– 906. [find](https://scholar.google.com/scholar?q=Mohit+Jain%2C+Pratyush+Kumar%2C+Ramachandra+Kota%2C+and+Shwetak+N+Patel%2E+2018%2E+Evaluating+and+informing+the+design+of+chatbots%2E+In+Proceedings+of+the+2018+designing+interactive+systems+conference%2C+pages+895)
29. Q Vera Liao, Matthew Davis, Werner Geyer, Michael Muller, and N Sadat Shami. 2016. What can you do? studying social-agent orientation and agent proactive interactions with an agent for employees. In Proceedings of the 2016 acm conference on designing interactive systems, pages 264–275. [find](https://scholar.google.com/scholar?q=Q+Vera+Liao%2C+Matthew+Davis%2C+Werner+Geyer%2C+Michael+Muller%2C+and+N+Sadat+Shami%2E+2016%2E+What+can+you+do%3F+studying+social%2Dagent+orientation+and+agent+proactive+interactions+with+an+agent+for+employees%2E+In+P)
30. Chin-Yew Lin. 2004. [Rouge: a package for automatic evaluation of summaries](https://md.research.elamur.ai/free/en/papers/lin-2004-rouge-package-automatic-evaluation-summaries/).
31. Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, and Shrimai Prabhumoye. 2020. [Politeness transfer: A tag and generate approach](https://md.research.elamur.ai/free/en/papers/madaan-2020-politeness-transfer-tag-generate-approach/). arXiv preprint arXiv:2004.14257. [↗](https://arxiv.org/abs/2004.14257)
32. Avinash Madasu, Mauajama Firdaus, and Asif Eqbal. 2022. A unified framework for emotion identification and generation in dialogues. arXiv preprint arXiv:2205.15513. [↗](https://arxiv.org/abs/2205.15513)
33. Abraham Harold Maslow. 1943. A theory of human motivation. Psychological review, 50(4):370. [↗](https://doi.org/10.1037/h0054346)
34. Mary L McHugh. 2012. Interrater reliability: The kappa statistic. Biochemia medica: Biochemia medica, 22(3):276–282. [↗](https://doi.org/10.11613/bm.2012.031)
35. Asbjørn Følstad and Marita Skjuve. 2019. Chatbots for customer service: user experience and motivation. In Proceedings of the 1st international conference on conversational user interfaces, pages 1–9. [find](https://scholar.google.com/scholar?q=Asbj%C3%B8rn+F%C3%B8lstad+and+Marita+Skjuve%2E+2019%2E+Chatbots+for+customer+service%3A+user+experience+and+motivation%2E+In+Proceedings+of+the+1st+international+conference+on+conversational+user+interfaces%2C+pages+1%E2%80%939%2E)
36. Hitesh Golchha, Mauajama Firdaus, Asif Ekbal, and Pushpak Bhattacharyya. 2019. Courteously yours: inducing courteous behavior in customer care responses using reinforced pointer generator network. In 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), pages 851–860. [find](https://scholar.google.com/scholar?q=Hitesh+Golchha%2C+Mauajama+Firdaus%2C+Asif+Ekbal%2C+and+Pushpak+Bhattacharyya%2E+2019%2E+Courteously+yours%3A+inducing+courteous+behavior+in+customer+care+responses+using+reinforced+pointer+generator+network%2E+In+)
37. Alexander Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, and Jason Weston. 2016. Key-value memory networks for directly reading documents. arXiv preprint arXiv:1606.03126. [↗](https://arxiv.org/abs/1606.03126)
38. Kshitij Mishra, Mauajama Firdaus, and Asif Ekbal. 2022a. Please be polite: Towards building a politeness adaptive dialogue system for goal-oriented conversations. Neurocomputing, 494:242–254. [↗](https://doi.org/10.1016/j.neucom.2022.04.029)
39. Kshitij Mishra, Mauajama Firdaus, and Asif Ekbal. 2022b. Predicting politeness variations in goal-oriented conversations. IEEE Transactions on Computational Social Systems. [↗](https://doi.org/10.1109/tcss.2022.3156580)
40. Kshitij Mishra, Mauajama Firdaus, and Asif Ekbal. 2023a. Genpads: Reinforcing politeness in an end-to-end dialogue system. Plos one, 18(1):e0278323. [↗](https://doi.org/10.1371/journal.pone.0278323)
41. Kshitij Mishra, Priyanshu Priya, and Asif Ekbal. 2023b. Help me heal: A reinforced polite and empathetic mental health and legal counseling dialogue system for crime victims. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 14408–14416. [find](https://scholar.google.com/scholar?q=Kshitij+Mishra%2C+Priyanshu+Priya%2C+and+Asif+Ekbal%2E+2023b%2E+Help+me+heal%3A+A+reinforced+polite+and+empathetic+mental+health+and+legal+counseling+dialogue+system+for+crime+victims%2E+In+Proceedings+of+the+AAA)
42. Kshitij Mishra, Priyanshu Priya, and Asif Ekbal. 2023c. [Pal to lend a helping hand: Towards building an emotion adaptive polite and empathetic counseling conversational agent](https://md.research.elamur.ai/free/en/papers/mishra-2023-pal-lend-helping-hand-building/). In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12254– 12271.
43. Tong Niu and Mohit Bansal. 2018a. Polite dialogue generation without parallel data. Transactions of the Association for Computational Linguistics, 6:373– 389. [↗](https://doi.org/10.1162/tacl_a_00027)
44. Tong Niu and Mohit Bansal. 2018b. Polite dialogue generation without parallel data. TACL, 6:373–389. [↗](https://doi.org/10.1162/tacl_a_00027)
45. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. [Bleu: a method for automatic evaluation of machine translation](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/). In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, July 6-12, 2002, Philadelphia, PA, USA, pages 311–318. Association for Computational Linguistics.
46. Sashank J Reddi, Satyen Kale, and Sanjiv Kumar. 2019. On the convergence of adam and beyond. 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. [↗](https://doi.org/10.48550/arxiv.1904.09237)
47. Azlaan Mustafa Samad, Kshitij Mishra, Mauajama Firdaus, and Asif Ekbal. 2022. Empathetic persuasion: reinforcing empathy and persuasiveness in dialogue systems. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 844–856. [find](https://scholar.google.com/scholar?q=Azlaan+Mustafa+Samad%2C+Kshitij+Mishra%2C+Mauajama+Firdaus%2C+and+Asif+Ekbal%2E+2022%2E+Empathetic+persuasion%3A+reinforcing+empathy+and+persuasiveness+in+dialogue+systems%2E+In+Findings+of+the+Association+for+Comp)
48. Iulian V Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau. 2015. [Hierarchical neural network generative models for movie dialogues](https://md.research.elamur.ai/free/en/papers/serban-2015-building-end-end-dialogue-systems/). arXiv preprint arXiv:1507.04808, 7(8):434– 441. [↗](https://arxiv.org/abs/1507.04808)
49. Xiaoyu Shen, Hui Su, Shuzi Niu, and Vera Demberg. 2018. Improving variational encoder-decoders in dialogue generation. In Proceedings of the AAAI conference on artificial intelligence, volume 32. [↗](https://doi.org/10.22028/d291-30974)
50. Weiyan Shi and Zhou Yu. 2018. Sentiment adaptive end-to-end dialog systems. arXiv preprint arXiv:1804.10731. [↗](https://arxiv.org/abs/1804.10731)
51. Gopendra Vikram Singh, Mauajama Firdaus, Asif Ekbal, and Pushpak Bhattacharyya. 2022. Emoint-trans: A multimodal transformer for identifying emotions and intents in social conversations. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31:290–300. [↗](https://doi.org/10.1109/taslp.2022.3224287)
52. Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. [Sequence to sequence learning with neural networks](https://md.research.elamur.ai/free/en/papers/sutskever-2014-sequence-sequence-learning-neural-networks/). In Advances in neural information processing systems, pages 3104–3112. [↗](https://doi.org/10.48550/arxiv.1409.3215)
53. Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. [Glove: Global vectors for word representation](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/). In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pages 1532–1543.
54. Zhiliang Tian, Wei Bi, Xiaopeng Li, and Nevin L Zhang. 2019. Learning to abstract for memory-augmented conversational response generation. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pages 3816–3825. [find](https://scholar.google.com/scholar?q=Zhiliang+Tian%2C+Wei+Bi%2C+Xiaopeng+Li%2C+and+Nevin+L+Zhang%2E+2019%2E+Learning+to+abstract+for+memory%2Daugmented+conversational+response+generation%2E+In+Proceedings+of+the+57th+Conference+of+the+Association+for+)
55. Johannes Pittermann, Angela Pittermann, and Wolfgang Minker. 2010. Emotion recognition and adaptation in spoken dialogue systems. International Journal of Speech Technology, 13(1):49–60. [↗](https://doi.org/10.1007/s10772-010-9068-y)
56. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. [Attention is all you need](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/). In Advances in neural information processing systems, pages 5998–6008.
57. Priyanshu Priya, Mauajama Firdaus, and Asif Ekbal. 2023a. A multi-task learning framework for politeness and emotion detection in dialogues for mental health counselling and legal aid. Expert Systems with Applications, 224:120025. [↗](https://doi.org/10.1016/j.eswa.2023.120025)
58. Priyanshu Priya, Kshitij Mishra, Palak Totala, and Asif Ekbal. 2023b. Partner: A persuasive mental health and legal counselling dialogue system for women and children crime victims. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, pages 6183–6191. International Joint Conferences on Artificial Intelligence Organization. AI for Good. [find](https://scholar.google.com/scholar?q=Priyanshu+Priya%2C+Kshitij+Mishra%2C+Palak+Totala%2C+and+Asif+Ekbal%2E+2023b%2E+Partner%3A+A+persuasive+mental+health+and+legal+counselling+dialogue+system+for+women+and+children+crime+victims%2E+In+Proceedings+of+)
59. Jiancheng Wang, Jingjing Wang, Changlong Sun, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, and Guodong Zhou. 2020a. Sentiment classification in customer service dialogue with topic-aware multi-task learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 9177–9184. [find](https://scholar.google.com/scholar?q=Jiancheng+Wang%2C+Jingjing+Wang%2C+Changlong+Sun%2C+Shoushan+Li%2C+Xiaozhong+Liu%2C+Luo+Si%2C+Min+Zhang%2C+and+Guodong+Zhou%2E+2020a%2E+Sentiment+classification+in+customer+service+dialogue+with+topic%2Daware+multi%2Dtask+)
60. Yi-Chia Wang, Alexandros Papangelis, Runze Wang, Zhaleh Feizollahi, Gokhan Tur, and Robert Kraut. 2020b. [Can you be more social? injecting politeness and positivity into task-oriented conversational agents](https://md.research.elamur.ai/free/en/papers/wang-2020-more-social-injecting-politeness-positivity/). arXiv preprint arXiv:2012.14653. [↗](https://arxiv.org/abs/2012.14653)
61. Hainan Zhang, Yanyan Lan, Jiafeng Guo, Jun Xu, and Xueqi Cheng. 2018. Reinforcing coherence for sequence to sequence model in dialogue generation. In IJCAI, pages 4567–4573. [find](https://scholar.google.com/scholar?q=Hainan+Zhang%2C+Yanyan+Lan%2C+Jiafeng+Guo%2C+Jun+Xu%2C+and+Xueqi+Cheng%2E+2018%2E+Reinforcing+coherence+for+sequence+to+sequence+model+in+dialogue+generation%2E+In+IJCAI%2C+pages+4567%E2%80%934573%2E)

*Updated 1 November 2026*