SlowGuess/NAACL_2022_OCR huggingface dataset:
provement on BLEU precision, which is similar to (Zhao et al., 2017; Gu et al., 2019) because the precision is penalized when the model generates diverse responses. Also, the distinct value of unigrams and bigrams are  $32.04\%$  and  $19.32\%$  higher than the previous SOTA, respectively. As shown in Figure 4 and Figure 6, the empathy intents of EmpHi's responses are more diverse than existing models, so the distinct scores improve significantly. Our method enhances the relevance and diversity simultaneously, which proves the effectiveness of human-like intent in empathetic response generation.
6.1.3 Empathetic Intent Distribution
We apply the same approach in Section 3 and quantify the empathetic intent distribution of EmpHi's responses to prove that EmpHi accurately captures humans' empathetic intent distribution. Comparing Figure 4 and Figure 6, the difference between them illustrates that our model successfully reduces the bias of empathetic expression. The KL-divergence of intent distributions between models and humans are 0.025 for EmpHi, 1.949 for MIME, 1.545 for MoEL, and 4.570 for Multitask-Transformer (See in Appendix A).
We evaluate each component of EmpHi using BLEU and ACC, where ACC indicates the accuracy of predicted empaththetic intent of generated response. Since each conversation could have multiple empathetic responses, the ACC of  $26.8\%$  is pretty ideal. As seen in Table 3, there is a dra
Emotion	Nostalgic
Context	When my wife and i started dating, our second date ended up lasting like 12 hours.
Reference	That is a pretty long date! Where did you guys go?
Multitask-Trans	That is so sad, I'm sorry to hear that.
MoEL	That is great! I am sure she will be fine.
MIME	That is a good thing to do. I am sure you will do great.
EmpHi	That ’s awesome! How long have you been together?
Emotion	Angry
Turn-1	I just moved to this neighborhood and some dumb criminals shot one of my neighbors and ran into the woods!
Turn-2	That’s not good. Do you own a gun?
Turn-3	I do! I want to be able to protect my son.
Reference	That is always number one goal.
Multitask-Trans	What did you do?
MoEL	That is a good idea.
MIME	I am sorry to hear that. I hope you get it!
EmpHi	Maybe you should go to the police.
Table 4: Comparison among EmpHi and other empathetic dialogue models.
Figure 7: Case study of EmpHi.
matic drop in the performance of EmpHi without any intent information (both implicit embedding and explicit keywords). Therefore, this proves the effectiveness of empathetic intents and the intent representation learning approach. As for implicit gate control, it improves both response quality and intent accuracy since it helps EmpHi dynamically capture intent information during generation. Same conclusion has been made in (Zhou et al., 2018). The copy mechanism provides EmpHi the ability to explicitly use intent keywords and thus contributes to the intent accuracy.
Intent-level diverse generation. Through sampling intents in the discrete latent space, EmpHi generates different responses with empathetic intents. As in Figure 7, the speaker shows an exciting emotion for getting a better job. EmpHi
generates empathetic yet contextually relevant responses as humans. Besides, EmpHi predicts the potential intent distribution and shows successful conditional generation based on the corresponding intents, which improves the interpretability and controllability of empathetic response generation. See Appendix C for error analysis.
Compare with existing models. For the first instance in Table 4, even though baseline models show naive empathy in their response, it is hard for the speaker to feel empathy because the response is not relevant to the topic. In contrast, EmpHi shows its understanding of the speaker's feelings and asks a relevant question to explore the speaker's experience. For second case, all baselines express contextually irrelevant empathy, while EmpHi truly understands the dialogue history and put itself into speaker's situation, then further reply: "Maybe you should go to the police" with the Suggesting intent.
Overall, we reveal the severe bias of empathetic expression between existing dialogue models and humans. To address this issue, this paper proposes EmpHi to generate empathetic responses with human-like empathetic intents. As a result, both automatic and human evaluation prove that EmpHi has a huge improvement on empathetic conversation. According to the anlaysis and case studies, EmpHi successfully learns the emaphetic intent distribution of human and shows high interpretability and controllability during the generation process. We will try large pretrained language models with empathetic intent in our future work.
Ethical Statement
Since this paper involves subjects related to human conversation, we have ensured that all the experiments will cause no harm to humans. The dataset EmpatheticDialogues is collected by (Rashkin et al., 2019), all the participants join the data collection voluntarily. Also, the dataset provider filters all personal information and obscene languages. Therefore, we believe that the dataset EmpatheticDialogues used in our experiments are harmless to users, and the model trained on this dataset is not dangerous to humans.
We thank the anonymous reviewers for their insightful comments and suggestions. This research was supported in part by the National Key Research and Development Program of China (No. 2020YFB1708200), the Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515011387) and the Shenzhen Key Laboratory of Marine IntellSense and Computation under Contract ZDSYS20200811142605016.
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In 3rd International Conference on Learning Representations, ICLR 2015.
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, and Samy Bengio. 2016. Generating sentences from a continuous space. In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016, Berlin, Germany, August 11-12, 2016, pages 10-21. ACL.
Sven Buechel, Anneke Buffone, Barry Slaff, Lyle H. Ungar, and João Sedoc. 2018. Modeling empathy and distress in reaction to news stories. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, pages 4758-4765. Association for Computational Linguistics.
Boxing Chen and Colin Cherry. 2014. A systematic comparison of smoothing techniques for sentence-level BLEU. In Proceedings of the Ninth Workshop on Statistical Machine Translation, WMT@ACL 2014, June 26-27, 2014, Baltimore, Maryland, USA, pages 362-367. The Association for Computer Linguistics.
Junyoung Chung, Caglar Gülcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of
gated recurrent neural networks on sequence modeling. CoRR, abs/1412.3555.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: pre-training of deep bidirectional transformers for language understanding. In 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. Association for Computational Linguistics.
Sayan Ghosh, Mathieu Chollet, Eugene Laksana, Louis-Philippe Morency, and Stefan Scherer. 2017. Affectlm: A neural language model for customizable affective text generation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, pages 634-642. Association for Computational Linguistics.
Xiaodong Gu, Kyunghyun Cho, Jung-Woo Ha, and Sunghun Kim. 2019. Dialogwae: Multimodal response generation with conditional Wasserstein autoencoder. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net.
Diederik P. Kingma and Max Welling. 2014. Autoencoding variational bayes. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings.
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016. A diversity-promoting objective function for neural conversation models. In *NAACL HLT* 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego California, USA, June 12-17, 2016, pages 110-119. The Association for Computational Linguistics.
Zhaojiang Lin, Andrea Madotto, Jamin Shin, Peng Xu, and Pascale Fung. 2019. Moel: Mixture of empathetic listeners. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, pages 121-132. Association for Computational Linguistics.
Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander F. Gelbukh, Rada Mihalcea, and Soujanya Poria. 2020. MIME: mimicking emotions for empathetic response generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pages 8968-8979. Association for Computational Linguistics.
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, July 6-12, 2002, Philadelphia, PA, USA, pages 311-318. ACL.
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for 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. ACL.
Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. 2019. Towards empathetic open-domain conversation models: A new benchmark and dataset. 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 5370-5381. Association for Computational Linguistics.
Ashish Sharma, Inna W Lin, Adam S Miner, David C Atkins, and Tim Althoff. 2021. Towards facilitating empathic conversations in online mental health support: A reinforcement learning approach. In WWW.
Ashish Sharma, Adam S. Miner, David C. Atkins, and Tim Althoff. 2020. A computational approach to understanding empathy expressed in text-based mental health support. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pages 5263-5276. Association for Computational Linguistics.
Anuradha Welivita and Pearl Pu. 2020. A taxonomy of empathetic response intents in human social conversations. In Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020, pages 4886-4899. International Committee on Computational Linguistics.
Xinchen Yan, Jimei Yang, Kihyuk Sohn, and Honglak Lee. 2016. Attribute2image: Conditional image generation from visual attributes. 9908:776-791.
Tiancheng Zhao, Kyusong Lee, and Maxine Eskenazi. 2018. Unsupervised discrete sentence representation learning for interpretable neural dialog generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers, pages 1098-1107. Association for Computational Linguistics.
Tiancheng Zhao, Ran Zhao, and Maxine Eskenazi. 2017. Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1:
Long Papers, pages 654-664. Association for Computational Linguistics.
Hao Zhou, Minlie Huang, Tianyang Zhang, Xiaoyan Zhu, and Bing Liu. 2018. Emotional chatting machine: Emotional conversation generation with internal and external memory. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, pages 730-739. AAAI Press.
A Empathetic Expression Gap
For more comprehensive recognition of the severe emathy expression bias between existing empathetic dialogue models and humans, we further quantify the bias of Multitask-Transformer (Rashkin et al., 2019) in Figure 8 and MoEL (Lin et al., 2019) in Figure 9, the intent index is consistent with Figure 2. The results are similar with MIME (Majumder et al., 2020), we can see the large intent distribution bias and the monotony of empathetic expression of existing models.
Figure 8: Empathetic intent distribution of human and Multitask-Transformer (Rashkin et al., 2019)
Figure 9: Empathetic intent distribution of human and MoEL (Lin et al., 2019)
B Intent Keywords Collection
The keywords are retrieved from the training set of Empathetic Intents dataset (Welivita and Pu, 2020) by using TF-IDF method. Empathetic Intents has a training set of 5490 responses, where each intent group has 610 responses. Based on the
labeled intent for each response in the training set, we concatenate all the responses which are in the same group and remove all the stop words. Finally, we apply TF-IDF to obtain the top  $k$  keywords for each intent group, we set  $k$  to 30 in our experiments. See Table 5 for top ten keywords for each intent.
Although EmpHi achieves huge improvement in terms of empathy, relevance, and diversity in empathetic dialogue generation, there is still some flaws. At first, the generation task of EmpHi is far difficult than existing models, because it needs to generate response condition on both context and the predicted intent, while other models generate response only condition on the context, therefor the exposure bias of EmpHi is more severe. See in Table 6, although the predicted intent of EmpHi is the same as reference and its corresponding response is great, EmpHi also gives high probability for Questioning intent and the corresponding response is not very contextually relevant, EmpHi knows it is suitable for asking more details to show its caring, but it does not know how to ask under this context, thus EmpHi needs better understanding for context information. We believe this issue could be mitigated when using more dialogue data for pretraining.
Intent	Keywords
Agreeing	know, understand, agree, definitely, feel, feeling, exactly, mean, oh, right
Acknowledging	sounds, nice, awesome, like, great, cool, would, oh, must, really
Encouraging	hope, well, hopefully, get, good, time, bet, great, goes, soon
Consoling	hope, hopefully, get, better, well, soon, time, find, good, things
Sympathizing	sorry, hear, oh, am, happened, loss, feel, hope, really, aw
Suggesting	maybe, get, time, could, think, well, next, something, try, go
Questioning	oh, get, go, going, long, kind, like, work, good, maybe
Wishing	congratulations, luck, good, wish, best, well, happy, oh, birthday, wow
Neutral	good, like, sure, well, time, one, have, people, never, get
Table 5: Keywords for each intent.
Emotion	Proud
Context	My wife was able to get a job with microsoft without going to a four-year college!
Reference	That’s pretty amazing, she must be skillful regardless.
Intent	Prob	Generated Response
Agreeing	2.91%	Oh, wow! That’s amazing! I know how you feel.
Acknowledging	40.9%	That’s awesome! I’m glad you’re going to be proud of her.
Encouraging	9.98%	That’s awesome! I hope you guys have a great job!
Consoling	3.66%	Wow, That’s good, i hope you are able to get it .
Sympathizing	1.43%	Oh wow! I am sorry to hear that.
Suggesting	0.62%	That’s good! Maybe she will be fine.
Questioning	28.6%	That’s great! How long have you been working?
Wishing	9.19%	Congratulations! That’s awesome! congratulations!
Neutral	2.75%	That is a good thing to do, I wish i was a lot of work .
Table 6: Error analysis of EmpHi, although most responses are reasonable, there are still some imperfection.