Just Accepted
Mixture-of-languages Routing for Multilingual Dialogues
Abstract
We consider multilingual dialogue systems and ask how the performance of a dialogue system can be improved by using information that is available in other languages than the language in which a conversation is being conducted. We adopt a collaborative chair-experts framework, where each expert agent can be either monolingual or cross-lingual, and a chair agent follows a mixture-of-experts procedure for globally optimizing multilingual task-oriented dialogue systems. We propose a mixture-of-languages routing framework that includes four functional components, i.e., input embeddings of multilingual dialogues, language model, pairwise alignment between the representation of every two languages, and mixture-of-languages. We quantify language characteristics of unity and diversity using a number of similarity metrics, i.e., genetic similarity, and word and sentence similarity based on embeddings. Our main finding is that the performance of multilingual task-oriented dialogue systems can be greatly impacted by three key aspects, i.e., data sufficiency, language characteristics, and model design in a mixture-of-languages routing framework.
References
[1]
Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, et al. 2023. A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv preprint arXiv:2302.04023 (2023).
[2]
Lisa Beinborn and Rochelle Choenni. 2020. Semantic drift in multilingual representations. Computational Linguistics 46, 3 (2020), 571–603.
[3]
Lisa Beinborn, Torsten Zesch, and Iryna Gurevych. 2013. Cognate production using character-based machine translation. In Proceedings of the Sixth International Joint Conference on Natural Language Processing. 883–891.
[4]
Johannes Bjerva, Robert Östling, Maria Han Veiga, Jörg Tiedemann, and Isabelle Augenstein. 2019. What do language representations really represent? Computational Linguistics 45, 2 (2019), 381–389.
[5]
Susanne Burger, Karl Weilhammer, Florian Schiel, and Hans G Tillmann. 2000. Verbmobil data collection and annotation. In Verbmobil: Foundations of Speech-to-speech Translation. Springer, 537–549.
[6]
Hugo C.C. Carneiro, Felipe M.G. França, and Priscila M.V. Lima. 2015. Multilingual part-of-speech tagging with weightless neural networks. Neural Networks 66 (2015), 11–21.
[7]
Dhivya Chandrasekaran and Vijay Mago. 2021. Evolution of semantic similarity—a survey. Comput. Surveys 54, 2 (2021), 1–37.
[8]
Guan-Lin Chao and Ian Lane. 2019. BERT-DST: Scalable end-to-end dialogue state tracking with bidirectional encoder representations from transformer. Proceedings of Interspeech (2019).
[9]
Hongshen Chen, Xiaorui Liu, Dawei Yin, and Jiliang Tang. 2017. A survey on dialogue systems: Recent advances and new frontiers. ACM SIGKDD Explorations Newsletter 19, 2 (2017), 25–35.
[10]
Wenhu Chen, Jianshu Chen, Yu Su, Xin Wang, Dong Yu, Xifeng Yan, and William Yang Wang. 2018. XL-NBT: A cross-lingual neural belief tracking framework. In EMNLP. 414–424.
[11]
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Édouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. Unsupervised cross-lingual representation learning at scale. In ACL. 8440–8451.
[12]
Alexis Conneau and Guillaume Lample. 2019. Cross-lingual language model pretraining. Advances in Neural Information Processing Systems 32 (2019).
[13]
Alexis Conneau, Guillaume Lample, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou. 2017. Word translation without parallel data. arXiv preprint arXiv:1710.04087 (2017).
[14]
David Crystal. 2008. Two thousand million? English Today 24, 1 (2008), 3–6.
[15]
Richard Csaky and Gabor Recski. 2020. The gutenberg dialogue dataset. arXiv preprint arXiv:2004.12752 (2020).
[16]
Michael Cysouw. 2013. Predicting language-learning difficulty. In Approaches to Measuring Linguistic Differences. De Gruyter.
[17]
Raj Dabre, Aizhan Imankulova, Masahiro Kaneko, and Abhisek Chakrabarty. 2021. Simultaneous multi-pivot neural machine translation. arXiv preprint arXiv:2104.07410 (2021).
[18]
Ewa Dabrowska. 2015. What exactly is universal grammar, and has anyone seen it? Frontiers in Psychology 6 (2015), 852.
[19]
Michael Daniel. 2011. Linguistic typology and the study of language. In The Oxford Handbook of Linguistic Typology. Oxford University Press.
[20]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT. 4171–4186.
[21]
Bosheng Ding, Junjie Hu, Lidong Bing, Mahani Aljunied, Shafiq Joty, Luo Si, and Chunyan Miao. 2022. GlobalWoZ: Globalizing multiwoz to develop multilingual task-oriented dialogue systems. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1639–1657.
[22]
Robert M.W. Dixon. 2010. I am a linguist: with a foreword by Peter Matthews. Brill.
[23]
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML. PMLR, 1126–1135.
[24]
W. Tecumseh Fitch. 2011. Unity and diversity in human language. Philosophical Transactions of the Royal Society B: Biological Sciences 366, 1563 (2011), 376–388.
[25]
Alexandre François. 2015. Trees, waves and linkages: Models of language diversification. In The Routledge Handbook of Historical Linguistics. Routledge, 161–189.
[26]
Pascale Fung and Tanja Schultz. 2008. Multilingual spoken language processing. IEEE Signal Processing Magazine 25, 3 (2008), 89–97.
[27]
Milan Gritta and Ignacio Iacobacci. 2021. XeroAlign: Zero-shot cross-lingual transformer alignment. In ACL Findings. 371–381.
[28]
Muhammad Usman Hadi, Rizwan Qureshi, Abbas Shah, Muhammad Irfan, Anas Zafar, Muhammad Bilal Shaikh, Naveed Akhtar, Jia Wu, Seyedali Mirjalili, et al. 2023. A survey on large language models: Applications, challenges, limitations, and practical usage. Authorea Preprints (2023).
[29]
Martin Haspelmath. 2004. How hopeless is genealogical linguistics, and how advanced is areal linguistics? Studies in Language 28, 1 (2004), 209–223.
[30]
Claudia Hauff, Julia Kiseleva, Mark Sanderson, Hamed Zamani, and Yongfeng Zhang. 2021. Conversational search and recommendation: Introduction to the special issue. ACM Transactions on Information Systems 39, 4 (2021), 1–6.
[31]
Hiyouga. 2023. LLaMA Factory. https://github.com/hiyouga/LLaMA-Factory.
[32]
Ehsan Hosseini-Asl, Bryan McCann, Chien-Sheng Wu, Semih Yavuz, and Richard Socher. 2020. A simple language model for task-oriented dialogue. Advances in Neural Information Processing Systems 33 (2020), 20179–20191.
[33]
Eduard Hovy, Nancy Ide, Robert Frederking, Joseph Mariani, and Antonio Zampolli. 2001. Multilingual Information Management: Current Levels and Future Abilities. Istituti Editoriali e Poligrafici Internazionali, Pisa.
[34]
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. LoRA: Low-Rank adaptation of large language models. In ICLR. OpenReview.net. https://openreview.net/forum?id=nZeVKeeFYf9
[35]
Minlie Huang, Xiaoyan Zhu, and Jianfeng Gao. 2020. Challenges in building intelligent open-domain dialog systems. ACM Transactions on Information Systems 38, 3 (2020), 1–32.
[36]
Chia-Chien Hung, Anne Lauscher, Ivan Vulić, Simone Paolo Ponzetto, and Goran Glavaš. 2022. Multi2WOZ: A robust multilingual dataset and conversational pretraining for task-oriented dialog. arXiv preprint arXiv:2205.10400 (2022).
[37]
Pratik Jayarao and Aman Srivastava. 2018. Intent detection for code-mix utterances in task oriented dialogue systems. In ICEECCOT. IEEE, 583–587.
[38]
Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al. 2024. Mixtral of experts. arXiv preprint arXiv:2401.04088 (2024).
[39]
Armand Joulin, Piotr Bojanowski, Tomáš Mikolov, Hervé Jégou, and Édouard Grave. 2018. Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion. In EMNLP. 2979–2984.
[40]
Prabhu Kaliamoorthi, Aditya Siddhant, Edward Li, and Melvin Johnson. 2021. Distilling Large Language Models into Tiny and Effective Students using pQRNN. arXiv preprint arXiv:2101.08890 (2021).
[41]
Seokhwan Kim, Luis Fernando D’Haro, Rafael E Banchs, Jason D Williams, Matthew Henderson, and Koichiro Yoshino. 2016. The fifth dialog state tracking challenge. In SLT Workshop. IEEE, 511–517.
[42]
Dan Kondratyuk. 2019. Cross-lingual lemmatization and morphology tagging with two-stage multilingual BERT fine-tuning. In Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology. 12–18.
[43]
Jitin Krishnan, Antonios Anastasopoulos, Hemant Purohit, and Huzefa Rangwala. 2021. Multilingual code-switching for zero-shot cross-lingual intent prediction and slot filling. In MRL Workshop. 211–223.
[44]
Adarsh Kumar, Peter Ku, Anuj Goyal, Angeliki Metallinou, and Dilek Hakkani-Tur. 2020. Ma-dst: Multi-attention-based scalable dialog state tracking. In AAAI, Vol. 34. 8107–8114.
[45]
Tuan Manh Lai, Quan Hung Tran, Trung Bui, and Daisuke Kihara. 2020. A simple but effective bert model for dialog state tracking on resource-limited systems. In ICASSP. IEEE, 8034–8038.
[46]
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilic, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022. BLOOM: A 176b-parameter open-access multilingual language model. arXiv preprint arXiv.2211.05100 (2022).
[47]
Hwaran Lee, Jinsik Lee, and Tae-Yoon Kim. 2019. SUMBT: Slot-utterance matching for universal and scalable belief tracking. In ACL. 5478–5483.
[48]
Haoran Li, Abhinav Arora, Shuohui Chen, Anchit Gupta, Sonal Gupta, and Yashar Mehdad. 2021. MTOP: A comprehensive multilingual task-oriented semantic parsing benchmark. In EACL. 2950–2962.
[49]
Juntao Li, Chang Liu, Chongyang Tao, Zhangming Chan, Dongyan Zhao, Min Zhang, and Rui Yan. 2021. Dialogue history matters! Personalized response selection in multi-turn retrieval-based chatbots. ACM Transactions on Information Systems 39, 4 (2021), 1–25.
[50]
Tomasz Limisiewicz and David Mareček. 2020. Syntax Representation in Word Embeddings and Neural Networks–A Survey. arXiv preprint arXiv:2010.01063 (2020).
[51]
Zhaojiang Lin, Bing Liu, Andrea Madotto, Seungwhan Moon, Zhenpeng Zhou, Paul A Crook, Zhiguang Wang, Zhou Yu, Eunjoon Cho, Rajen Subba, et al. 2021. Zero-shot dialogue state tracking via cross-task transfer. In EMNLP. 7890–7900.
[52]
Zhaojiang Lin, Zihan Liu, Genta Indra Winata, Samuel Cahyawijaya, Andrea Madotto, Yejin Bang, Etsuko Ishii, and Pascale Fung. 2021. XPersona: Evaluating multilingual personalized chatbot. In Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI. 102–112.
[53]
Zhaojiang Lin, Andrea Madotto, Genta Indra Winata, Peng Xu, Feijun Jiang, Yuxiang Hu, Chen Shi, and Pascale Fung. 2021. Bitod: A bilingual multi-domain dataset for task-oriented dialogue modeling. arXiv preprint arXiv:2106.02787 (2021).
[54]
Yanxiang Ling, Fei Cai, Jun Liu, Honghui Chen, and Maarten de Rijke. 2023. Generating relevant and informative questions for open-domain conversations. ACM Transactions on Information Systems 41, 1 (2023), Article 2.
[55]
Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, and Luke Zettlemoyer. 2020. Multilingual denoising pre-training for neural machine translation. Transactions of the Association for Computational Linguistics 8 (2020), 726–742.
[56]
Zihan Liu, Jamin Shin, Yan Xu, Genta Indra Winata, Peng Xu, Andrea Madotto, and Pascale Fung. 2019. Zero-shot Cross-lingual dialogue systems with transferable latent variables. In EMNLP-IJCNLP. 1297–1303.
[57]
Zihan Liu, Genta I Winata, Samuel Cahyawijaya, Andrea Madotto, Zhaojiang Lin, and Pascale Fung. 2021. On the importance of word order information in cross-lingual sequence labeling. In AAAI, Vol. 35. 13461–13469.
[58]
Zihan Liu, Genta Indra Winata, Zhaojiang Lin, Peng Xu, and Pascale Fung. 2020. Attention-informed mixed-language training for zero-shot cross-lingual task-oriented dialogue systems. In AAAI, Vol. 34. 8433–8440.
[59]
Ilya Loshchilov and Frank Hutter. 2018. Decoupled weight decay regularization. In ICLR.
[60]
Samuel Louvan and Bernardo Magnini. 2020. Simple is better! Lightweight data augmentation for low resource slot filling and intent classification. In PACLIC. 167–177.
[61]
Jianjun Ma, Jiahuan Pei, and Degen Huang. 2016. Identification of English functional noun phrases using CRFs combining the semantic information. Journal of Chinese Information Processing 30, 6 (2016), 59–66.
[62]
Jianjun Ma, Jiahuan Pei, Degen Huang, and Dingxin Song. 2018. Syntactic parsing of clause constituents for statistical machine translation. International Journal of Computational Science and Engineering 17, 1 (2018), 126–132.
[63]
Longxuan Ma, Mingda Li, Wei-Nan Zhang, Jiapeng Li, and Ting Liu. 2021. Unstructured Text Enhanced Open-Domain Dialogue System: A Systematic Survey. ACM Transactions on Information Systems 40, 1 (2021), 1–44.
[64]
Brian MacWhinney. 2005. A unified model of language acquisition. In Handbook of Bilingualism: Psycholinguistic Approaches, Judith F. Kroll and Annette M.B. de Groot (Eds.). Vol. 4967. Oxford University Press, 50–70.
[65]
Andrea Madotto, Zhaojiang Lin, Genta Indra Winata, and Pascale Fung. 2021. Few-shot bot: Prompt-based learning for dialogue systems. arXiv preprint arXiv:2110.08118 (2021).
[66]
Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher. 2017. Learned in translation: Contextualized word vectors. Advances in Neural Information Processing Systems 30 (2017).
[67]
Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger. 2018. UMAP: Uniform manifold approximation and projection. Journal of Open Source Software 3, 29 (2018), 861.
[68]
David P Medeiros. 2018. ULTRA: Universal grammar as a universal parser. Frontiers in Psychology 9 (2018), 155.
[69]
Nikola Mrkšić, Diarmuid O Séaghdha, Tsung-Hsien Wen, Blaise Thomson, and Steve Young. 2017. Neural belief tracker: Data-driven dialogue state tracking. In ACL. 1777–1788.
[70]
Nikola Mrkšić and Ivan Vulić. 2018. Fully statistical neural belief tracking. In ACL. 108–113.
[71]
Nikola Mrkšić, Ivan Vulić, Diarmuid Ó Séaghdha, Ira Leviant, Roi Reichart, Milica Gašić, Anna Korhonen, and Steve Young. 2017. Semantic specialization of distributional word vector spaces using monolingual and cross-Lingual constraints. Transactions of the Association for Computational Linguistics 5 (2017), 309–324.
[72]
André Müller, Søren Wichmann, Viveka Velupillai, Cecil H. Brown, Pamela Brown, Sebastian Sauppe, Eric W. Holman, Dik Bakker, Johann-Mattis List, Dmitri Egorov, Oleg Belyaev, Robert Mailhammer, Matthias Urban, Helen Geyer, and Anthony Grant. 2010. ASJP world language tree of lexical similarity: Version 3 (July 2010). https://asjp.clld.org/static/WorldLanguageTree-003.pdf.
[73]
Tahira Naseem, Benjamin Snyder, Jacob Eisenstein, and Regina Barzilay. 2009. Multilingual part-of-speech tagging: Two unsupervised approaches. Journal of Artificial Intelligence Research 36 (2009), 341–385.
[74]
Jinjie Ni, Tom Young, Vlad Pandelea, Fuzhao Xue, Vinay Adiga, and Erik Cambria. 2021. Recent advances in deep learning based dialogue systems: A systematic survey. arXiv preprint arXiv:2105.04387 (2021).
[75]
Joakim Nivre. 2015. Towards a universal grammar for natural language processing. In CICLing. Springer, 3–16.
[76]
Elnaz Nouri and Ehsan Hosseini-Asl. 2018. Toward scalable neural dialogue state tracking model. arXiv preprint arXiv:1812.00899 (2018).
[77]
Javad Nouri and Roman Yangarber. 2016. From alignment of etymological data to phylogenetic inference via population genetics. In CogACLL Workshop. 27–37.
[78]
Nathaniel Oco, Leif Romeritch Syliongka, Rachel Edita Roxas, and Joel Ilao. 2013. Dice’s coefficient on trigram profiles as metric for language similarity. In O-COCOSDA/CASLRE. IEEE, 1–4.
[79]
Helen O’Horan, Yevgeni Berzak, Ivan Vulić, Roi Reichart, and Anna Korhonen. 2016. Survey on the use of typological information in natural language processing. In COLING Technical Papers. 1297–1308.
[80]
Subhadarshi Panda, Caglar Tirkaz, Tobias Falke, and Patrick Lehnen. 2021. Multilingual paraphrase generation for bootstrapping new features in task-oriented dialog systems. In Workshop on NLP for Conversational AI. 30–39.
[81]
Hyunji Hayley Park, Katherine J Zhang, Coleman Haley, Kenneth Steimel, Han Liu, and Lane Schwartz. 2021. Morphology matters: A multilingual language modeling analysis. Transactions of the Association for Computational Linguistics 9 (2021), 261–276.
[82]
Nicholas D. Pattengale, Eric J. Gottlieb, and Bernard M.E. Moret. 2007. Efficiently computing the Robinson-Foulds metric. Journal of Computational Biology 14, 6 (2007), 724–735.
[83]
Michael Paul, Andrew Finch, and Eiichrio Sumita. 2013. How to choose the best pivot language for automatic translation of low-resource languages. ACM Transactions on Asian Language Information Processing (TALIP) 12, 4 (2013), 1–17.
[84]
Jiahuan Pei, Pengjie Ren, and Maarten de Rijke. 2019. A modular task-oriented dialogue system using a neural mixture-of-experts. In SIGIR Workshop on Conversational Interaction Systems.
[85]
Jiahuan Pei, Pengjie Ren, and Maarten de Rijke. 2021. A cooperative memory network for personalized task-oriented dialogue systems with incomplete user profiles. In The Web Conference. 1552–1561.
[86]
Jiahuan Pei, Pengjie Ren, Christof Monz, and Maarten de Rijke. 2020. Retrospective and prospective mixture-of-generators for task-oriented dialogue response generation. In ECAI. 2148–2155.
[87]
Carol Peters, Martin Braschler, and Paul Clough. 2012. Multilingual Information Retrieval. Springer-Verlag, Berlin, Heidelberg.
[88]
Libo Qin, Minheng Ni, Yue Zhang, and Wanxiang Che. 2021. CoSDA-ML: multi-lingual code-switching data augmentation for zero-shot cross-lingual NLP. In IJCAI. 3853–3860.
[89]
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI blog 1, 8 (2019), 9.
[90]
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research 21 (2020), 1–67.
[91]
Evgeniia Razumovskaia, Goran Glavaš, Olga Majewska, Anna Korhonen, and Ivan Vulic. 2021. Crossing the conversational chasm: A primer on multilingual task-oriented dialogue systems. arXiv preprint arXiv:2104.08570 (2021).
[92]
Evgeniia Razumovskaia, Goran Glavaš, Olga Majewska, Edoardo Ponti, and Ivan Vulić. 2022. Natural language processing for multilingual task-oriented dialogue. In ACL Tutorial Abstracts. 44–50.
[93]
Evgeniia Razumovskaia, Ivan Vulić, and Anna Korhonen. 2022. Data augmentation and learned layer aggregation for improved multilingual language understanding in dialogue. In ACL Findings. 2017–2033.
[94]
Liliang Ren, Kaige Xie, Lu Chen, and Kai Yu. 2018. Towards universal dialogue state tracking. In EMNLP. 2780–2786.
[95]
Pengjie Ren, Zhumin Chen, Zhaochun Ren, Evangelos Kanoulas, Christof Monz, and Maarten de Rijke. 2021. Conversations with search engines: SERP-based conversational response generation. ACM Transactions on Information Systems 39, 4 (2021), 1–29.
[96]
Sebastian Ruder, Ivan Vulić, and Anders Søgaard. 2019. A survey of cross-lingual word embedding models. Journal of Artificial Intelligence Research 65 (2019), 569–631.
[97]
Sergio Scalise, Elisabetta Magni, and Antonietta Bisetto. 2009. Universals of Language Today. Springer.
[98]
Sebastian Schuster, Sonal Gupta, Rushin Shah, and Mike Lewis. 2019. Cross-lingual transfer learning for multilingual task oriented dialog. In NAACL-HLT. 3795–3805.
[99]
Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, and Joelle Pineau. 2018. A survey of available corpora for building data-driven dialogue systems. Dialogue & Discourse 9, 1 (2018), 1–49.
[100]
Maurizio Serva and Filippo Petroni. 2008. Indo-European languages tree by Levenshtein distance. EPL (Europhysics Letters) 81, 6 (2008), 68005.
[101]
Aditya Siddhant, Melvin Johnson, Henry Tsai, Naveen Ari, Jason Riesa, Ankur Bapna, Orhan Firat, and Karthik Raman. 2020. Evaluating the cross-lingual effectiveness of massively multilingual neural machine translation. In AAAI, Vol. 34. 8854–8861.
[102]
Anders Søgaard, Ivan Vulić, Sebastian Ruder, and Manaal Faruqui. 2019. Cross-lingual word embeddings. Synthesis Lectures on Human Language Technologies 12, 2 (2019), 1–132.
[103]
Georgios P Spithourakis, Ivan Vulić, Michał Lis, Iñigo Casanueva, and Paweł Budzianowski. 2022. Evi: Multilingual spoken dialogue tasks and dataset for knowledge-based enrolment, verification, and identification. arXiv preprint arXiv:2204.13496 (2022).
[104]
Chaoju Tang and Vincent J. van Heuven. 2007. Mutual intelligibility and similarity of Chinese dialects: Predicting judgments from objective measures. Linguistics in the Netherlands 24, 1 (2007), 223–234.
[105]
Sandra A. Thompson, Robert E. Longacre, Shin Ja J. Hwang, and Timothy Shopen. 2007. Language typology and syntactic description. Cambridge University Press.
[106]
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023).
[107]
Shyam Upadhyay, Manaal Faruqui, Gokhan Tür, Hakkani-Tür Dilek, and Larry Heck. 2018. (Almost) zero-shot cross-lingual spoken language understanding. In ICASSP. IEEE, 6034–6038.
[108]
Phi Nguyen Van, Tung Cao Hoang, Dung Nguyen Manh, Quan Nguyen Minh, and Long Tran Quoc. 2022. ViWOZ: A multi-domain task-oriented dialogue systems dataset for low-resource language. arXiv preprint arXiv:2203.07742 (2022).
[109]
Piet van Sterkenburg (Ed.). 2008. Unity and Diversity of Languages. John Benjamins Publishing.
[110]
Ivan Vulić, Nikola Mrkšić, Roi Reichart, Diarmuid Ó Séaghdha, Steve Young, and Anna Korhonen. 2017. Morph-fitting: Fine-tuning word vector spaces with simple language-specific rules. In ACL. 56–68.
[111]
Guan Wang, Sijie Cheng, Xianyuan Zhan, Xiangang Li, Sen Song, and Yang Liu. 2023. Openchat: Advancing open-source language models with mixed-quality data. arXiv preprint arXiv:2309.11235 (2023).
[112]
Jiapeng Wang and Yihong Dong. 2020. Measurement of text similarity: a survey. Information 11, 9 (2020), 421.
[113]
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 EACL. 438–449.
[114]
Lindsay J. Whaley. 1996. Introduction to Typology: The Unity and Diversity of Language. SAGE publications.
[115]
Chien-Sheng Wu, Andrea Madotto, Ehsan Hosseini-Asl, Caiming Xiong, Richard Socher, and Pascale Fung. 2019. Transferable multi-domain state generator for task-oriented dialogue systems. In ACL. 808–819.
[116]
Lu Xiang, Junnan Zhu, Yang Zhao, Yu Zhou, and Chengqing Zong. 2021. Robust cross-lingual task-oriented dialogue. Transactions on Asian and Low-Resource Language Information Processing 20, 6 (2021), 1–24.
[117]
Puyang Xu and Qi Hu. 2018. An end-to-end approach for handling unknown slot values in dialogue state tracking. In ACL. 1448–1457.
[118]
Ruijian Xu, Chongyang Tao, Jiazhan Feng, Wei Wu, Rui Yan, and Dongyan Zhao. 2021. Response ranking with multi-types of deep interactive representations in retrieval-based dialogues. ACM Transactions on Information Systems 39, 4 (2021), 1–28.
[119]
Weijia Xu, Batool Haider, and Saab Mansour. 2020. End-to-end slot alignment and recognition for cross-lingual NLU. In EMNLP. 5052–5063.
[120]
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. mT5: A massively multilingual pre-trained text-to-text transformer. In NAACL-HLT.
[121]
Guojun Yan, Jiahuan Pei, Pengjie Ren, Zhaochun Ren, Xin Xin, Huasheng Liang, Maarten de Rijke, and Zhumin Chen. 2022. ReMeDi: Resources for multi-domain, multi-service, medical dialogues. In SIGIR. 3013–3024.
[122]
Rui Yan, Weiheng Liao, Dongyan Zhao, and Ji-Rong Wen. 2021. Multi-response awareness for retrieval-based conversations: Respond with diversity via dynamic representation learning. ACM Transactions on Information Systems 39, 4 (2021), 1–29.
[123]
Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, and Qun Liu. 2020. Dialog state tracking with reinforced data augmentation. In AAAI, Vol. 34. 9474–9481.
[124]
Zheng Zhang, Ryuichi Takanobu, Qi Zhu, MinLie Huang, and XiaoYan Zhu. 2020. Recent advances and challenges in task-oriented dialog systems. Science China Technological Sciences 63, 10 (2020), 2011–2027.
[125]
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023. A survey of large language models. arXiv preprint arXiv:2303.18223 (2023).
[126]
Zijian Zhao, Su Zhu, and Kai Yu. 2019. Data augmentation with atomic templates for spoken language understanding. In EMNLP-IJCNLP. 3637–3643.
[127]
Victor Zhong, Caiming Xiong, and Richard Socher. 2018. Global-locally self-attentive encoder for dialogue state tracking. In ACL. 1458–1467.
[128]
Han Zhou, Ignacio Iacobacci, and Pasquale Minervini. 2022. XQA-DST: Multi-domain and multi-lingual dialogue state tracking. arXiv preprint arXiv:2204.05895 (2022).
[129]
Lei Zuo, Kun Qian, Bowen Yang, and Zhou Yu. 2021. AllWOZ: Towards multilingual task-oriented dialog systems for all. arXiv preprint arXiv:2112.08333 (2021).
Index Terms
- Mixture-of-languages Routing for Multilingual Dialogues
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Association for Computing Machinery
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Publication History
Online AM: 05 August 2024
Accepted: 24 June 2024
Revised: 04 March 2024
Received: 10 March 2023
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