Crossing the Conversational Chasm: A Primer on Multilingual Task-Oriented Dialogue Systems

Despite the fact that natural language conversations with machines represent one of the central objectives of AI, and despite the massive increase of research and development efforts in conversational AI, task-oriented dialogue (TOD) – i.e., conversations with an artificial agent with the aim of completing a concrete task – is currently limited to a few narrow domains (e.g., food ordering, ticket booking) and a handful of major languages (e.g., English, Chinese). In this work, we provide an extensive overview of existing efforts in multilingual TOD and analyse the factors preventing the development of truly multilingual TOD systems. We identify two main challenges that combined hinder the faster progress in multilingual TOD: (1) current state-of-the-art TOD models based on large pretrained neural language models are data hungry; at the same time (2) data acquisition for TOD use cases is expensive and tedious. Most existing approaches to multilingual TOD thus rely on (zeroor few-shot) cross-lingual transfer from resource-rich languages (in TOD, this is basically only English), either by means of (i) machine translation or (ii) multilingual representation spaces. However, such approaches are currently not a viable solution for a large number of low-resource languages without parallel data and/or limited monolingual corpora. Finally, we discuss critical challenges and potential solutions by drawing parallels between TOD and other cross-lingual and multilingual NLP research.

[1]  Gertjan van Noord,et al.  UDapter: Language Adaptation for Truly Universal Dependency Parsing , 2020, EMNLP.

[2]  Orhan Firat,et al.  Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages , 2020, NAACL.

[3]  Alan W Black,et al.  What Code-Switching Strategies are Effective in Dialog Systems? , 2020, SCIL.

[4]  Ehud Reiter,et al.  Book Reviews: Building Natural Language Generation Systems , 2000, CL.

[5]  Vladimir Vlasov,et al.  DIET: Lightweight Language Understanding for Dialogue Systems , 2020, ArXiv.

[6]  Gökhan Tür,et al.  Language Model is all You Need: Natural Language Understanding as Question Answering , 2020, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  John A. Bateman,et al.  Enabling technology for multilingual natural language generation: the KPML development environment , 1997, Natural Language Engineering.

[8]  Samuel R. Bowman,et al.  A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference , 2017, NAACL.

[9]  Yoshua Bengio,et al.  Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus , 2016, ACL.

[10]  Wei Lu,et al.  Neural Architectures for Multilingual Semantic Parsing , 2017, ACL.

[11]  Wanxiang Che,et al.  Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog , 2020, ACL.

[12]  Matthew Henderson,et al.  Training Neural Response Selection for Task-Oriented Dialogue Systems , 2019, ACL.

[13]  Dilek Z. Hakkani-Tür,et al.  Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems , 2018, NAACL.

[14]  Raffaella Bernardi,et al.  Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat , 2018, NAACL.

[15]  Geoffrey E. Hinton,et al.  Distilling the Knowledge in a Neural Network , 2015, ArXiv.

[16]  David Sankoff,et al.  A Formal Grammar for Code-Switching. CENTRO Working Papers 8. , 1980 .

[17]  Anna Korhonen,et al.  Cross-lingual Semantic Specialization via Lexical Relation Induction , 2019, EMNLP.

[18]  Xin Wang,et al.  XL-NBT: A Cross-lingual Neural Belief Tracking Framework , 2018, EMNLP.

[19]  Joelle Pineau,et al.  Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models , 2015, AAAI.

[20]  Gyuwan Kim,et al.  Efficient Dialogue State Tracking by Selectively Overwriting Memory , 2020, ACL.

[21]  Doug Downey,et al.  Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks , 2020, ACL.

[22]  Colin Raffel,et al.  mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer , 2021, NAACL.

[23]  Gökhan Tür,et al.  The AT&T spoken language understanding system , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[24]  Zhoujun Li,et al.  Sequential Match Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots , 2016, ArXiv.

[25]  Giuseppe Castellucci,et al.  Almawave-SLU: A New Dataset for SLU in Italian , 2019, CLiC-it.

[26]  John Miller,et al.  Globally Normalized Reader , 2017, EMNLP.

[27]  Haoran Li,et al.  MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark , 2020, EACL.

[28]  Antoine Raux,et al.  The Dialog State Tracking Challenge , 2013, SIGDIAL Conference.

[29]  Yan Cao,et al.  Adaptive Dialog Policy Learning with Hindsight and User Modeling , 2020, SIGDIAL.

[30]  Anna Korhonen,et al.  Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints , 2017, TACL.

[31]  Li Dong,et al.  Cross-Lingual Natural Language Generation via Pre-Training , 2020, AAAI.

[32]  Bowen Zhou,et al.  Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling , 2016, EMNLP.

[33]  Young-Bum Kim,et al.  Efficient Large-Scale Domain Classification with Personalized Attention , 2018, ArXiv.

[34]  Emiel Krahmer,et al.  Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation , 2017, J. Artif. Intell. Res..

[35]  Matej Klemen,et al.  Enhancing deep neural networks with morphological information , 2020, ArXiv.

[36]  Veselin Stoyanov,et al.  Unsupervised Cross-lingual Representation Learning at Scale , 2019, ACL.

[37]  Frank Keller,et al.  Image Pivoting for Learning Multilingual Multimodal Representations , 2017, EMNLP.

[38]  Maxine Eskénazi,et al.  Let's go public! taking a spoken dialog system to the real world , 2005, INTERSPEECH.

[39]  Jie Zhou,et al.  A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking , 2020, ACL.

[40]  Anna Korhonen,et al.  On the Relation between Linguistic Typology and (Limitations of) Multilingual Language Modeling , 2018, EMNLP.

[41]  Min Zhang,et al.  Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention , 2019, ACL.

[42]  Heriberto Cuayáhuitl,et al.  SimpleDS: A Simple Deep Reinforcement Learning Dialogue System , 2016, IWSDS.

[43]  Richard Socher,et al.  TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue , 2020, EMNLP.

[44]  Steve Renals,et al.  Multilingual training of deep neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[45]  Anders Søgaard,et al.  A Survey of Cross-lingual Word Embedding Models , 2017, J. Artif. Intell. Res..

[46]  Chenguang Zhu,et al.  Multi-task Learning for Natural Language Generation in Task-Oriented Dialogue , 2019, EMNLP.

[47]  Ondvrej Duvsek,et al.  Neural Generation for Czech: Data and Baselines , 2019, 1910.05298.

[48]  Jianfeng Gao,et al.  Microsoft Dialogue Challenge: Building End-to-End Task-Completion Dialogue Systems , 2018, ArXiv.

[49]  Jianfeng Gao,et al.  A Neural Network Approach to Context-Sensitive Generation of Conversational Responses , 2015, NAACL.

[50]  Iñigo Casanueva,et al.  Deep Learning for Conversational AI , 2018, NAACL.

[51]  Mark Steedman,et al.  Data Augmentation via Dependency Tree Morphing for Low-Resource Languages , 2018, EMNLP.

[52]  Matthew Henderson,et al.  ConVEx: Data-Efficient and Few-Shot Slot Labeling , 2021, NAACL.

[53]  Gabriel Synnaeve,et al.  Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters , 2020, INTERSPEECH.

[54]  Hai Zhao,et al.  Modeling Multi-turn Conversation with Deep Utterance Aggregation , 2018, COLING.

[55]  Joelle Pineau,et al.  Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses , 2017, ACL.

[56]  Zheng Zhang,et al.  CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset , 2020, Transactions of the Association for Computational Linguistics.

[57]  Hermann Ney,et al.  When and Why is Unsupervised Neural Machine Translation Useless? , 2020, EAMT.

[58]  Marcel Bollmann.  Adapting SimpleNLG to German , 2011, ENLG.

[59]  Jian Zhang,et al.  SQuAD: 100,000+ Questions for Machine Comprehension of Text , 2016, EMNLP.

[60]  Siddhant Garg,et al.  BAE: BERT-based Adversarial Examples for Text Classification , 2020, EMNLP.

[61]  Jessica A. Chen,et al.  Conversational agents in healthcare: a systematic review , 2018, J. Am. Medical Informatics Assoc..

[62]  Gökhan Tür,et al.  Multi-style adaptive training for robust cross-lingual spoken language understanding , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[63]  Mitesh M. Khapra,et al.  Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization , 2018, NAACL.

[64]  Barbara Plank,et al.  Neural Unsupervised Domain Adaptation in NLP—A Survey , 2020, COLING.

[65]  Fei Liu,et al.  Dialog state tracking, a machine reading approach using Memory Network , 2016, EACL.

[66]  Quoc V. Le,et al.  Unsupervised Data Augmentation for Consistency Training , 2019, NeurIPS.

[67]  Goran Glavaš,et al.  Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces , 2020, ACL.

[68]  Eunsol Choi,et al.  Neural Metaphor Detection in Context , 2018, EMNLP.

[69]  Lingjia Tang,et al.  An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction , 2019, EMNLP.

[70]  Ganesh Ramakrishnan,et al.  Cross-Lingual Training for Automatic Question Generation , 2019, ACL.

[71]  Ankur Bapna,et al.  Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation , 2020, AAAI.

[72]  Patrick Littell,et al.  URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors , 2017, EACL.

[73]  Sebastian Schuster,et al.  Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog , 2018, NAACL.

[74]  Xiaocheng Feng,et al.  Knowledge-Aware Conversational Semantic Parsing Over Web Tables , 2018, NLPCC.

[75]  Niels van der Heijden,et al.  Multilingual and cross-lingual document classification: A meta-learning approach , 2021 .

[76]  Leonhard Hennig,et al.  Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction , 2019, ACL.

[77]  Joelle Pineau,et al.  How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation , 2016, EMNLP.

[78]  Matthew Henderson,et al.  Word-Based Dialog State Tracking with Recurrent Neural Networks , 2014, SIGDIAL Conference.

[79]  Eunsol Choi,et al.  TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages , 2020, Transactions of the Association for Computational Linguistics.

[80]  Adam Lopez,et al.  A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages , 2019, EMNLP/IJCNLP.

[81]  Peng Xu,et al.  Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems , 2019, AAAI.

[82]  Alberto Bugarín,et al.  Adapting SimpleNLG to Galician language , 2018, INLG.

[83]  Goran Glavaš,et al.  From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers , 2020, EMNLP.

[84]  Gaurav Pandey,et al.  Exemplar Encoder-Decoder for Neural Conversation Generation , 2018, ACL.

[85]  Michael Elhadad,et al.  An Overview of SURGE: a Reusable Comprehensive Syntactic Realization Component , 1996, INLG.

[86]  Graham Neubig,et al.  Choosing Transfer Languages for Cross-Lingual Learning , 2019, ACL.

[87]  Richard Socher,et al.  Global-Locally Self-Attentive Encoder for Dialogue State Tracking , 2018, ACL.

[88]  Graham Neubig,et al.  XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization , 2020, ICML.

[89]  Akio Yoneyama,et al.  Integrating Language and Emotion Features for Multilingual Speech Emotion Recognition , 2020, HCI.

[90]  Alex Wang,et al.  BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model , 2019, Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation.

[91]  Iryna Gurevych,et al.  How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models , 2021, ACL/IJCNLP.

[92]  Zhichang Zhang,et al.  A Joint Learning Framework With BERT for Spoken Language Understanding , 2019, IEEE Access.

[93]  Pierre Lison,et al.  Named Entity Recognition without Labelled Data: A Weak Supervision Approach , 2020, ACL.

[94]  Qianchu Liu,et al.  XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning , 2020, EMNLP.

[95]  Rodrigo de Oliveira,et al.  Adapting SimpleNLG for Brazilian Portuguese realisation , 2014, INLG.

[96]  Jason Weston,et al.  Personalizing Dialogue Agents: I have a dog, do you have pets too? , 2018, ACL.

[97]  Richard May,et al.  Intelligent Conversational Agents in Healthcare: Hype or Hope? , 2019, Studies in health technology and informatics.

[98]  Dan Jurafsky,et al.  Racial disparities in automated speech recognition , 2020, Proceedings of the National Academy of Sciences.

[99]  Susan McRoy,et al.  An augmented template-based approach to text realization , 2003, Natural Language Engineering.

[100]  M. de Rijke,et al.  DukeNet: A Dual Knowledge Interaction Network for Knowledge-Grounded Conversation , 2020, SIGIR.

[101]  Andrew Zisserman,et al.  Visual Grounding in Video for Unsupervised Word Translation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[102]  Andy Way,et al.  Improving Character-Based Decoding Using Target-Side Morphological Information for Neural Machine Translation , 2018, NAACL.

[103]  Fuji Ren,et al.  Intention Detection Based on Siamese Neural Network With Triplet Loss , 2020, IEEE Access.

[104]  Francisco Javier González-Castaño,et al.  A library for automatic natural language generation of spanish texts , 2019, Expert Syst. Appl..

[105]  Gökhan Tür,et al.  Multi-Domain Joint Semantic Frame Parsing Using Bi-Directional RNN-LSTM , 2016, INTERSPEECH.

[106]  Goran Glavas,et al.  How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions , 2019, ACL.

[107]  Yejin Choi,et al.  Commonsense Reasoning for Natural Language Processing , 2020, ACL.

[108]  David Vandyke,et al.  A Network-based End-to-End Trainable Task-oriented Dialogue System , 2016, EACL.

[109]  Bill Byrne,et al.  Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset , 2019, EMNLP.

[110]  Cristina Bosco,et al.  SimpleNLG-IT: adapting SimpleNLG to Italian , 2016, INLG.

[111]  Jason Weston,et al.  Retrieve and Refine: Improved Sequence Generation Models For Dialogue , 2018, SCAI@EMNLP.

[112]  Mark Dredze,et al.  Are All Languages Created Equal in Multilingual BERT? , 2020, REPL4NLP.

[113]  Jun Huang,et al.  Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems , 2018, SIGIR.

[114]  Jing He,et al.  A Sequence-to-Sequence Model for User Simulation in Spoken Dialogue Systems , 2016, INTERSPEECH.

[115]  Keqing He,et al.  Multi-Level Cross-Lingual Transfer Learning With Language Shared and Specific Knowledge for Spoken Language Understanding , 2020, IEEE Access.

[116]  Pascale Fung,et al.  Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems , 2018, ACL.

[117]  Ronan Collobert,et al.  Unsupervised Cross-lingual Representation Learning for Speech Recognition , 2020, Interspeech.

[118]  Heike Adel,et al.  An Analysis of Simple Data Augmentation for Named Entity Recognition , 2020, COLING.

[119]  Sosuke Kobayashi,et al.  Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations , 2018, NAACL.

[120]  Joelle Pineau,et al.  Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus , 2017, Dialogue Discourse.

[121]  Veselin Stoyanov,et al.  Emerging Cross-lingual Structure in Pretrained Language Models , 2020, ACL.

[122]  Verena Rieser,et al.  History for Visual Dialog: Do we really need it? , 2020, ACL.

[123]  Zhiyuan Liu,et al.  Low-Resource Name Tagging Learned with Weakly Labeled Data , 2019, EMNLP.

[124]  Arantxa Otegi,et al.  Survey on evaluation methods for dialogue systems , 2019, Artificial Intelligence Review.

[125]  Anna Korhonen,et al.  A Closer Look at Few-Shot Crosslingual Transfer: Variance, Benchmarks and Baselines , 2020, ArXiv.

[126]  Geoffrey Zweig,et al.  Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[127]  Myle Ott,et al.  Understanding Back-Translation at Scale , 2018, EMNLP.

[128]  Danqi Chen,et al.  of the Association for Computational Linguistics: , 2001 .

[129]  Thierry Poibeau,et al.  Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing , 2018, Computational Linguistics.

[130]  Wolfgang Minker,et al.  Emotion recognition and adaptation in spoken dialogue systems , 2010, Int. J. Speech Technol..

[131]  Tapio Salakoski,et al.  Is Multilingual BERT Fluent in Language Generation? , 2019, ArXiv.

[132]  Alexander I. Rudnicky,et al.  The RavenClaw dialog management framework: Architecture and systems , 2009, Comput. Speech Lang..

[133]  Lorena Sainz-Maza Lecanda,et al.  Cross-lingual Transfer Learning for Intent Detection of Covid-19 Utterances , 2020 .

[134]  Dan Klein,et al.  Multilingual Alignment of Contextual Word Representations , 2020, ICLR.

[135]  Robert Dale,et al.  Building applied natural language generation systems , 1997, Natural Language Engineering.

[136]  Verena Rieser,et al.  Benchmarking Natural Language Understanding Services for building Conversational Agents , 2019, IWSDS.

[137]  Matthew Henderson,et al.  PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking , 2019, EMNLP.

[138]  Iryna Gurevych,et al.  MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale , 2020, EMNLP.

[139]  Vishrav Chaudhary,et al.  Self-training Improves Pre-training for Natural Language Understanding , 2020, NAACL.

[140]  Andreas Stolcke,et al.  Recurrent neural network and LSTM models for lexical utterance classification , 2015, INTERSPEECH.

[141]  Matthew Henderson,et al.  ConveRT: Efficient and Accurate Conversational Representations from Transformers , 2020, EMNLP.

[142]  Hugo Larochelle,et al.  GuessWhat?! Visual Object Discovery through Multi-modal Dialogue , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[143]  Etsuko Ishii,et al.  XPersona: Evaluating Multilingual Personalized Chatbot , 2020, NLP4CONVAI.

[144]  Dongyan Zhao,et al.  Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix , 2017, ACL.

[145]  Thomas Wolf,et al.  TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents , 2019, ArXiv.

[146]  Kevin Knight,et al.  The Practical Value of N-Grams Is in Generation , 1998, INLG.

[147]  Michael Strube,et al.  Generating Constituent Order in German Clauses , 2007, ACL.

[148]  Sophie Rosset,et al.  Semantic annotation of the French media dialog corpus , 2005, INTERSPEECH.

[149]  Jianfeng Gao,et al.  Few-shot Natural Language Generation for Task-Oriented Dialog , 2020, FINDINGS.

[150]  Chih-Li Huo,et al.  Slot-Gated Modeling for Joint Slot Filling and Intent Prediction , 2018, NAACL.

[151]  Joelle Pineau,et al.  The Second Conversational Intelligence Challenge (ConvAI2) , 2019, The NeurIPS '18 Competition.

[152]  Mihail Eric,et al.  MultiWOZ 2. , 2019 .

[153]  Matthew Henderson,et al.  Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations , 2020, ACL.

[154]  Zachary C. Lipton,et al.  Entity Projection via Machine Translation for Cross-Lingual NER , 2019, EMNLP.

[155]  Tathagata Chakraborti,et al.  Planning for Goal-Oriented Dialogue Systems , 2019, ArXiv.

[156]  Jianfeng Gao,et al.  Deep Reinforcement Learning for Dialogue Generation , 2016, EMNLP.

[157]  Meina Song,et al.  KB-Transformer: Incorporating Knowledge into End-to-End Task-Oriented Dialog Systems , 2019, 2019 15th International Conference on Semantics, Knowledge and Grids (SKG).

[158]  Roi Reichart,et al.  Bridging Languages through Images with Deep Partial Canonical Correlation Analysis , 2018, ACL.

[159]  Tsung-Hsien Wen,et al.  Neural Belief Tracker: Data-Driven Dialogue State Tracking , 2016, ACL.

[160]  Kee-Eung Kim,et al.  End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2 , 2020, ACL.

[161]  Anna Korhonen,et al.  Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP , 2018, ACL.

[162]  Ivan Vulic,et al.  Survey on the Use of Typological Information in Natural Language Processing , 2016, COLING.

[163]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[164]  Stephen Clark,et al.  Visual Bilingual Lexicon Induction with Transferred ConvNet Features , 2015, EMNLP.

[165]  Graham Neubig,et al.  Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces , 2019, ACL.

[166]  Ivan Vulić,et al.  Fully Statistical Neural Belief Tracking , 2018, ACL.

[167]  Goran Glavas,et al.  Explicit Retrofitting of Distributional Word Vectors , 2018, ACL.

[168]  Francesco Caltagirone,et al.  Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces , 2018, ArXiv.

[169]  Pushpak Bhattacharyya,et al.  Shata-Anuvadak: Tackling Multiway Translation of Indian Languages , 2014, LREC.

[170]  Ondrej Dusek,et al.  One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech , 2020, INTERSPEECH.

[171]  Maxine Eskénazi,et al.  Context-Aware Dialog Re-Ranking for Task-Oriented Dialog Systems , 2018, 2018 IEEE Spoken Language Technology Workshop (SLT).

[172]  Quoc V. Le,et al.  Exploiting Similarities among Languages for Machine Translation , 2013, ArXiv.

[173]  Deniz Yuret,et al.  Transfer Learning for Low-Resource Neural Machine Translation , 2016, EMNLP.

[174]  Sergey Levine,et al.  Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.

[175]  Xiaodong Liu,et al.  A Hybrid Retrieval-Generation Neural Conversation Model , 2019, CIKM.

[176]  Wen Wang,et al.  BERT for Joint Intent Classification and Slot Filling , 2019, ArXiv.

[177]  Goran Glavas,et al.  Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization , 2018, EMNLP.

[178]  Eneko Agirre,et al.  A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings , 2018, ACL.

[179]  Yasumasa Onoe,et al.  Learning to Denoise Distantly-Labeled Data for Entity Typing , 2019, NAACL.

[180]  Anna Korhonen,et al.  Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules , 2017, ACL.

[181]  John X. Morris,et al.  TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP , 2020, EMNLP.

[182]  Yaser Al-Onaizan,et al.  To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging , 2020, EMNLP.

[183]  Giuseppe Castellucci,et al.  Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model , 2019, ArXiv.

[184]  Jian Ni,et al.  Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection , 2017, ACL.

[185]  Wanxiang Che,et al.  The First Evaluation of Chinese Human-Computer Dialogue Technology , 2017, ArXiv.

[186]  Dain Kaplan,et al.  Conversational Semantic Parsing for Dialog State Tracking , 2020, EMNLP.

[187]  Steve J. Young,et al.  Still talking to machines (cognitively speaking) , 2010, INTERSPEECH.

[188]  Jindong Chen,et al.  MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines , 2020, NLP4CONVAI.

[189]  Holger Schwenk,et al.  Beyond English-Centric Multilingual Machine Translation , 2020, J. Mach. Learn. Res..

[190]  Hervé Jégou,et al.  Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion , 2018, EMNLP.

[191]  Qian Cao,et al.  RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling , 2020, EMNLP.

[192]  Marilyn A. Walker,et al.  Trainable Sentence Planning for Complex Information Presentations in Spoken Dialog Systems , 2004, ACL.

[193]  Gökhan Tür,et al.  (Almost) Zero-Shot Cross-Lingual Spoken Language Understanding , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[194]  Heike Adel,et al.  A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios , 2020, NAACL.

[195]  Monojit Choudhury,et al.  The State and Fate of Linguistic Diversity and Inclusion in the NLP World , 2020, ACL.

[196]  Bernardo Magnini,et al.  Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey , 2020, COLING.

[197]  Sen Su,et al.  Conversational semantic parsing over tables by decoupling and grouping actions , 2020, Knowl. Based Syst..

[198]  Luis Gravano,et al.  Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher , 2020, FINDINGS.

[199]  Matthew Henderson,et al.  Efficient Intent Detection with Dual Sentence Encoders , 2020, NLP4CONVAI.

[200]  P. J. Price,et al.  Evaluation of Spoken Language Systems: the ATIS Domain , 1990, HLT.

[201]  Pascale Fung,et al.  On the Importance of Word Order Information in Cross-lingual Sequence Labeling , 2020 .

[202]  Milica Gasic,et al.  POMDP-Based Statistical Spoken Dialog Systems: A Review , 2013, Proceedings of the IEEE.

[203]  Goran Glavas,et al.  Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources , 2018, NAACL.

[204]  Piek T. J. M. Vossen,et al.  Cross-linguistic differences and similarities in image descriptions , 2017, INLG.

[205]  Mitesh M. Khapra,et al.  Graph Convolutional Network with Sequential Attention for Goal-Oriented Dialogue Systems , 2019, Transactions of the Association for Computational Linguistics.

[206]  Samuel L. Smith,et al.  Offline bilingual word vectors, orthogonal transformations and the inverted softmax , 2017, ICLR.

[207]  Muhua Zhu,et al.  Deep Cascade Multi-Task Learning for Slot Filling in Online Shopping Assistant , 2018, AAAI.

[208]  Laurent Besacier,et al.  Automatic Speech Recognition for Under-Resourced Languages: Application to Vietnamese Language , 2009, IEEE Transactions on Audio, Speech, and Language Processing.

[209]  Fan Yang,et al.  XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation , 2020, EMNLP.

[210]  Lu Chen,et al.  Towards Universal Dialogue State Tracking , 2018, EMNLP.

[211]  Tetsuji Nakagawa,et al.  An Empirical Study of Language Relatedness for Transfer Learning in Neural Machine Translation , 2017, PACLIC.

[212]  Quoc V. Le,et al.  Towards a Human-like Open-Domain Chatbot , 2020, ArXiv.

[213]  Dat Quoc Nguyen,et al.  Intent detection and slot filling for Vietnamese , 2021, Interspeech.

[214]  Huzefa Rangwala,et al.  Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling , 2021, MRL.

[215]  Rafael E. Banchs,et al.  The fifth dialog state tracking challenge , 2016, 2016 IEEE Spoken Language Technology Workshop (SLT).

[216]  Heiga Zen,et al.  Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning , 2019, INTERSPEECH.

[217]  Tie-Yan Liu,et al.  Soft Contextual Data Augmentation for Neural Machine Translation , 2019, ACL.

[218]  Mark Dredze,et al.  Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT , 2019, EMNLP.

[219]  Ivan Vulić,et al.  Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems , 2019, EMNLP.

[220]  Emiel Krahmer,et al.  DIDEC: The Dutch Image Description and Eye-tracking Corpus , 2018, COLING.

[221]  Gabriel Synnaeve,et al.  MLS: A Large-Scale Multilingual Dataset for Speech Research , 2020, INTERSPEECH.

[222]  Geoffrey Zweig,et al.  Joint semantic utterance classification and slot filling with recursive neural networks , 2014, 2014 IEEE Spoken Language Technology Workshop (SLT).

[223]  Cheng Yang,et al.  Zero-Shot Cross-Lingual Neural Headline Generation , 2018, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[224]  Marjan Ghazvininejad,et al.  Multilingual Denoising Pre-training for Neural Machine Translation , 2020, Transactions of the Association for Computational Linguistics.

[225]  Isabelle Augenstein,et al.  Zero-Shot Cross-Lingual Transfer with Meta Learning , 2020, EMNLP.

[226]  Roi Reichart,et al.  Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal Guidance , 2018, EMNLP.

[227]  Y-Lan Boureau,et al.  Overview of the sixth dialog system technology challenge: DSTC6 , 2019, Comput. Speech Lang..

[228]  Goran Glavas,et al.  Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? , 2019, EMNLP.

[229]  Dietrich Klakow,et al.  Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages , 2020, EMNLP.

[230]  Jianfeng Gao,et al.  DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation , 2020, ACL.

[231]  Saab Mansour,et al.  End-to-End Slot Alignment and Recognition for Cross-Lingual NLU , 2020, EMNLP.

[232]  Eva Schlinger,et al.  How Multilingual is Multilingual BERT? , 2019, ACL.

[233]  Ankur Bapna,et al.  Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation , 2020, ACL.

[234]  Tomek Strzalkowski,et al.  Data-Driven Strategies for an Automated Dialogue System , 2004, ACL.

[235]  Dilek Z. Hakkani-Tür,et al.  DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue , 2020, ArXiv.

[236]  Albert Gatt,et al.  SimpleNLG: A Realisation Engine for Practical Applications , 2009, ENLG.

[237]  Rodney D. Nielsen,et al.  Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network , 2016, COLING.

[238]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[239]  Peng Xu,et al.  Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables , 2019, EMNLP.

[240]  Khalil Sima'an,et al.  Multi30K: Multilingual English-German Image Descriptions , 2016, VL@ACL.

[241]  José Luis Redondo García,et al.  Cross-lingual Alignment Methods for Multilingual BERT: A Comparative Study , 2020, FINDINGS.

[242]  Monojit Choudhury,et al.  GLUECoS: An Evaluation Benchmark for Code-Switched NLP , 2020, ACL.

[243]  Dan Roth,et al.  Named Entity Recognition with Partially Annotated Training Data , 2019, CoNLL.

[244]  Ruhi Sarikaya,et al.  Convolutional neural network based triangular CRF for joint intent detection and slot filling , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.

[245]  Iñigo Casanueva,et al.  Data Collection and End-to-End Learning for Conversational AI , 2019, EMNLP/IJCNLP.

[246]  Kai Zou,et al.  EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks , 2019, EMNLP.

[247]  Jianfeng Gao,et al.  A Diversity-Promoting Objective Function for Neural Conversation Models , 2015, NAACL.

[248]  Minlie Huang,et al.  KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation , 2020, ACL.

[249]  Matthew Henderson,et al.  The Second Dialog State Tracking Challenge , 2014, SIGDIAL Conference.

[250]  Christopher D. Manning,et al.  Key-Value Retrieval Networks for Task-Oriented Dialogue , 2017, SIGDIAL Conference.

[251]  Xiaojun Wan,et al.  Cross-Language Document Summarization Based on Machine Translation Quality Prediction , 2010, ACL.

[252]  Guy Lapalme,et al.  Adapting SimpleNLG for Bilingual English-French Realisation , 2013, ENLG.

[253]  Zhe Gan,et al.  Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization , 2018, NeurIPS.

[254]  Rafael E. Banchs,et al.  R-cube: A dialogue agent for restaurant recommendation and reservation , 2014, Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific.

[255]  Mona T. Diab,et al.  Multi-Domain Goal-Oriented Dialogues (MultiDoGO): Strategies toward Curating and Annotating Large Scale Dialogue Data , 2019, EMNLP.

[256]  Xirong Li,et al.  Adding Chinese Captions to Images , 2016, ICMR.

[257]  Jiliang Tang,et al.  A Survey on Dialogue Systems: Recent Advances and New Frontiers , 2017, SKDD.

[258]  Iryna Gurevych,et al.  MAD-X: An Adapter-based Framework for Multi-task Cross-lingual Transfer , 2020, EMNLP.

[259]  Alberto Bugarín,et al.  Adapting SimpleNLG to Spanish , 2017, INLG.

[260]  Duygu Altinok,et al.  An Ontology-Based Dialogue Management System for Banking and Finance Dialogue Systems , 2018, ArXiv.

[261]  Raymond J. Mooney,et al.  Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language , 2014, J. Artif. Intell. Res..

[262]  Regina Barzilay,et al.  Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing , 2019, NAACL.

[263]  Iryna Gurevych,et al.  UNKs Everywhere: Adapting Multilingual Language Models to New Scripts , 2021, EMNLP.

[264]  Noah A. Smith,et al.  A Simple, Fast, and Effective Reparameterization of IBM Model 2 , 2013, NAACL.