Natural Language Processing for Multilingual Task-Oriented Dialogue (2022)

Abstract: Recent advances in deep learning have also enabled fast progress in the research of task-oriented dialogue (ToD) systems. However, the majority of ToD systems are developed for English and merely a handful of other widely spoken languages, e.g., Chinese and German. This hugely limits the global reach and, consequently, transformative socioeconomic potential of such systems. In this tutorial, we will thus discuss and demonstrate the importance of (building) multilingual ToD systems, and then provide a systematic overview of current research gaps, challenges and initiatives related to multilingual ToD systems, with a particular focus on their connections to current research and challenges in multilingual and low-resource NLP. The tutorial will aim to provide answers or shed new light to the following questions: a) Why are multilingual dialogue systems so hard to build: what makes multilinguality for dialogue more challenging than for other NLP applications and tasks? b) What are the best existing methods and datasets for multilingual and cross-lingual (task-oriented) dialog systems? How are (multilingual) ToD systems usually evaluated? c) What are the promising future directions for multilingual ToD research: where can one draw inspiration from related NLP areas and tasks?

## motivation and objectives enabling machines to intelligently converse with humans in order to solve particular well-defined tasks is in the core focus of task-oriented dialogue (tod) systems and their development @xcite @xcite . such systems have wide applications in a multitude of domains such as hospitality industry, travel, e-banking, healthcare, entertainment industry, industrial production and maintenance, etc. tod-oriented research has been recently catalysed by the growing ability and viability of deep learning techniques such as largescale pretraining of language models @xcite @xcite @xcite . the momentum of development in this research area has, however, mainly targeted a very small proportion of potential beneficiaries: most existing tod systems are predominantly built for english and a few other, major languages only (e.g., chinese) @xcite . this limits the use, global reach, and transformative potential of tod systems. consequently, this deepens the chasm between speakers of dominant versus underrepresented low-resource languages in their access to state-of-the-art language technology @xcite and contributes to the digital language divide and inequality of information. 1 extending the reach of tod technology is crucial for the democratisation and wide adoption of human-machine communication, with an inclusive long-term goal of bringing it to virtually all citizens of the world. building on top of our recent comprehensive survey on the topic of multilingual tod @xcite , in this tutorial our aim is to systematise the current research on multilingual tod, and offer a fresh perspective to other researchers and nlp practitioners on the importance and challenges of developing multilingual tod systems. the tutorial will offer a comprehensive overview of multilingual tod research and know-hows focused on the following central questions: foot_1 (q1) why are multilingual tod systems so hard to build? what are the main roadblocks and how can we facilitate their development? what are the main design paradigms of multilingual tod? (q2) which tod datasets are currently available in one or more languages other than english? what are their strengths and weaknesses? how can we improve the current data design and collection efforts and protocols? (q3) what are the best methods and practices to incorporate language-specific information and perform target language adaptation for multilingual and cross-lingual tod? (q4) how can multilingual tod take inspiration from other related fields of nlp research to better tackle low-resource scenarios (e.g., cross-lingual transfer, injection of external knowledge into parameters of neural models)? (q5) how good are current (multilingual) tod systems? do automatic evaluation measures correlate with user satisfaction? what implication does multilinguality have on tod evaluation? part i will cover the basics of modular and end-toend dialogue systems @xcite @xcite , offering a brief overview of the full tod system structure, and critical modules such as speech-totext/asr, natural language understanding (nlu) for dialogue, dialogue state tracking (dst), dialogue management (dm), natural language generation (nlg), and text-to-speech (tts), along with their functionality. we will analyse which components require language-specific processing and adaptation, and which modules are generally language-invariant. we will then proceed to define the detailed scope and schedule of the tutorial. concretely, we plan to list and discuss all the current problems and challenges related to multilingual tod development, and how we will introduce them in the subsequent tutorial parts. topics overview: part ii: methods and resources for multilingual nlu in tod (50 minutes) part ii will cover to-date work in multilingual nlu in tod, including standard approaches and recent trends. we will provide a comprehenstive overview of methods for learning cross-lingual representation spaces in tod @xcite @xcite and their applications in different setups (multilingual vs. cross-lingual, zero-shot vs. few-shot). finally, we will list available resources: those created specifically for multilingual tod nlu @xcite @xcite part iv: evaluation of multilingual tod systems (30 minutes) part iv will focus on evaluation for (multilingual) tod. we will cover both automatic metrics and human evaluation: automatic metrics allow for faster development cycles, but often do not correlate with user satisfaction with tod systems @xcite . we will discuss the shortcomings of automated tod evaluation, but also the potential pitfalls of human evaluation @xcite . we will then analyse the difficulties that multilingual setups pose for both automatic metrics and human evaluation, including evaluation of generated responses in morpho-logically rich languages and difficulty of finding qualified evaluators for rare languages. topics: ## tutorial breadth and diversity according to the representative set of papers listed in the selected bibliography as well as in our recent survey paper @xcite , we anticipate that a total of 20%-25% of the tutorial concerns work which involves at least one of the five presenters. the rest of the tutorial will focus on providing a detailed comprehensive overview of the main topic by covering all the relevant work from other researchers: see again the wide bibliography and coverage in the survey paper. ## prerequisites and reading list math: no special requirements; linguistics: basic knowledge of language typology and of morphology (recommended); machine learning: good grasp of core (supervised) machine learning concepts and familiarity with self-supervised pretraining of language models (required). pre-tutorial reading list (examples): systematic inequalities in language technology performance across the world's languages. arxiv preprint arxiv:2110.06733. ## other tutorial information related tutorials. conversational ai and (components of) tod systems have been taught in several tutorials in past years, where the focus has been put on diverse aspects such as: deep learning techniques for tod @xcite @xcite , data collection and end-to-end learning @xcite , or nlg methods @xcite and their evaluation (khapra and sai, naacl-hlt 2021). however, our tutorial is the first to focus on the crucial aspects of multilingualism and low-resource languages in relation to the design, development, evaluation, and application of (multilingual) tod systems. our tutorial offers a completely novel and unique perspective to tod also through the optics of multilingual nlp.