[论文解读] Crossing the Conversational Chasm: A Primer on Multilingual Task-Oriented Dialogue Systems.

Evgeniia Razumovskaia, Goran Glavašš+3 more|arXiv (Cornell University)|Apr 17, 2021

Topic Modeling参考文献 108被引用 13

一句话总结

本文 identifies data scarcity and reliance on high-resource languages like English as core barriers to multilingual task-oriented dialogue (ToD) systems. It reviews cross-lingual transfer methods—machine translation and multilingual embeddings—and argues for scalable, low-resource solutions through cross-lingual transfer and multilingual pretraining to enable truly multilingual ToD.

ABSTRACT

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 (zero- or 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.

研究动机与目标

• To analyze the current limitations in developing multilingual task-oriented dialogue (ToD) systems.
• To identify data scarcity and dependency on high-resource languages like English as primary bottlenecks in multilingual ToD.
• To evaluate existing cross-lingual transfer approaches—machine translation and multilingual representation spaces—for low-resource language transfer.
• To propose pathways for scalable multilingual ToD by drawing parallels with broader multilingual NLP research.
• To highlight the urgent need for low-resource, data-efficient methods beyond zero- or few-shot transfer from English.

提出的方法

• Surveying existing multilingual ToD systems and their reliance on transfer from high-resource languages, primarily English.
• Analyzing the role of large pretrained neural language models in driving data hunger for effective ToD performance.
• Evaluating two main transfer strategies: (i) using machine translation to translate high-resource language data into low-resource languages, and (ii) leveraging multilingual representation spaces for zero- or few-shot transfer.
• Drawing analogies from broader multilingual NLP research to identify scalable, data-efficient techniques applicable to multilingual ToD.
• Highlighting the lack of parallel data and limited monolingual corpora as key constraints for low-resource languages.
• Proposing that future progress depends on innovations in multilingual pretraining and low-resource fine-tuning strategies.

实验结果

研究问题

• RQ1 What are the primary technical and data-related barriers preventing the development of truly multilingual task-oriented dialogue systems?
• RQ2 How effective are zero- or few-shot cross-lingual transfer methods in low-resource language settings for ToD?
• RQ3 To what extent can machine translation and multilingual representation spaces substitute for monolingual ToD data in low-resource languages?
• RQ4 What lessons from broader multilingual NLP can be applied to overcome data scarcity in multilingual ToD?
• RQ5 What scalable, data-efficient approaches are needed to enable multilingual ToD beyond reliance on English as the sole source language?

主要发现

• Current state-of-the-art ToD models based on large pretrained language models are highly data-hungry, limiting their applicability to low-resource languages.
• Most multilingual ToD approaches rely on transfer from English, which is not viable for languages lacking parallel data or sufficient monolingual corpora.
• Zero- or few-shot transfer via machine translation or multilingual embeddings fails to deliver robust performance in low-resource settings due to data scarcity.
• The lack of parallel data and limited monolingual corpora remains a critical bottleneck for low-resource languages in multilingual ToD.
• Cross-lingual transfer methods are currently insufficient for widespread deployment across diverse languages without significant improvements in data efficiency.
• Future progress in multilingual ToD depends on scalable, low-resource solutions inspired by advances in multilingual representation learning and pretraining.