MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark (2008.09335v2)

Abstract: Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets. Available datasets suffer from several shortcomings: a) they contain few languages b) they contain small amounts of labeled examples per language c) they are based on the simple intent and slot detection paradigm for non-compositional queries. In this paper, we present a new multilingual dataset, called MTOP, comprising of 100k annotated utterances in 6 languages across 11 domains. We use this dataset and other publicly available datasets to conduct a comprehensive benchmarking study on using various state-of-the-art multilingual pre-trained models for task-oriented semantic parsing. We achieve an average improvement of +6.3 points on Slot F1 for the two existing multilingual datasets, over best results reported in their experiments. Furthermore, we demonstrate strong zero-shot performance using pre-trained models combined with automatic translation and alignment, and a proposed distant supervision method to reduce the noise in slot label projection.

Overview of MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark

The paper presents MTOP, an extensive multilingual dataset tailored for task-oriented semantic parsing in dialog systems, addressing critical gaps in existing resources that stymie advancements across multiple languages. Traditional datasets for semantic parsing suffer from language constraints, limited annotations, and simplistic query paradigms focused mainly on intent and slot detection. MTOP steps forward with 100,000 annotated utterances distributed across six languages and eleven domains, enabling the development and benchmarking of sophisticated semantic parsing models on a multilingual scale.

Core Contributions

1. MTOP Dataset: The dataset is pioneering in its inclusion of compositional representations that facilitate accurate parsing of complex nested queries. This incorporation allows for more nuanced semantic representations, marking a departure from the limitations of previous resources that handle simpler queries.
2. Benchmarking with State-of-the-Art Models: The paper benchmarks advanced multilingual pre-trained models on the MTOP dataset, illustrating significant improvements, notably an average increase of +6.3 Slot F1 points over existing multilingual datasets. This highlights the potential of such models in enhancing semantic parsing capabilities beyond the English language.
3. Zero-Shot Cross-Lingual Performance: By leveraging pre-trained models combined with automatic translation and alignment, the authors report strong zero-shot cross-lingual transfer results. Notably, the paper achieves an exact match accuracy of 67.2% averaged across five languages, without utilizing any target language data, raising intriguing possibilities for LLMs that inherently generalize across linguistic boundaries.

Methodological Insights

The authors present a comprehensive methodology for multilingual training, zero-shot settings, and translation-based data augmentation, fortifying the reliability and adaptability of their models. The inclusion of distant supervision techniques and multitask training further refines model robustness against noisy data and biases in slot label projection. Through these technical elaborations, the research positions itself as an instrumental guide for the development of cross-lingual dialog systems capable of handling complex task-oriented queries.

Results and Discussions

The MTOP benchmarks demonstrate substantial performance gains across different evaluation settings, including in-language and multilingual models. Notably, the use of XLM-R encoder and CRISS model underscores the advantages of transformer-based architectures in obtaining high accuracy for compositional decoupled representations. The model architectures presented achieve impressive exact match accuracies, with multilingual training strategies contributing further improvements.

Implications and Future Directions

In setting this precedent, the MTOP dataset enriches the exploration space for task-oriented semantic parsing, underscoring the necessity for multilingual resources that capture complex semantic knowledge. Practically, this paves the way for more inclusive dialog systems capable of understanding diverse linguistic nuances, driving real-world applications in virtual assistants and automated customer support systems. Theoretically, it stimulates discourse on the optimization of cross-lingual models, encouraging future research to explore transformer-based architectures and alignment methodologies.

In conclusion, MTOP emerges as a vital benchmark for multilingual semantic parsing, pushing the boundaries of LLM efficacy across languages with differing structures. The insights drawn from this paper are poised to propel future developments in natural language processing and enhance the scope of task-oriented dialog systems, ensuring they are equipped to meet the challenges posed by a globalized linguistic landscape.