Multi3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue (2023.findings-acl)
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| Challenge: | Task-oriented dialogue systems are typically constructed for a single domain or language and do not generalise well beyond this. |
| Approach: | They constructed a multilingual, multi-intent, multi domain dataset to support work on Natural Language Understanding (NLU) in ToD across multiple languages and domains simultaneously. |
| Outcome: | The proposed dataset extends the English-only dataset to include manual translations into a range of high, medium, and low resource languages in two domains (banking and hotels). |
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NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue (2022.findings-naacl)
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| Challenge: | NLU++ provides a more challenging evaluation environment for dialogue NLU models . Typical ToD systems still rely on a modular design . |
| Approach: | They propose to use NLU++ to provide a more challenging evaluation environment for dialogue NLU models. |
| Outcome: | The proposed dataset improves existing datasets and provides a much more challenging evaluation environment for dialogue NLU models. |
Natural Language Processing for Multilingual Task-Oriented Dialogue (2022.acl-tutorials)
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| Challenge: | a tutorial will examine the challenges and gaps in multilingual ToD research . multilingual systems are difficult to build, and are limited to English and other languages . |
| Approach: | This tutorial will discuss the importance of multilingual task-oriented dialogue systems . it will provide an 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. |
| Outcome: | This tutorial will provide an overview of current research gaps, challenges and initiatives related to multilingual ToD systems. |
Multi 3 WOZ: A Multilingual, Multi-Domain, Multi-Parallel Dataset for Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems (2023.tacl-1)
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Songbo Hu, Han Zhou, Mete Hergul, Milan Gritta, Guchun Zhang, Ignacio Iacobacci, Ivan Vulić, Anna Korhonen
| Challenge: | Task-oriented dialog (TOD) is one of the central objectives, hallmarks, and applications of machine intelligence. |
| Approach: | They propose a multilingual, multi-domain, multiparallele ToD dataset that offers culturally adapted dialogs in 4 languages for training and evaluation of multilingual and cross-lingual systems. |
| Outcome: | The proposed dataset is large-scale and culturally adapted to enable training and evaluation of multilingual and cross-lingual ToD systems. |
Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog (2022.naacl-main)
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| Challenge: | Task-oriented dialog (TOD) is arguably one of the most popular natural language processing (NLP) application areas. |
| Approach: | They propose a multilingual multi-domain TOD dataset that spans four languages . they use a framework for multilingual conversational specialization of pretrained language models . |
| Outcome: | The proposed datasets show that they perform better than existing datasets in English . the proposed framework allows for sample-efficient few-shot transfer for TOD tasks . |
GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems (2022.acl-long)
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| Challenge: | Existing multilingual task-oriented dialogue datasets lack high-quality data curation due to the high expense and challenges of human annotation. |
| Approach: | They propose a method that generates a multilingual ToD dataset globalized from an English ToD data set for three unexplored use cases of multilingual toD systems. |
| Outcome: | The proposed method generates a large-scale multilingual ToD dataset globalized from an English ToD data set for three unexplored use cases of multilingual toD systems. |
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation (2023.tacl-1)
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| Challenge: | Multilingual task-oriented dialogue (ToD) datasets suffer from severe limitations, such as being small in scale and lacking naturalness and cultural specificity in the target language. |
| Approach: | They propose a novel outline-based annotation process where domain-specific abstract schemata of dialogue are mapped into natural language outlines. |
| Outcome: | The proposed approach improves understanding, dialogue state tracking, and end-to-end dialogue evaluation in Arabic, Indonesian, Russian, and Kiswahili. |
BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing multilingual benchmarks focus primarily on language understanding tasks. |
| Approach: | They develop a multi-way multilingual benchmark that measures critical capabilities of large language models across languages. |
| Outcome: | Extensive experiments on BenchMAX reveal uneven utilization of core capabilities across languages, emphasizing the performance gaps that scaling model size alone does not resolve. |
TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue (2020.emnlp-main)
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| Challenge: | Existing pre-trained language models with self-attention encoder architectures are less useful in practice. |
| Approach: | They propose to use user and system tokens to model dialogue behavior during pre-training . they propose a contrastive objective function to simulate the response selection task . |
| Outcome: | The proposed model outperforms baseline models on four downstream tasks . it also has a few-shot ability that can mitigate the data scarcity problem . |
DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models (2024.naacl-demo)
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| Challenge: | DIALIGHT is a toolkit for developing and evaluating multilingual Task-Oriented Dialogue systems. |
| Approach: | They propose a toolkit for developing and evaluating multilingual Task-Oriented Dialogue systems which facilitates systematic evaluations and comparisons between ToD systems using pretrained language models and those utilising the zero-shot and in-context learning capabilities of Large Language Models. |
| Outcome: | The toolkit enables systematic evaluations between ToD systems using pretrained language models and those utilising the zero-shot and in-context learning capabilities of Large Language Models (LLMs). |
XNLI: Evaluating Cross-lingual Sentence Representations (D18-1)
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Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, Veselin Stoyanov
| Challenge: | State-of-the-art natural language processing systems rely on annotated data to learn competent models. |
| Approach: | They extend the development and test sets of the Multi-Genre Natural Language Inference Corpus to 14 languages, including Swahili and Urdu. |
| Outcome: | The proposed evaluation set extends the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 14 languages including low-resource languages such as Swahili and Urdu. |