| Challenge: | Slot labeling (SL) is a key component of task-oriented dialogue systems . extending the system to any new language-domain-task configuration requires expensive data annotation . |
| Approach: | They propose a two-stage slot labeling approach which transforms sentence encoders into effective slot labels. |
| Outcome: | The proposed approach is especially effective for the most challenging transfer-free few-shot setups. |
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| Challenge: | Existing methods to expand an NLU system to new languages use machine translation with slot label projection from source to translated utterances. |
| Approach: | They propose a novel end-to-end model that learns to align and predict target slot labels jointly for cross-lingual transfer. |
| Outcome: | The proposed model outperforms a simple label projection method on most languages and achieves competitive performance to the more complex, state-of-the-art projection method with only half the training time. |
Translate and Label! An Encoder-Decoder Approach for Cross-lingual Semantic Role Labeling (D19-1)
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| Challenge: | Unlike annotation projection techniques, our model does not need parallel data during inference time. |
| Approach: | They propose a cross-lingual Encoder-Decoder model that simultaneously translates and generates sentences with semantic role annotations in a resource-poor target language. |
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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. |
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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. |
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Building a Task-oriented Dialog System for Languages with no Training Data: the Case for Basque (2020.lrec-1)
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| Challenge: | a new approach to task-oriented dialog systems is proposed for less-resourced languages . the authors compare training data projection with direct model transfer strategies . |
| Approach: | They propose to project existing annotations in rich-resource languages and use NMT to project them. |
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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. |
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MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark (2021.eacl-main)
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| Challenge: | Existing datasets for task-oriented dialog systems are limited and expensive . current models are based on the simple intent and slot detection paradigm for non-compositional queries. |
| Approach: | They propose to use a multilingual dataset to scale semantic parsing models to new languages . they demonstrate an average improvement of +6.3 points on Slot F1 for existing datasets . |
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A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling (P18-1)
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| Challenge: | Existing studies have shown that multi-task learning can boost the performance of related tasks such as MT and abstractive text summarization. |
| Approach: | They propose a multi-lingual multi-task architecture to develop supervised models with a minimal amount of labeled data for sequence labeling. |
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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. |
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 . |
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