Enhanced MEDIA Benchmark for French Spoken Language Understanding with Intent Annotations

Core Concepts

This paper presents an enhanced version of the MEDIA benchmark dataset for French Spoken Language Understanding (SLU), with newly added intent annotations. It also provides baseline results for joint intent classification and slot-filling models on this enhanced dataset, using both manual transcriptions and automatic speech recognition outputs.

Abstract

The paper presents an enhanced version of the MEDIA benchmark dataset for French Spoken Language Understanding (SLU). The original MEDIA dataset, distributed by ELRA since 2005, is a French dataset mainly used by the French research community and free for academic research since 2020. It is annotated with semantic concepts (slots) but not with intents. To extend the use of MEDIA to more tasks and use cases, the authors built an enhanced version of the dataset annotated with intents. They used a semi-automatic approach based on a tri-training algorithm with Transformer-based models to obtain these intent annotations. The resulting enhanced MEDIA dataset contains 11 intent labels, with some utterances associated with multiple intents. The paper then presents baseline results for joint intent classification and slot-filling models on this enhanced MEDIA dataset, using both manual transcriptions and automatic speech recognition (ASR) outputs. For manual transcriptions, the authors explore different French Transformer models (CamemBERT, FrALBERT, FlauBERT) and compare cascade and end-to-end architectures. The results show that end-to-end models perform better on intent classification, while cascade models are more competitive on the slot-filling task. For ASR outputs, the authors use a cascade approach with a speech encoder followed by the joint intent and slot-filling model. They also explore different speech encoders, including SAMU-XLSR, SAMU-XLSRIT⊕FR, and LeBenchmark FR 3k large. The results indicate that end-to-end models can achieve state-of-the-art performance on the slot-filling task for the MEDIA 2022 full version. Overall, this work provides a new enhanced version of the MEDIA dataset and establishes baselines for joint intent classification and slot-filling on this dataset, paving the way for further research in French Spoken Language Understanding.

Stats

The MEDIA dataset represents 1258 official recorded dialogues from 250 different speakers and about 70 hours of conversations. The MEDIA dataset has 83 attributes and 19 specifiers, resulting in 1121 possible attribute/specifier pairs. The enhanced MEDIA dataset contains 11 intent labels, with some utterances associated with multiple intents.

Quotes

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Key Insights Distilled From

New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark

by Nadè... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.19727.pdf

Deeper Inquiries

What are the potential use cases and applications of the enhanced MEDIA dataset with intent annotations

The enhanced MEDIA dataset with intent annotations opens up a range of potential use cases and applications in the field of spoken language understanding. One key application is the development of advanced virtual assistants and chatbots capable of understanding user intents more accurately. This dataset can be used to train models for natural language understanding in various domains such as customer service, healthcare, education, and more. Additionally, it can be utilized in research settings to explore new techniques for improving semantic understanding in spoken dialog systems. The dataset can also serve as a benchmark for evaluating the performance of different models and approaches in joint intent classification and slot-filling tasks.

How can the performance of joint intent classification and slot-filling models be further improved on the MEDIA dataset, especially for the more challenging full version

To improve the performance of joint intent classification and slot-filling models on the more challenging full version of the MEDIA dataset, several strategies can be implemented. Data Augmentation: Increasing the diversity of training data through data augmentation techniques can help the model generalize better to unseen examples. Model Architecture: Experimenting with more complex architectures or ensembling multiple models can enhance the model's ability to capture intricate patterns in the data. Fine-tuning: Fine-tuning pre-trained language models on the specific task and dataset can lead to better performance by leveraging the knowledge learned from large-scale data. Hyperparameter Tuning: Optimizing hyperparameters such as learning rate, batch size, and dropout rates can significantly impact model performance. Error Analysis: Conducting thorough error analysis to identify common failure patterns and areas of improvement can guide targeted model enhancements. By incorporating these strategies and continuously iterating on model development, the performance of joint intent classification and slot-filling models on the MEDIA dataset can be further enhanced.

What other French language understanding benchmarks could benefit from a similar semi-automatic approach to annotate intents and expand their capabilities

Other French language understanding benchmarks that could benefit from a similar semi-automatic approach to annotate intents and expand their capabilities include: ATIS FR Dataset: Extending the ATIS dataset to include intent annotations in French could enhance its usability for training and evaluating models in the French language understanding domain. MultiATIS++ Corpus: Similar to the ATIS FR dataset, enriching the MultiATIS++ corpus with intent annotations could provide a more comprehensive benchmark for evaluating SLU models in French. SNIPS Dataset: Annotating the SNIPS dataset with intents in French could broaden its applicability for evaluating multilingual SLU models and enhancing performance on French language understanding tasks. By applying a semi-automatic methodology to annotate intents in these benchmarks, researchers and practitioners can leverage the enhanced datasets to advance the development of robust and accurate language understanding models in the French language.