Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog (1810.13327v2)

Abstract: One of the first steps in the utterance interpretation pipeline of many task-oriented conversational AI systems is to identify user intents and the corresponding slots. Since data collection for machine learning models for this task is time-consuming, it is desirable to make use of existing data in a high-resource language to train models in low-resource languages. However, development of such models has largely been hindered by the lack of multilingual training data. In this paper, we present a new data set of 57k annotated utterances in English (43k), Spanish (8.6k) and Thai (5k) across the domains weather, alarm, and reminder. We use this data set to evaluate three different cross-lingual transfer methods: (1) translating the training data, (2) using cross-lingual pre-trained embeddings, and (3) a novel method of using a multilingual machine translation encoder as contextual word representations. We find that given several hundred training examples in the the target language, the latter two methods outperform translating the training data. Further, in very low-resource settings, multilingual contextual word representations give better results than using cross-lingual static embeddings. We also compare the cross-lingual methods to using monolingual resources in the form of contextual ELMo representations and find that given just small amounts of target language data, this method outperforms all cross-lingual methods, which highlights the need for more sophisticated cross-lingual methods.

Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog: Analysis and Implications

The paper "Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog" introduces a new dataset and explores methods to enhance cross-lingual transfer for task-oriented conversational systems. This research tackles a crucial problem in the domain of multilingual conversational AI: efficient and effective multilingual intent and slot detection without the need for massive data annotations in every target language.

Dataset and Experimental Setup

The authors present a dataset comprising 57,000 annotated utterances in English, Spanish, and Thai, spanning the domains of weather, alarms, and reminders. The dataset features 43,000 English utterances, 8,600 Spanish utterances, and 5,000 Thai utterances. This data enables a comparative evaluation of different cross-lingual methods. The paper primarily focuses on three transfer methods: translating the training data, utilizing cross-lingual pre-trained embeddings, and deploying a novel method employing a multilingual machine translation encoder for contextual word representations.

Methodologies Examined

1. Translating Training Data: This involves translating English training utterances into the target languages using machine translation, followed by projection of slot annotations.
2. Cross-Lingual Pre-trained Embeddings: Embeddings like MUSE embeddings are leveraged to train a model that can generalize across languages by mapping words into shared vector spaces.
3. Novel Contextual Word Representations: A multilingual machine translation encoder is repurposed to create contextual embeddings, introducing a cross-lingual dimension for effective representation learning.

The research also juxtaposes these methods against monolingual ELMo representations, highlighting their relative efficacy.

Numerical Results and Findings

The paper reveals several notable outcomes across both high- and low-resource settings:

• Cross-lingual Training Benefits: Models trained across languages consistently outperform those trained solely on target language data, showcasing the efficacy of cross-lingual learning.
• Multilingual Machine Translation Encoder: In scenarios with limited target language data, this novel approach often surpasses using translated training data and displays robustness in very low-resource conditions compared to static cross-lingual embeddings.
• Monolingual Contextual Representations: Given a small amount of target language data, models using monolingual ELMo representations outperform cross-lingual methods, emphasizing the need for advancements in cross-lingual techniques.

The results underscore that while cross-lingual embeddings improve transfer performance, sharing higher-level model parameters, such as the biLSTM layer, plays a pivotal role in the improvement, potentially more so than simply aligning embedding spaces.

Implications and Future Directions

The findings of this paper have significant implications for the development of multilingual conversational AI systems. By demonstrating the potential of cross-lingual models, this research paves the way towards more efficient resource utilization in training dialog systems. The practical aspects highlight a clear advantage in joint training strategies over simple translation methods, suggesting future explorations could delve into further optimizing and understanding parameter sharing across languages.

Future research could explore enhancements through character-level embeddings, especially beneficial for languages with shared scripts, and investigate additional learning objectives to further align cross-lingual vector spaces. Integrating these with comprehensive models like BERT could yield even more pronounced improvements. Additionally, the concept of combining translation with monolingual tasks, as seen in recent unsupervised machine translation efforts, might offer unexplored potential for cross-lingual dialog systems.

In conclusion, this paper's contributions—introducing a multilingual dataset and evaluating cross-lingual methods—offer a foundation for continued research into multilingual task-oriented dialogue systems, with promising avenues for enhancing low-resource language understanding through innovative transfer learning approaches.