Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling

• URL: http://arxiv.org/abs/2103.07792v2
• Date: Tue, 16 Mar 2021 16:39:48 GMT
• Title: Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling
• Authors: Jitin Krishnan, Antonios Anastasopoulos, Hemant Purohit, and Huzefa Rangwala
• Abstract summary: We propose a novel method to augment the monolingual source data using multilingual code-switching via random translations. Experiments on the benchmark dataset of MultiATIS++ yielded an average improvement of +4.2% in accuracy for intent task and +1.8% in F1 for slot task. We present an application of our method for crisis informatics using a new human-annotated tweet dataset of slot filling in English and Haitian Creole.
• Score: 29.17194639368877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting user intent and detecting the corresponding slots from text are two key problems in Natural Language Understanding (NLU). In the context of zero-shot learning, this task is typically approached by either using representations from pre-trained multilingual transformers such as mBERT, or by machine translating the source data into the known target language and then fine-tuning. Our work focuses on a particular scenario where the target language is unknown during training. To this goal, we propose a novel method to augment the monolingual source data using multilingual code-switching via random translations to enhance a transformer's language neutrality when fine-tuning it for a downstream task. This method also helps discover novel insights on how code-switching with different language families around the world impact the performance on the target language. Experiments on the benchmark dataset of MultiATIS++ yielded an average improvement of +4.2% in accuracy for intent task and +1.8% in F1 for slot task using our method over the state-of-the-art across 8 different languages. Furthermore, we present an application of our method for crisis informatics using a new human-annotated tweet dataset of slot filling in English and Haitian Creole, collected during Haiti earthquake disaster.

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