Cross-Lingual Event Detection via Optimized Adversarial TrainingDownload PDF

Anonymous

17 Sept 2021 (modified: 05 May 2023)ACL ARR 2021 September Blind SubmissionReaders: Everyone
Abstract: In this work, we focus on Cross-Lingual Event Detection (CLED) where a model is trained on data from a source language but its performance is evaluated on data from a second, target, language. Most recent works in this area have harnessed the language-invariant qualities displayed by pre-trained Multi-lingual Language Models (MLM). Their performance, however, reveals there is room for improvement as they mishandle delicate cross-lingual instances. We leverage the use of unlabeled data to train a Language Discriminator (LD) to discern between the source and target languages. The LD is trained in an adversarial manner so that our encoder learns to produce refined, language-invariant representations that lead to improved CLED performance. More importantly, we optimize the adversarial training by only presenting the LD with the most \textit{informative} samples. We base our intuition about \textit{what} makes a sample informative on two disparate metrics: sample similarity and event presence. Thus, we propose using Optimal Transport (OT) as a solution to naturally combine these two distinct information sources into the selection process. Extensive experiments on 8 different language pairs, using 4 languages from unrelated families, show the flexibility and effectiveness of our model that achieves new state-of-the-art results.
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