Emergent Communication Pretraining for Few-Shot Machine Translation

Proceedings of the 28th International Conference on Computational Linguistics, pages 4716–4731 Barcelona, Spain (Online), December 8-13, 2020 4716 Emergent Communication Pretraining for Few-Shot Machine Translation Yaoyiran Li, Edoardo M. Ponti, Ivan Vuli´c and Anna Korhonen Language Technology Lab, TAL, University of Cambridge {yl711,ep490,iv250,alk23}@cam.ac.uk Abstract While state-of-the-art models that rely upon massively multilingual pretrained encoders achieve sample efficiency in downstream applications, they still require abundant amounts of unlabelled text. Nevertheless, most of the world’s languages lack such resources. Hence, we investigate a more radical form of unsupervised knowledge transfer in the absence of linguistic data. In particular, for the first time we pretrain neural networks via emergent communication from referential games. Our key assumption is that grounding communication on images—as a crude approximation of real-world environments—inductively biases the model towards learning natural languages. On the one hand, we show that this substantially benefits machine translation in few-shot settings. On the other hand, this also provides an extrinsic evaluation protocol to probe the properties of emergent languages ex vitro. Intuitively, the closer they are to natural languages, the higher the gains from pretraining on them should be. For instance, in this work we measure the influence of communication success and maximum sequence length on downstream performances. Finally, we introduce a customised adapter layer and annealing strategies for the regulariser of maximum-a-posteriori inference during fine-tuning. These turn out to be crucial to facilitate knowledge transfer and prevent catastrophic forgetting. Compared to a recurrent baseline, our method yields gains of 59.0%∼147.6% in BLEU score with only 500 NMT training instances and 65.1%∼196.7% with 1, 000 NMT training instances across four language pairs. These proof- of-concept results reveal the potential of emergent communication pretraining for both natural language processing tasks in resource-poor settings and extrinsic evaluation of artificial languages. 1 Introduction Zero-shot and few-shot learning are notoriously challenging for neural networks (Bottou and Bousquet, 2008; Vinyals et al., 2016; Ravi and Larochelle, 2017). However, they are a prerequisite for natural language processing in most languages, which suffer from the paucity of annotated data (Ponti et al., 2019a). State-of-the-art models rely on knowledge transfer, whereby an encoder is pretrained via language modeling on texts from multiple languages, and subsequently ‘fine-tuned’ on labelled examples of resource-rich languages (Wu and Dredze, 2019; Conneau et al., 2020) or few examples in a target resource- poor language (Lauscher et al., 2020). However, even raw texts required for pretraining are scant (Kornai, 2013): for instance, Wikipedia dumps cover 278 languages out of the 7,097 spoken world-wide (Eberhard et al., 2020). For this reason, we push the idea of cross-lingual knowledge transfer even further, exploring and profiling a setting where not even raw natural language data for a target language are available for unsupervised pretraining. In their stead, we exploit artificial languages emerging from a referential game on raw images (Kazemzadeh et al., 2014; Lazaridou et al., 2017). In particular, we encourage agents to cooperate in identifying images among distractors by communicating over vocabularies whose meanings are unknown. The key intuition is …

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