Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking

Schick, Timo und Schütze, Hinrich (April 2019): Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York, USA, February 7-12, 2020.

Abstract

Pretraining deep neural network architectures with a language modeling objective has brought large improvements for many natural language processing tasks. Exemplified by BERT, a recently proposed such architecture, we demonstrate that despite being trained on huge amounts of data, deep language models still struggle to understand rare words. To fix this problem, we adapt Attentive Mimicking, a method that was designed to explicitly learn embeddings for rare words, to deep language models. In order to make this possible, we introduce one-token approximation, a procedure that enables us to use Attentive Mimicking even when the underlying language model uses subword-based tokenization, i.e., it does not assign embeddings to all words. To evaluate our method, we create a novel dataset that tests the ability of language models to capture semantic properties of words without any taskspecific fine-tuning. Using this dataset, we show that adding our adapted version of Attentive Mimicking to BERT does substantially improve its understanding of rare words.

• Dokumententyp: Konferenzbeitrag (Paper)
• EU Funded Grant Agreement Number: 740516
• EU-Projekte: Horizon 2020 > ERC Grants > ERC Advanced Grant > ERC Grant 740516: NonSequeToR - Non-sequence models for tokenization replacement
• Fakultätsübergreifende Einrichtungen: Centrum für Informations- und Sprachverarbeitung (CIS)
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme ; 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik ; 400 Sprache > 400 Sprache ; 400 Sprache > 410 Linguistik
• Sprache: Englisch