Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction



Bollegala, Danushka, Kiryo, Ryuichi, Tsujino, Kosuke and Yukawa, Haruki
(2020) Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction. PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2020), abs/20. pp. 3851-3860.

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Abstract

Language-independent tokenisation (LIT) methods that do not require labelled language resources or lexicons have recently gained popularity because of their applicability in resource-poor languages. Moreover, they compactly represent a language using a fixed size vocabulary and can efficiently handle unseen or rare words. On the other hand, language-specific tokenisation (LST) methods have a long and established history, and are developed using carefully created lexicons and training resources. Unlike subtokens produced by LIT methods, LST methods produce valid morphological subwords. Despite the contrasting trade-offs between LIT vs. LST methods, their performance on downstream NLP tasks remain unclear. In this paper, we empirically compare the two approaches using semantic similarity measurement as an evaluation task across a diverse set of languages. Our experimental results covering eight languages show that LST consistently outperforms LIT when the vocabulary size is large, but LIT can produce comparable or better results than LST in many languages with comparatively smaller (i.e. less than 100K words) vocabulary sizes, encouraging the use of LIT when language-specific resources are unavailable, incomplete or a smaller model is required. Moreover, we find that smoothed inverse frequency (SIF) to be an accurate method to create word embeddings from subword embeddings for multilingual semantic similarity prediction tasks. Further analysis of the nearest neighbours of tokens show that semantically and syntactically related tokens are closely embedded in subword embedding spaces

Item Type: Article
Additional Information: To appear in the 12th Language Resources and Evaluation (LREC 2020) Conference
Uncontrolled Keywords: Subtokenisation, Byte Pair Encoding, Language Independent Tokenisation
Depositing User: Symplectic Admin
Date Deposited: 05 Mar 2020 08:41
Last Modified: 18 Jan 2023 23:59
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3077740