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A Comparison of Wordpiece Selection Methods
Table 3 of the main paper contains results of the DIFFVEC method with the best approach for selecting wordpieces (WPs) for each model. In Table 5, we present results obtained using the alternative approach for each model and language:
- for all monolingual models and the multilingual model for English, Table 5 contains results obtained with the WP approach;
- for the multilingual models in the other languages, we show results with WP-1.
The best approach was determined by comparing their average scores across the different methods. Some configurations improve, but they yield overall worse results per model, especially in Spanish. Differences between WP and WP-1 are generally more pronounced in the multilingual models than in the monolingual models.
EN
Mono WP	FR
Mono WP	ES
Mono WP	EL
Mono WP
P-ACC	τ	ρavg	P-ACC	τ	ρavg	P-ACC	τ	ρavg	P-ACC	τ	ρavg
DM	DV-1 (+)	.6649	.4639	.5319	.6173	.3843	.4063	.6529	.3679	.3909	.5468	.2018	.2158
DV-WK	.5579	.2469	.2846	.5171	.1701	.1401	.64510	.35310	.31310	.5572	.2262	.2402
WK	DV-1 (+)	.8527	.7057	.7661	.6127	.2621	.2156	.7638	.5258	.7556	.6328	.3128	.2568
DV-DM	.9186	.8366	.8396	.6272	.2922	.3922	.7466	.4926	.6586	.7791	.61711	.66311
Multi WP	Multi WP-1	Multi WP-1	Multi (unc) WP-1
DM	DV-1 (+)	.5884	.3014	.3124	.5497	.2397	.2767	.5893	.2293	.2341	.5249	.1539	.1719
DV-WK	.5165	.15311	.1985	.4902	.1132	.1347	.60312	.26812	.28712	.5216	.1466	.1866
WK	DV-1 (+)	.8207	.6397	.6673	.6123	.2623	.3623	.7464	.4924	.6084	.6479	.3589	.3699
DV-DM	.8857	.7707	.8347	.6877	.4127	.4353	.66110	.32210	.4476	.6626	.3886	.4446
Table 5: Results of DIFFVEC (DV) methods with contextualised representations derived from monolingual and multilingual models for each language, using an alternative approach to selecting wordpieces (WP, WP-1) than the one used for the results reported in Table 3. For all languages but Greek, the multilingual model is cased.