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. Author manuscript; available in PMC: 2022 Oct 19.
Published in final edited form as: Proc Int Conf Comput Ling. 2022 Oct;2022:2979–2991.

Table 3:

Performance of T5 with domain adaptation pre-training using Assessment (Assmt) as input, under two mask policies: Token Masking and Concept Masking. We report Rouge-L F-score (RL-F), and BERTScore (BS), as well as Sentence embedding cosine similarity (Sent θ) and CUI F-score. Numbers with green background address the highest performance across all results, with subscript number (↑) denoting the improvements over rule-based results.

Setting Model Token Masking Concept Masking

RL-F Sent.θ BS CUI RL-F Sent.θ BS CUI
Explicit Assmt 32.66 61.34(↑2.53) 56.68 47.10(↑8.13) 29.86 55.87 53.91 40.27(↑2.14)
++ 26.94 59.40(↑0.59) 55.05 42.73(↑3.76) 32.82 58.21 56.80 43.16(↑4.19)

Direct Assmt 12.69 53.63 42.40(↑2.27) 35.39(↑1.16) 14.90(↑2.59) 55.48(↑0.15) 47.10(↑6.97) 35.29(↑1.06)
++ 10.44 53.47 43.46(↑3.33) 37.45(↑3.22) 15.76(↑5.22) 56.82(↑1.49) 48.72(↑8.72) 37.74(↑3.51)

Indirect Assmt 10.07(↑0.58) 52.72 41.47 38.19(↑5.03) 13.58(↑4.36) 53.44 44.91(↑0.45) 33.56(↑0.40)
++ 8.04 51.84 40.45 37.53(↑4.37) 13.28(↑4.06) 55.02 45.51(↑1.05) 35.10(↑1.94)

All Assmt 14.49(↑1.04) 62.40 49.62 40.44 18.72(↑5.27) 64.69 54.03(↑3.71) 42.69
++ 12.12 63.08 50.20 45.58(↑1.65) 18.80(↑5.35) 66.08 55.29(↑4.86) 44.56(↑0.63)