SlowGuess/NAACL_2021_OCR huggingface dataset:
Yang Liu, Zi Lin, and Sichen Kang. 2018. Towards a description of Chinese morpheme conceptions and semantic composition of word. Journal of Chinese Information Processing, 32(2):12-21.
Thanapon Noraset, Chen Liang, Larry Birnbaum, and Doug Downey. 2017. Definition modeling: Learning to define word embeddings in natural language. In AAAI 2017, pages 3259-3266.
Hyun-jung Park, Min-chae Song, and Kyung-Shik Shin. 2018. Sentiment analysis of korean reviews using cnn: Focusing on morpheme embedding. Journal of Intelligence and Information Systems, 24(2):59-83.
Koki Washio, Satoshi Sekine, and Tsuneaki Kato. 2019. Bridging the defined and the defining: Exploiting implicit lexical semantic relations in definition modeling. In EMNLP-IJCNLP 2019, pages 3519-3525.
Anna Wierzbicka. 1996. Semantics: Primes and universals: Primes and universals. Oxford University Press, UK.
Liner Yang, Cunliang Kong, Yun Chen, Yang Liu, Qinan Fan, and Erhong Yang. 2020. Incorporating sememes into chinese definition modeling. IEEE/ACM Transactions on Audio, Speech and Language Processing, 28:1669-1677.
Dexi Zhu. 1982. Yufa Jiangyi (Lectures on Grammar). The Commercial Press, China.
Yi Zhu, Ivan Vulic, and Anna Korhonen. 2019. A systematic study of leveraging subword information for learning word representations. In *NAACL-HLT* 2019, pages 912-932.
A Formation-Informed Dataset Details
We show the complete descriptions of 16 formation rules in Table 7.
B Additional Analysis on Formation Rules
Here we provide some additional interesting analyses that reveal the specific properties and influences of word formation rules.
B.1 The similarity among different formation rules
In Section 4.2.1, we produce a comprehensive morpheme embedding using a rule-specific linear layer. We study the relations of the weight matrices  $\mathbf{W}_m^{(rule)}$  in these layers by resizing them into vectors and calculating their pairwise cosine similarity, as shown in Figure 4.
Figure 4 (a) shows that Overlapping is most similar to Suffixation, Prefixation, and Single Morpheme. Interestingly, word meanings constructed
Word Formation Rule	Explanation	Use Case	%
定中 dìng-zhōng (Modifier-Head)	morph\( _1 \)modifies morph\( _2 \)(noun).	红花 hóng-huā (red-flower)	38.62
联合 lián-he (Parallel)	\( morph_1 \) and \( morph_2 \) are similar, contrasting or complementary.	昏花 hūn-huā (dizzy-dim)	22.87
述宾 shù-bīn (Verb-Object)	\( morph_1 \) operates on \( morph_2 \).	花钱 huā qián (spend-money)	16.44
状中 zhuàng-zhōng (Adverb-Verb)	\( morph_1 \) modifies \( morph_2 \)(verb).	白花 bái-huā (vainly-spend)	8.45
单纯 dān-chún (Single Morpheme)	The word is a single morpheme.	花生 huā-shēng (peanut)	3.51
连谓 lián-wèi (Verb-Consequence)	\( morph_2 \) is the consequence of \( morph_1 \).	休息 xiū-xi (stop-rest)	3.43
后缀 hòu-zhuì (Suffixfixation)	\( morph_2 \) is the suffix of \( morph_1 \).	花头 huā-tóu (trick-∅)	2.70
述补 shù-bǔ (Verb-Complement)	\( morph_2 \) is the action follows \( morph_1 \).	压低 yā-dī (press-down)	1.28
主谓 zhǔ-wèi (Subject-Predicate)	\( morph_1 \) is the subject of \( morph_2 \).	眼花 yǎn-huā (eyesight-dim)	1.06
重叠 chóng-dié (Overlapping)	\( morph_1 \) and \( morph_2 \) are the same.	白白 bái-bái (vainly-vainly)	0.59
方位 fāng-wèi (Entity-Position)	\( morph_1 \) is an entity, \( morph_2 \) is a position.	期中 qī-zhōng (semester-mid)	0.37
介宾 jiè-bīn (Preposition-Object)	\( morph_1 \) is a preposition, \( morph_2 \) is an object.	凭空 píng-kōng (from-nowhere)	0.31
名量 míng-liàng (Noun-Quantifier)	\( morph_2 \) is the quantifier of \( morph_1 \).	花朵 huā-duǒ (flower-bud)	0.13
数量 shù-liàng (Number-Quantifier)	\( morph_1 \) is a number, \( morph_2 \) is a quantifier.	一点 yì-diǎn (one-dot)	0.10
前缀 qián-zhuì (Prefixation)	\( morph_1 \) is the prefix of \( morph_2 \).	老师 lǎo-shī (∅-teacher)	0.10
复量 fù-liàng (Quantifier-Quantifier)	Both \( morph_1 \) and \( morph_2 \) are quantifiers.	千米 qián-mǐ (kilo-meter)	0.03
by these four formation rules share a similar pattern of using only one morpheme. For example, in Suffixation, only the first morpheme carries the meaning, like "花头" (trick-∅).
Figure 4 (b) shows that Noun-Quantifier is most similar to Quantifier-Quantifier and Number-Quantifier. Word meanings constructed by these three formation rules all have a quantifier morpheme. For example, in Number-Quantifier, the first morpheme is a number, and the second morpheme is a quantifier, like "一点" (one-dot).
Figure 4 (c) shows that Verb-Object is most similar to Modifier-Head, Parallel and Adverb-Verb. Word meanings constructed by Verb-Object, Modifier-Head and Adverb-Verb share a similar pattern of using the first morpheme to operate on the second morpheme. For example, in Adverb-Verb, the first morpheme modifies the second verb morpheme, like "白花" (vainly-spend).
B.2 The specific impact of formation rules
In Table 8, we generate definitions using different formation rules for the same word. Results
show that each predicted definition indicates a clear pattern of the used formation rule. The Modifier-Head rule uses touched to modify sad, and outputs a noun; the Adverb-Verb rule outputs an adjective in a similar modifying way; the Parallel rule outputs a single meaning of sad with differ
Table 7: Descriptions of the total 16 formation rules.  $\varnothing$  denotes the affix and  $\%$  denotes the instance percentage. The first and the third columns are in the format of "Chinese characters - Chinese phonetic notation - (English translation)". To help understand these rules, we give a simple explanation to describe the relation between two morphemes in the second column.
感伤
Morph1: 感触 (touched)	Morph2: 伤心 (sad)
Verb-Consequence (✓)	因有所感触而悲伤。
(Sad due to being touched.)
Modifier-Head	感触的悲伤。
(Sadness of being touched.)
Parallel	伤心。
(Sad.)
Adverb-verb	难过的伤心。
(Woefully sad.)
Table 8: A case study of 4 predicted definitions of "感伤" using 1 correct formation rule (✓) and 3 others.
Figure 4: We take Verb-Object, Overlapping and Noun-Quantifier weight matrices as three examples and display their similarity with all the other 15 formation rules in the heatmap. The color goes deeper for more similar formation rules. The top 3 most similar formation rules are shown in **Bold**.
ent parts-of-speech. However, only the correct rule (Verb-Consequence) captures the cause-and-effect semantic aspect and outputs the correct definition. This reveals the impact of formation rules in the word formation process.