Quantifying Discourse Support for Omitted Pronouns (2022)

Abstract: Pro-drop is commonly seen in many languages, but its discourse motivations have not been well characterized. Inspired by the topic chain theory in Chinese, this study shows how character-verb usage continuity distinguishes dropped pronouns from overt references to story characters. We model the choice to drop vs. not drop as a function of character-verb continuity. The results show that omitted subjects have higher character history-current verb continuity salience than non-omitted subjects. This is consistent with the idea that discourse coherence with a particular topic, such as a story character, indeed facilitates the omission of pronouns in languages and contexts where they are optional.

## introduction pro-drop is a phenomenon that pronouns can be omitted when they are inferable. it is common across the world's languages, and mandarin chinese is one of them (see examples (3) and (4) in figure 1 ). omitted pronouns in these languages, also called zero pronouns, are increasingly important in computational linguistics (e.g. @xcite @xcite . this paper formalizes the notion of topic chains, introduced by @xcite and demonstrates that people omit pronouns when a certain kind of discourse salience is high. we show that this notion of salience is robust across various choices of language models, however, locality (i.e. clause recency) seems to be a key requirement. the proposed formalization leverages the idea that verbs predicated on the same storycharacter exhibit discourse coherence @xcite @xcite . figure 1 shows a literary example where the same character, the narrator, is explicitly referred to once using an explicit pronoun "wo". after that, the pronoun is dropped. the list of predicates (shown in red) applying to the narrator in examples (1) -(3) is ["draw", "lose", "draw"]. when faced with another omitted pronoun in example (4), the fact that the predicate is also "draw" supports the interpretation that the omitted element refers to the narrator. this is because "draw" is similar to the history verbs "draw" and "lose" which were predicated of this same character earlier in the discourse. in this short example, there are other entities such as "grownups" and the "boa constrictor", but their verb histories make them less plausible as candidate referents of the omitted pronoun. in this paper, we use representations from three neural language models to quantify character-verb usage continuity in a literary discourse, and calculate salience values for each of 32 possible characters at the site of each omitted pronoun. figure 2 summarizes the analytical steps of this process. our contributions are as follows: (1) we provide a numerical description of the topic chain continuity. ## related work various linguistic theories point to discourse coherence as a factor that enables or encourages pro-drop. one of these is @xcite notion of topic chain. as reviewed in @xcite , a topic chain is a sequence of clauses sharing an identical topic that occurs overtly in one of the clauses. topic chains may cross several sentences and even paragraphs @xcite . the multiclausal aspect of topic chains supports figure 2 : analysis steps adopted in this study: (a) grammatical subjects and objects of each main verb are identified via dependency parsing on the whole story discourse of the little prince (see a sentence example from we conceptualize accessibility in pu's sense as the relative salience of a story character that participates in a chain of predications. instead of focusing on named entities, we form the chain based on the verbs in the preceding discourse. ## results in this study, the following analyses are applied to the little prince discourse to explore the effect of verb continuity on the pro-drop phenomenon: (1) relevance between history and current verbs for all story characters (with three types of word embeddings applied); (2) character salience of the correct character, and ## discussion the main findings of this study are: (1) compared with overtly expressed subjects, omitted subjects have higher verb-usage continuity. it shall be noted that verb-usage continuity is not the only factor that conditions pro-drop. other factors, including nonverbal lexical information and syntactic patterns e.g. with conjunctions, also support discourse coherence @xcite . in this light, it is remarkable that one factor on its own, verbusage continuity, yields above-chance accuracy in predicting pro-drop. ## conclusion this study quantifies character-verb usage continuity as an aspect of discourse that helps comprehenders resolve omitted pronouns. omitted pronouns tend to show higher verb usage consistency compared to pronounced entities, and this effect is strengthened by clause recency. a appendix story character annotation label number of occurrence the little prince ch4_prince 676 the story teller ch1_storyteller 356 the rose ch12_rose 166 the king ch18_king 71 the fox ch28_fox 67 the planet ch11_planet 62 the lamplighter ch23_lighter 54 the sheep ch5_sheep 48 the geologist ch24_geologist 41 the grownups ch3_grownups 39 the snake ch26_snake 39 the businessman ch22_shiyejia 37 readers ch8_audience 30 the volcano ch17_volcano 22 the baobab ch9_tree 20 the drunk man ch21_drunk 18 the conceited man ch20_xurong 16 the travelers ch31_traveler 15 the seed ch10_grass 13 the explorer ch25_explorer 13 the red-faced man ch14_redface 11 the boa ch2_boa 10 the switch man ch29_switcher 10 the astronomer ch6_universescholar 7 the echo ch27_echo 5 the tiger ch15_tiger 5 the drafts ch16_wind 4 the train ch30_train 4 the merchant ch32_merchant 4 the children ch13_kids 3 the general ch19_general 3 the ruler ch7_ruler 1 table a1: the number of occurrence of each character in the annotated discourse agent patient pro-drop 422 16 non-pro-drop 2032 1329 total number 2454 1345 table a2: distribution of annotated agents and patients in the whole discourse. verb 回来 verb_id 16008 agent_character ch4 pro_drop false ch1_prev_verbs [只有, 看到, 想, 用, 画, 画, 让, 画,...] ch2_prev_verbs [咀嚼, 吞, 动弹, 消化, 消化, 开, 闭, 闭,...] ch3_prev_verbs [理解, 看, 懂, 需要, 解释, 劝, 靠, 弄,...] ch4_prev_verbs [朝, 望, 出现, 给, 像, 没有, 像, 干,...] ch5_prev_verbs [病, 需要, 像, 睡, 去, 用, 跑, 跑,...] ... ... ch30_prev_verbs [运载, 发, 往, 朝着, 开, 过] ch31_prev_verbs [寻找, 回来, 满意, 住, 追随, 追随, 睡觉, 打哈欠,...] ch32_prev_verbs [说道, 贩卖, 卖, 说] table a3: example of verb-character table. (see a translation of this table in table a4) verb come back verb_id 16008 agent_character ch4 pro_drop false ch1_prev_verbs [have, see, want, use, draw, draw, let, draw,...] ch2_prev_verbs [chew, swallow, move, digest, digest, open, close, close,...] ch3_prev_verbs [understand, see, understand, need, explain, advise, lean, play,...] ch4_prev_verbs [turn, watch, show up, give, alike, (not) have, alike, do,...] ch5_prev_verbs [sick, need, alike, sleep, go, use, run, run,...] ... ... ch30_prev_verbs [carry, send, go, turn, drive, pass] ch31_prev_verbs [look up, come back, satisfy, live, follow, follow, sleep, yawn,...] ch32_prev_verbs [speak, sell, sell, say] table a4: translation of table a3: example of verb-character table.