Decompose, Fuse and Generate: A Formation-Informed Method for C hinese Definition Generation (2021)

Abstract: In this paper, we tackle the task of Definition Generation (DG) in Chinese, which aims at automatically generating a definition for a word. Most existing methods take the source word as an indecomposable semantic unit. However, in parataxis languages like Chinese, word meanings can be composed using the word formation process, where a word (“桃花”, peach-blossom) is formed by formation components (“桃”, peach; “花”, flower) using a formation rule (Modifier-Head). Inspired by this process, we propose to enhance DG with word formation features. We build a formation-informed dataset, and propose a model DeFT, which Decomposes words into formation features, dynamically Fuses different features through a gating mechanism, and generaTes word definitions. Experimental results show that our method is both effective and robust.

## introduction definition generation (dg) aims at automatically generating an explanatory text for a word. this task is of practical importance to assist dictionary construction, especially in highly productive languages like chinese @xcite . most existing methods take the source word as an indecomposable lexico-semantic unit, using features like word embedding @xcite and context @xcite . recently, @xcite and @xcite achieve improvement by decomposing the word meaning into different semantic components. in decomposing the word meaning, the word formation process is an intuitive and informative way that has not been explored in dg by far. for parataxis languages like chinese, a word is formed by formation components, i.e., morphemes, and 2 : modifier-head adverb-verb (white flower.) (vainly spend.) ## word formation process in chinese it is linguistically motivated to explore the word formation process to better understand words. instead of combining roots and affixes, chinese words are formed by characters in a parataxis way @xcite . here, we introduce two formation features and construct a formation-informed dataset. ## conclusion in this paper, we propose to use formation features to enhance dg. we build a formation-informed dataset and design a model deft, which decomposes words into formation features and fuses features via a gating mechanism. experimental results show that our method is both effective and robust. 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. 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).