Discourse Representation Structure Parsing for C hinese (2023)

Abstract: Previous work has predominantly focused on monolingual English semantic parsing. We, instead, explore the feasibility of Chinese semantic parsing in the absence of labeled data for Chinese meaning representations. We describe the pipeline of automatically collecting the linearized Chinese meaning representation data for sequential-to-sequential neural networks. We further propose a test suite designed explicitly for Chinese semantic parsing, which provides fine-grained evaluation for parsing performance, where we aim to study Chinese parsing difficulties. Our experimental results show that the difficulty of Chinese semantic parsing is mainly caused by adverbs. Realizing Chinese parsing through machine translation and an English parser yields slightly lower performance than training a model directly on Chinese data.

## introduction semantic parsing is the task of transducing natural language text into semantic representations, which are expressed in logical forms underlying various grammar formalisms, such as abstract meaning representations (amr, @xcite , minimal recursion semantics @xcite , and discourse representation theory (drt, @xcite . in this work, we explore the feasibility of parsing chinese text to semantic representation based on discourse representation structures @xcite , which are meaning representations proposed from drt, a recursive first-order logic representation comprising of discourse referents (the entities introduced in the discourse) and relations between them. several neural parsers for drs have been recently developed @xcite @xcite @xcite and reached remarkable performance, but mostly focused on monolingual english or some language using the latin alphabet. meaning representations are considered to be language-neutral, and texts with the same semantics but in different languages have the same meaning representation. the literature presents several examples of parsing multilingual text by training on monolingual english semantic representations @xcite . for the reason of relatively limited amounts of labeled gold-standard multilingual meaning representation data, multilingual text parsing relies on the source of silver english meaning representation data. as long as the meanings are expressed in a language-neutral way, this is a valid approach. however, named entities aren't usually, because they can (a) have different orthography for different languages using the same alphabet (in particular for location names, e.g., berlin, berlijn, berlino, berlynas) or (b) be written with a completely different character set, as is the case for chinese. figure 1 shows a (nearly) language-neutral meaning representation for a simple english sentence. for non-english latin alphabet languages, the named entities in the text are usually consistent with english, and the meaning in the form of a graph structure of the corresponding discourse representation (discourse representation graph, drg) would be identical to these languages @xcite , as shown in figure 1 . however, it would be rather absurd to expect a semantic parser for chinese to produce meaning representations (with interlingual wordnet synsets) where proper names are anchored using the latin alphabet using english (or any other language for that matter) orthography. we need to keep this important aspect in mind when evaluating semantic parsers for languages other than english. however, for non-latin alphabet languages, such as the widely used language of chinese, is it feasible to use english meaning representation as the meaning representation of chinese? our objective is to investigate whether chinese semantic parsing can achieve the same performance as english semantic parsing while using the same amount of data. we try to investigate whether it is necessary to develop a dedicated parser for chinese, or whether it is possible to achieve a similar performance using an english parser by leveraging machine translation (mt) on chinese. we provide inexpensively acquired silver-standard chinese drs data to implement our exploration: (1) we collect chinese and english aligned texts from the parallel meaning bank (pmb, @xcite , which provides parallel multilingual corpora including corresponding english meaning representation expressed in drss. (2) we leverage giza++ @xcite to align the word-segmented chinese and english to obtain chinese-english named entity alignment pairs, the resulting named entities are used to replace the named entities in our english semantic representation. (3) we train two monolingual parsers on the two languages separately, and then provide a set of fine-grained evaluation metrics to make better comparison between parsers. we aim to answer the following questions: ## data creation in previous work, for non-english parsing tasks, the semantic representation of english is usually directly used as the semantic representation of the target language, but most of these works focus on latin languages @xcite . for non-latin languages such as chinese, named entities are not language-neutral, as illustrated in the work of @xcite , and are quite different from named entities in english texts. to design a more reasonable chinese parser, we first focus on replacing the named entities in the english semantic representation with chinese, so that the parser can parse out the chinese named entities corresponding to the text content according to different texts. to achieve our goal, we use the data of pmb, the largest parallel corpus of drs data available, as our experimental object. from the pmb, english-chinese parallel texts and drs data for english texts are collected. based on that, we propose a pipeline to obtain chinese drs for chinese text. our pipeline has three steps: (1) using tokenizers tools to segment chinese and english text data; ## conclusion given an annotated meaning bank primarily designed for english, it is feasible to develop a semantic parser for chinese by pairing the "english" meaning representation with chinese translations, reaching good results. most difficulties in chinese parsing are caused by adverbs, while the diversity of chinese verbs and adjectives also has a big impact on parsing performance. using machine translation as an alternative to approach semantic parsing for chinese yields slightly lower results. our fine-grained graph evaluation gives better insight when comparing different parsing approaches.