A Survey on Zero Pronoun Translation (2023)

Abstract: Zero pronouns (ZPs) are frequently omitted in pro-drop languages (e.g. Chinese, Hungarian, and Hindi), but should be recalled in non-pro-drop languages (e.g. English). This phenomenon has been studied extensively in machine translation (MT), as it poses a significant challenge for MT systems due to the difficulty in determining the correct antecedent for the pronoun. This survey paper highlights the major works that have been undertaken in zero pronoun translation (ZPT) after the neural revolution so that researchers can recognize the current state and future directions of this field. We provide an organization of the literature based on evolution, dataset, method, and evaluation. In addition, we compare and analyze competing models and evaluation metrics on different benchmarks. We uncover a number of insightful findings such as: 1) ZPT is in line with the development trend of large language model; 2) data limitation causes learning bias in languages and domains; 3) performance improvements are often reported on single benchmarks, but advanced methods are still far from real-world use; 4) general-purpose metrics are not reliable on nuances and complexities of ZPT, emphasizing the necessity of targeted metrics; 5) apart from commonly-cited errors, ZPs will cause risks of gender bias.

## introduction pronouns play an important role in natural language, as they enable speakers to refer to people, objects, or events without repeating the nouns that represent them. zero pronoun (zp) 1 is a complex phenomenon that appears frequently in pronoundropping (pro-drop) languages such as chinese, hungarian, and hindi. specifically, pronouns are often omitted when they can be pragmatically * longyue wang and siyou liu contributed equally to this work. or grammatically inferable from intra-and intersentential contexts @xcite . since recovery of such zps generally fails, this poses difficulties for several generation tasks, including dialogue modelling @xcite , question answering @xcite , and machine translation @xcite . when translating texts from pro-drop to non-prodrop languages (e.g. chinese⇒english), this phenomenon leads to serious problems for translation models in terms of: 1) completeness, since translation of such invisible pronouns cannot be normally reproduced; 2) correctness, because understanding the semantics of a source sentence needs to identifying and resolving the pronominal reference. figure 1 shows zp examples in three typological patterns determined by language family (detailed in appendix §a.1). taking a full-drop language for instance, the first-person subject and third-person object pronouns are omitted in hindi input while these pronouns are all compulsory in english translation. this is not a problem for human beings since we can easily recall these missing pronoun from the context. however, even a real-life mt system still fails to accurately translate zps. in response to this problem, zero pronoun translation (zpt) has been studied extensively in the mt community on three significant challenges: • dataset: there is limited availability of zpannotated parallel data, making it difficult to develop systems that can handle zp complexities. ## evolution of zero pronoun modelling considering the evolution of zp modelling, we cannot avoid discussing other related tasks. thus, we first review three typical zp tasks and conclude their essential relations and future trends. ## conclusion and future work zpt is a challenging and interesting task, which needs abilities of models on discourse-aware understanding and generation. figure 3 best illustrates the increase in scientific publications related to zp over the past few years. this paper is a literature review of existing research on zero pronoun translation, providing insights into the challenges and opportunities of this area and proposing potential directions for future research. as we look to the future, we intend to delve deeper into the challenges of zpt. our plan is to leverage large language models, which have shown great potential in dealing with complex tasks, to tackle this particular challenge @xcite @xcite . moreover, we plan to evaluate our approach on more discourseaware tasks. specifically, we aim to utilize the guofeng benchmark @xcite , which presents a comprehensive testing ground for evaluating the performance of models on a variety of discourse-level translation tasks. by doing so, we hope to gain more insights into the strengths and weaknesses of our approach, and continually refine it to achieve better performance.