While large language models (LLMs) have shown promise in translating extremely lowresource languages using resources like dictionaries, the effectiveness of grammar books remains debated.This paper investigates the role of grammar books in translating extremely low-resource languages by decomposing it into two key steps: grammar rule retrieval and application.To facilitate the study, we introduce ZHUANGRULES, a modularized dataset of grammar rules and their corresponding test sentences.Our analysis reveals that rule retrieval constitutes a primary bottleneck in grammarbased translation.Moreover, although LLMs can apply simple rules for translation when explicitly provided, they encounter difficulties in handling more complex rules.To address these challenges, we propose to represent grammar rules as code functions, motivated by their similarities in structures and the benefit of code in facilitating LLM reasoning.Our experiments show that using code rules significantly boosts both rule retrieval and application, ultimately resulting in a 13.1% BLEU improvement in translation.

Quzhe Huang, Zhenwei An, Nan Zhuang, Mingxu Tao, Chen Zhang, Yang Jin, Kun Xu, Kun Xu, Liwei Chen, Songfang Huang, Yansong Feng. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Current large language models demonstrate deficiencies in understanding low-resource languages, particularly the minority languages in China.This limitation stems from the scarcity of available pre-training data.To address this accessibility challenge, we present MC 2 , a Multilingual Corpus of Minority Languages in China, which is the largest open-source corpus of its kind so far.MC 2 includes four underrepresented languages: Tibetan, Uyghur, Kazakh, and Mongolian.Notably, we focus on the less common writing systems of Kazakh and Mongolian, i.e., Kazakh Arabic script and traditional Mongolian script, respectively, which have been long neglected in previous corpus construction efforts.Recognizing the prevalence of language contamination within existing corpora, we adopt a quality-centric solution for collecting MC 2 , prioritizing accuracy while enhancing diversity.Furthermore, we underscore the importance of attending to the multiplicity of writing systems, which is closely related to the cultural awareness of the resulting models.The MC 2 corpus and related models are made public to the community 1 .

FEVEROUS is a fact extraction and verification task that requires systems to extract evidence of both sentences and table cells from a Wikipedia dump, then predict the veracity of the given claim accordingly.Existing works extract evidence in the two formats separately, ignoring potential connections between them.In this paper, we propose a Unified Evidence Extraction model (UNIFEE), which uses a mixed evidence graph to extract the evidence in both formats.With the carefully-designed unified evidence graph, UNIFEE allows evidence interactions among all candidates in both formats at similar granularity.Experiments show that, with information aggregated from related evidence candidates in the fusion graph, UNIFEE can make better decisions about which evidence should be kept, especially for claims requiring multi-hop reasoning or a combination of tables and texts.Thus it outperforms all previous evidence extraction methods and brings significant improvement in the subsequent claim verification step.