Effectively resolving phonological ambiguities is crucial for robust natural language processing, as these ambiguities are pervasive in tasks ranging from speech-to-text, spelling correction, to offensive language detection.However, current Large Language Models (LLMs) frequently struggle to resolve such ambiguities.To address this challenge, we present a framework to enhances LLMs' phonological capability through a multiple-stage training approach.Our method begins with supervised fine-tuning on well-constructed datasets, including three subtask datasets designed to enhance the model's foundational phonological knowledge, along with a synthetic dataset of step-by-step reasoning chains.Following this, we apply reinforcement learning to incentivize and stabilize its reasoning.Results show that our framework enables the base model to achieve relatively comparable performance to a much larger model.Our ablation studies reveal that subtask datasets and the synthetic dataset can simultaneously impact as complementary modular enhancers to strengthen LLMs' integrated application 1 .

To investigate the impact of Multiword Expressions (MWEs) on the fine-grained performance of the state-of-the-art metrics for Machine Translation Evaluation (MTE), we conduct experiments on the WMT22 Metrics Shared Task dataset with a preliminary focus on the Chinese-to-English language pair. We further annotate 28 types of Chinese MWEs on the source texts and then examine the performance of 31 MTE metrics on groups of sentences containing different MWEs. We have 3 interesting findings: 1) Machine Translation (MT) systems tend to perform worse on most Chinese MWE categories, confirming the previous claim that MWEs are a bottleneck of MT; 2) automatic metrics tend to overrate the translation of sentences containing MWEs; 3) most neural-network-based metrics perform better than string-overlap-based metrics. It concludes that both MT systems and MTE metrics still suffer from MWEs, suggesting richer annotation of data to facilitate MWE-aware automatic MTE and MT.