Self-training has proven to be an effective approach for cross-domain tasks,
and in this study, we explore its application to cross-domain constituency
parsing. Traditional self-training methods rely on limited and potentially
low-quality raw corpora. To overcome this limitation, we propose enhancing
self-training with the large language model (LLM) to generate domain-specific
raw corpora iteratively. For the constituency parsing, we introduce grammar
rules that guide the LLM in generating raw corpora and establish criteria for
selecting pseudo instances. Our experimental results demonstrate that
self-training for constituency parsing, equipped with an LLM, outperforms
traditional methods regardless of the LLM's performance. Moreover, the
combination of grammar rules and confidence criteria for pseudo-data selection
yields the highest performance in the cross-domain constituency parsing.

通过结合大型语言模型（LLM）生成领域特定原始语料库的迭代过程，以及引入语法规则指导 LLM 生成原始语料库并建立伪实例选择标准等方法，本研究探索了自训练方法在跨领域句法分析中的应用，实验证明，自训练方法配备 LLM 在句法分析任务上胜过传统方法，且通过语法规则和置信度标准选择伪数据的组合效果最佳。

基于 LLM 增强的跨领域短语结构句法分析的自学习

LLM-enhanced Self-training for Cross-domain Constituency Parsing

Past approaches to translate a phrase in a language L1 to a language L2 using
a dictionary-based approach require grammar rules to restructure initial
translations. This paper introduces a novel method without using any grammar
rules to translate a given phrase in L1, which does not exist in the
dictionary, to L2. We require at least one L1-L2 bilingual dictionary and
n-gram data in L2. The average manual evaluation score of our translations is
4.29/5.00, which implies very high quality.

本文介绍了一种新的翻译方法，使用至少一个 L1-L2 的双语词典和 L2 的 n-gram 数据来翻译 L1 中给定的短语，无需使用任何语法规则，平均手动评估得分为 4.29/5.00，品质非常高。

使用双语词典和 n-gram 数据的短语翻译：以越南文到英文为例的实证研究

Phrase translation using a bilingual dictionary and n-gram data: A case study from Vietnamese to English

In recent years, deep neural language models have made strong progress in
various NLP tasks. This work explores one facet of the question whether
state-of-the-art NLP models exhibit elementary mechanisms known from human
cognition. The exploration is focused on a relatively primitive mechanism for
which there is a lot of evidence from various psycholinguistic experiments with
infants. The computation of "abstract sameness relations" is assumed to play an
important role in human language acquisition and processing, especially in
learning more complex grammar rules. In order to investigate this mechanism in
BERT and other pre-trained language models (PLMs), the experiment designs from
studies with infants were taken as the starting point. On this basis, we
designed experimental settings in which each element from the original studies
was mapped to a component of language models. Even though the task in our
experiments was relatively simple, the results suggest that the cognitive
faculty of computing abstract sameness relations is stronger in infants than in
all investigated PLMs.

本研究探讨了最先进的 NLP 模型是否展现了来自人类认知的基本机制，特别是它们是否能计算 “抽象的相似关系” 这一机制，结果发现人类婴儿在计算该抽象机制方面的认知能力比所有研究中的预训练语言模型都要强。