The performance of multilingual language models (MLLMs) is notably inferior for low-resource languages (LRL) compared to high-resource ones, primarily due to the limited available corpus during the pre-training phase. This inadequacy stems from the under-representation of low-resource language words in the subword vocabularies of MLLMs, leading to their misidentification as unknown or incorrectly concatenated subwords. Previous approaches are based on frequency sorting to select words for augmenting vocabularies. However, these methods overlook the fundamental disparities between model representation distributions and frequency distributions. To address this gap, we introduce a novel Entropy-Consistency Word Selection (ECWS) method, which integrates semantic and frequency metrics for vocabulary augmentation. Our results indicate an improvement in performance, supporting our approach as a viable means to enrich vocabularies inadequately represented in current MLLMs.

Chinese Semantic Error Recognition (CSER) has always been a weak link in Chinese language processing due to the complexity and obscureness of Chinese semantics. Existing research has gradually focused on leveraging pre-trained models to perform CSER. Although some researchers have attempted to integrate syntax information into the pre-trained language model, it requires training the models from scratch, which is time-consuming and laborious. Furthermore, despite the existence of datasets for CSER, the constrained size of these datasets impairs the performance of the models. Thus, in order to address the difficulty posed by a limited sample set and the need of annotating samples with semantic-level errors, we propose a Pseudo-label Data Construction method for CSER (PDC-CSER), generating pseudo-labels for augmented samples based on perplexity and model respectively, which overcomes the difficulty of constructing pseudo-label data containing semantic-level errors and ensures the quality of pseudo-labels. Moreover, we propose a CSER method with the Dependency Syntactic Attention mechanism (CSER-DSA) to explicitly infuse dependency syntactic information only in the fine-tuning stage, achieving robust performance, and simultaneously reducing substantial computing power and time cost. Results demonstrate that the pseudo-label technology PDC-CSER and the semantic error recognition method CSER-DSA surpass the existing models

“空间语义理解任务致力于使语言模型能够准确解析和理解文本中描述的物体间的空间方位关系,这一能力对于深入理解自然语言并支持复杂的空间推理至关重要。本文聚焦于探索大模型的上下文学习策略在空间语义理解任务上的有效性,提出了一种基于选项相似度与空间语义理解能力相似度的样本选择策略。本文将上下文学习与高效微调融合对开源模型进行微调,以提高大模型的空间语义理解能力。此外,本文尝试结合开源模型和闭源模型的能力处理不同类型的样本。实验结果显示,本文所采用的策略有效地提高了大模型在空间语义理解任务上的性能。”

Recently, the field of language acquisition (LA) has significantly benefited from natural language processing technologies. A crucial task in LA involves tracking the evolution of language learners’ competence, namely language development assessment (LDA). However, the majority of LDA research focuses on high-resource languages, with limited attention directed toward low-resource languages. Moreover, existing methodologies primarily depend on linguistic rules and language characteristics, with a limited exploration of exploiting pre-trained language models (PLMs) for LDA. In this paper, we construct the IndoCL corpus (Indonesian Corpus of L2 Learners), which comprises compositions written by undergraduate students majoring in Indonesian language. Moreover, we propose a model for LDA tasks, which automatically extracts language-independent features, relieving laborious computation and reliance on specific language. The proposed model uses sequential information attention and similarity representation learning to capture the differences and common information from the first-written and second-written essays, respectively. It has demonstrated remarkable performance on both our self-constructed corpus and publicly available corpora. Our work could serve as a novel benchmark for Indonesian LDA tasks. We also explore the feasibility of using existing large-scale language models (LLMs) for LDA tasks. The results show significant potential for improving LLM performance in LDA tasks.

Transliteration is an important task in natural language processing (NLP) which aims to convert a name in the source language to the target language without changing its pronunciation. Particularly, transliteration from English to Arabic is highly needed in many applications, especially in countries (e.g., United Arab Emirates (UAE)) whose most citizens are foreigners but the official language is Arabic. In such a task-oriented scenario, namely transliterating the English names to the corresponding Arabic ones, the performance of the transliteration model is highly important. However, most existing neural approaches mainly apply a universal transliteration model with advanced encoders and decoders to the task, where limited attention is paid to leveraging the phonemic association between English and Arabic to further improve model performance. In this paper, we focus on transliteration of people’s names from English to Arabic for the general public. In doing so, we collect a corpus named EANames by extracting high quality name pairs from online resources which better represent the names in the general public than linked Wikipedia entries that are always names of famous people). We propose a model for English-Arabic transliteration, where a memory module modeling the phonemic association between English and Arabic is used to guide the transliteration process. We run experiments on the collected data and the results demonstrate the effectiveness of our approach for English-Arabic transliteration.

Trained on the large corpus, pre-trained language models (PLMs) can capture different levels of concepts in context and hence generate universal language representations. They can benefit from multiple downstream natural language processing (NLP) tasks. Although PTMs have been widely used in most NLP applications, especially for high-resource languages such as English, it is under-represented in Lao NLP research. Previous work on Lao has been hampered by the lack of annotated datasets and the sparsity of language resources. In this work, we construct a text classification dataset to alleviate the resource-scarce situation of the Lao language. In addition, we present the first transformer-based PTMs for Lao with four versions: BERT-Small , BERT-Base , ELECTRA-Small , and ELECTRA-Base . Furthermore, we evaluate them on two downstream tasks: part-of-speech (POS) tagging and text classification. Experiments demonstrate the effectiveness of our Lao models. We release our models and datasets to the community, hoping to facilitate the future development of Lao NLP applications.

In this paper, we report the solution of the team BERT 4EVER for the LT-EDI-2022 shared task2: Homophobia/Transphobia Detection in social media comments in ACL 2022, which aims to classify Youtube comments into one of the following categories: no,moderate, or severe depression. We model the problem as a text classification task and a text generation task and respectively propose two different models for the tasks. To combine the knowledge learned from these two different models, we softly fuse the predicted probabilities of the models above and then select the label with the highest probability as the final output. In addition, multiple augmentation strategies are leveraged to improve the model generalization capability, such as back translation and adversarial training. Experimental results demonstrate the effectiveness of the proposed models and two augmented strategies.