In the field of computational linguistics, addressing machine translation (MT) challenges for low-resource languages remains crucial, as these languages often lack extensive data compared to high-resource languages. General large language models (LLMs), such as GPT-4 and Llama, primarily trained on monolingual corpora, face significant challenges in translating low-resource languages, often resulting in subpar translation quality. This study introduces Language-Specific Fine-Tuning with Low-rank adaptation (LSFTL), a method that enhances translation for low-resource languages by optimizing the multi-head attention and feed-forward networks of Transformer layers through low-rank matrix adaptation. LSFTL preserves the majority of the model parameters while selectively fine-tuning key components, thereby maintaining stability and enhancing translation quality. Experiments on non-English centered low-resource Asian languages demonstrated that LSFTL improved COMET scores by 1-3 points compared to specialized multilingual machine translation models.Additionally, LSFTL’s parameter-efficient approach allows smaller models to achieve performance comparable to their larger counterparts, highlighting its significance in making machine translation systems more accessible and effective for low-resource languages.

This paper presents the development of the speech, text and pronunciation dictionary resources required to build a large vocabulary speech recognizer for the Malay language. This project is a collaboration project among three universities: USM, MMU from Malaysia and NTU from Singapore. The Malay speech corpus consists of read speech (speaker independent/ dependent and accent independent/ dependent) and broadcast news. To date, 90 speakers have been recorded which is equal to a total of nearly 70 hours of read speech, and 10 hours of broadcast news from local TV stations in Malaysia was transcribed. The text corpus consists of 700Mbytes of data extracted from Malaysia's local news Web pages from 1998-2008 and a rule based G2P tool is develop to generate the pronunciation dictionary.

The explanation of legal judgments is crucial for the transparency, fairness, and trustworthiness, aiming to provide rationales for decision-making. While previous works have focused on improving the accuracy of legal judgment prediction, the lack of explainability seriously limits the practical application of these methods. Many researchers have dedicated effort to extracting or generating rationales as explanations for legal judgments, ignoring the factual consistency of these rationales. Inconsistencies between rationales and fact descriptions severely impact their applicability. To address these issues, we investigate Event Chain – ordered sequences of events related to criminal behavior – as an intermediate representation in the fact descriptions, to enhance the representation of causal relationships among events, and focus on crucial events within the fact description. Specifically, we propose a multi-task learning approach, dubbed LegalMind, that introduces Event Chain as an auxiliary task and jointly models Event Chain and rationale in a unified decoder, to improve factual consistency in rationale generation. The experiment results show that our model outperforms the state-of-the-art methods, achieving improvements of 7.65% and 6.65% in AVG-BLEU on the CJO dataset and LAIC2021 dataset respectively compared to BART-C3VG. Furthermore, in comparison to BART-C3VG, our model demonstrated superior performance with increases of 6.17% and 8.33% in factual consistency on the CJO and LAIC2021 datasets, respectively.

The extracted product aspects (like “battery life”, “zoom”) from online customer reviews are dissimilar in their significances, some of these aspects have a great influence on the potential customer's decision likewise on the businesses' strategies for product enhancements. Supporting the probable customers with a list of the most representative product aspects will assist their purchasing decision and facilitate the comparative process among the presented products. For the firms, identifying critical product aspects creates a new perspective of product manufacturing and marketing strategies to be competitive and innovative. However the manual identification of the most representative product aspects from the huge amounts of the extracted product aspects in online reviews is a tedious and time-consuming task. Thus, ranking the extracted aspects becomes a necessity to identify the important product aspects mentioned in the customer reviews. The purpose of this study is to formulate the product aspect ranking problem as a decision making process using Multi-Criteria Decision Making (MCDM). In response, a comparative analysis between two different MCDM ranking approaches, namely; TOPSIS and VIKOR has been conducted to investigate their performances in prioritizing the most important product aspects in customer reviews. The experimental results on different product reviews demonstrate the effectiveness of these two methods in prioritizing the genuine product aspects in customer feedback.

In this paper, we propose a novel approach to automatic recognition of code-switching speech. The proposed method consists of two phases: automatic speech recognition, and rescoring. The framework uses parallel automatic speech recognizers for speech recognition. The lattices produced are subsequently joined and rescored to estimate the most probable word sequence. Experiment shows that the proposed approach reduction of more than 5% WER, when tested on English/Malay code switching speech. In addition, the framework has shown to be very robust. Besides, we also propose an acoustic model adaptation approach known as hybrid approach of interpolation and merging to cross adapt acoustic models of different languages to recognize code switching speech. The adapted acoustic models show reduction in WER, when they are used for code switching speech recognition.