ACM Transactions on Asian and Low-Resource Language Information Processing最新文献

Current Sundanese stemmers either ignore reduplication words or define rules to handle only affixes. There is a significant amount of reduplication words in the Sundanese language. Because of that, it is impossible to achieve superior stemming precision in the Sundanese language without addressing reduplication words. This paper presents an improved stemmer for the Sundanese language, which handles affixed and reduplicated words. With a Sundanese root word list, we use a rules-based stemming technique. In our approach, all stems produced by the affixes removal or normalization processes are added to the stem list. Using a stem list can help increase stemmer accuracy by reducing stemming errors caused by affix removal sequence errors or morphological issues. The current Sundanese language stemmer, RBSS, was used as a comparison. Two datasets with 8218 unique affixed words and reduplication words were evaluated. The results show that our stemmer's strength and accuracy have improved noticeably. The use of stem list and word reduplication rules improved our stemmer's affixed type recognition and allowed us to achieve up to 99.30% accuracy.

Utterance rewriting aims to identify and supply the omitted information in human conversation, which further enables the downstream task to understand conversations more comprehensively. Recently, sequence edit methods, which leverage the overlap between two sentences, have been widely applied to narrow the search space confronted by the previous linear generation methods. However, these methods ignore the relationship between linguistic elements in the conversation, which reflects how the knowledge and thoughts are organized in human communication. In this case, although most of the content in rewritten sentences can be found in the context, we found that some connecting words expressing relationships are often missing, which results in the out-of-context problem for the previous sentence edit method. To that end, in this paper, we propose a new semantic Graph-based Incomplete Utterance Rewriting (Graph4IUR) framework, which takes the semantic graph to depict the relationship between linguistic elements and captures out-of-context words. Specifically, we adopt the Abstract Meaning Representation (AMR) [4] graph as the basic sentence-to-graph method to depict the dialogue from the graph perspective, which could well represent the high-level semantics relationships of sentences. Along this line, we further adapt the sentence editing models to rewrite without changing the sentence architecture, which brings a restriction to exploring the overlap part of the current and rewritten sentences in the IUR task. Extensive experimental results indicate that our Graph4IUR framework can effectively alleviate the out-of-context problem and improve the performance of the previous edit-based methods in the IUR task.

Over the years, social media has emerged as one of the most popular platforms where people express their views and share thoughts about various aspects. The social media content now includes a variety of components such as text, images, videos etc. One type of interest is memes, which often combine text and images. It is relevant to mention here that, social media being an unregulated platform, sometimes also has instances of discriminatory, offensive and hateful content being posted. Such content adversely affects the online well-being of the users. Therefore, it is very important to develop computational models to automatically detect such content so that appropriate corrective action can be taken. Accordingly, there have been research efforts on automatic detection of such content focused mainly on the texts. However, the fusion of multimodal data (as in memes) creates various challenges in developing computational models that can handle such data, more so in the case of low-resource languages. Among such challenges, the lack of suitable datasets for developing computational models for handling memes in low-resource languages is a major problem. This work attempts to bridge the research gap by providing a large-sized curated dataset comprising 5,054 memes in Hindi-English code-mixed language, which are manually annotated by three independent annotators. It comprises two subtasks: (i) Subtask-1 (Binary classification involving tagging a meme as misogynous or non-misogynous), and (ii) Subtask-2 (multi-label classification of memes into different categories). The data quality is evaluated by computing Krippendorff's alpha. Different computational models are then applied on the data in three settings: text-only, image-only, and multimodal models using fusion techniques. The results show that the proposed multimodal method using the fusion technique may be the preferred choice for the identification of misogyny in multimodal Internet content and that the dataset is suitable for advancing research and development in the area.

The goal of emotion detection is to find and recognise emotions in text, speech, gestures, facial expressions, and more. This paper proposes an effective multimodal emotion recognition system based on facial expressions, sentence-level text, and voice. Using public datasets, we examine face expression image classification and feature extraction. The Tri-modal fusion is used to integrate the findings and to provide the final emotion. The proposed method has been verified in classroom students, and the feelings correlate with their performance. This method categorizes students' expressions into seven emotions: happy, surprise, sad, fear, disgust, anger, and contempt. Compared to the unimodal models, the suggested multimodal network design may reach up to 65% accuracy. The proposed method can detect negative feelings such as boredom or loss of interest in the learning environment.

Scholars in the humanities heavily rely on ancient manuscripts to study history, religion, and socio-political structures of the past. Significant efforts have been devoted to digitizing these precious manuscripts using OCR technology. However, most manuscripts have been blemished over the centuries, making it unrealistic for OCR programs to accurately capture faded characters. This work presents the Transformer + Confidence Score mechanism architecture for post-processing Google’s Tibetan OCR-ed outputs. According to the Loss and Character Error Rate metrics, our Transformer + Confidence Score mechanism architecture proves superior to the Transformer, LSTM-to-LSTM, and GRU-to-GRU architectures. Our method can be adapted to any language dealing with post-processing OCR outputs.

Manchu language, a minority language of China, is of significant historical and research value. An increasing number of Manchu documents are digitized into image format for better preservation and study. Recently, many researchers focused on identifying Manchu words in digitized documents. In previous approaches, a variety of Manchu words are recognized based on visual cues. However, we notice that visual-based approaches have some obvious drawbacks. On one hand, it is difficult to distinguish between similar and distorted letters. On the other hand, portions of letters obscured by breakage and stains are hard to identify. To cope with these two challenges, we propose a visual-language framework, namely the Visual-Language framework for Manchu word Recognition (VLMR), which fuses visual and semantic information to accurately recognize Manchu words. Whenever visual information is not available, the language model can automatically associate the semantics of words. The performance of our method is further enhanced by introducing a self-knowledge distillation network. In addition, we created a new handwritten Manchu word dataset named (HMW), which contains 6,721 handwritten Manchu words. The novel approach is evaluated on WMW and HMW. The experiments show that our proposed method achieves state-of-the-art performance on both datasets.

Primary school English teaching resources play an important role in primary school English teaching. The information age requires that primary school English teaching should strengthen the use of multimedia resources and gradually realize the diversification of teaching content. Expanded reality innovation is a sort of mixture picture handling innovation, which is one of the significant innovations that would influence the improvement of fundamental schooling in the following five years. It can seamlessly output virtual objects to the real environment, which is convenient for this paper to obtain and absorb information. It can also help students to participate in exploration and cultivate their creativity and imagination. It can strengthen the cooperation between students and teachers and create various learning environments. It has an immeasurable prospect of development in the field of education. The primary school English teaching resources based on augmented reality create a realistic learning situation from two-dimensional plane to three-dimensional three-dimensional display, and enrich the presentation of primary school English teaching content. It can stimulate students’ interest in learning English and promote the transformation of English teaching methods. It is a useful attempt in the field of education. This paper made statistics on the test results of the experimental class and the control class. Most of the scores of the experimental group were between 71 and 100, a total of 27, accounting for 67.5%. The score distribution of the control class was relatively balanced, with the highest number between 61-70, and the number was 10, accounting for 25%. Therefore, it can be seen that hybrid image processing technology is important for interactive English teaching.

Social media is a widely used platform that includes a vast amount of user-generated content, allowing the extraction of information about users’ thoughts from texts. Individuals freely express their thoughts on these platforms, often without constraints, even if the content is offensive or contains hate speech. The identification and removal of offensive content from social media are imperative to prevent individuals or groups from becoming targets of harmful language. Despite extensive research on offensive content detection, addressing this challenge in code-mixed languages remains unsolved, characterised by issues such as imbalanced datasets and limited data sources. Most previous studies on detecting offensive content in these languages focus on creating datasets and applying deep neural networks, such as Recurrent Neural Networks (RNNs), or pre-trained language models (PLMs) such as BERT and its variations. Given the low-resource nature and imbalanced dataset issues inherent in these languages, this study delves into the efficacy of the syntax-aware BERT model with continual pre-training for the accurate identification of offensive content and proposes a framework called Cont-Syntax-BERT by combining continual learning with continual pre-training. Comprehensive experimental results demonstrate that the proposed Cont-Syntax-BERT framework outperforms state-of-the-art approaches. Notably, this framework addresses the challenges posed by code-mixed languages, as evidenced by its proficiency on the DravidianCodeMix [10,19] and HASOC 2109 [37] datasets. These results demonstrate the adaptability of the proposed framework in effectively addressing the challenges of code-mixed languages.

Time-sensitive question answering is to answer questions limited to certain timestamps based on the given long document, which mixes abundant temporal events with an explicit or implicit timestamp. While existing models make great progress in answering time-sensitive questions, their performance degrades dramatically when a long distance separates the correct answer from the timestamp mentioned in the question. In this paper, we propose a Context-enhanced Adaptive Graph network (CoAG) to capture long-distance dependencies between sentences within the extracted question-related episodes. Specifically, we propose a time-aware episode extraction module that obtains question-related context based on timestamps in the question and document. As the involvement of episodes confuses sentences with adjacent timestamps, an adaptive message passing mechanism is designed to capture and transfer inter-sentence differences. In addition, we present a hybrid text encoder to highlight question-related context built on global information. Experimental results show that CoAG significantly improves compared to state-of-the-art models on five benchmarks. Moreover, our model has a noticeable advantage in solving long-distance time-sensitive questions, improving the EM scores by 2.03% to 6.04% on TimeQA-Hard.

Predicting information cascades holds significant practical implications, including applications in public opinion analysis, rumor control, and product recommendation. Existing approaches have generally overlooked the significance of semantic topics in information cascades or disregarded the dissemination relations. Such models are inadequate in capturing the intricate diffusion process within an information network inundated with diverse topics. To address such problems, we propose a neural-based model (named ICP-TMAN) using Topic-Aware Masked Attentive Network for Information Cascade Prediction to predict the next infected node of an information cascade. First, we encode the topical text into user representation to perceive the user-topic dependency. Next, we employ a masked attentive network to devise the diffusion context to capture the user-context dependency. Finally, we exploit a deep attention mechanism to model historical infected nodes for user embedding enhancement to capture user-history dependency. The results of extensive experiments conducted on three real-world datasets demonstrate the superiority of ICP-TMAN over existing state-of-the-art approaches.