The article examines the so-called “Critical Buddhism,” which is a specific trend in Japanese Buddhist philosophy that has become widespread since the 1980s and has questioned the “truth” of both Japanese Buddhism itself and the entire Far Eastern Buddhist tradition due to their gross discrepancies with the Indian roots of the teaching. The main ideologists of the movement were Matsumoto Shirō and Hakamaya Noriaki, professors at Komazawa University and followers of the Sōtō school. The author analyzes the social aspect of their criticism, which caused the greatest resonance at the initial stages of development of “Critical Buddhism.” Arguing that Japanese Buddhism had actually created and supported the oppression of the Burakumin (Japanese “untouchables”) by imposing the ideas of “Buddha-nature” and “original enlightenment” on the population over the centuries, Hakamaya and Matsumoto accused the teaching itself and its adherents (including themselves) of departing from the true precepts of the Buddha, the key one of which was the renunciation of one’s own self for the sake of another. The spread of doctrines of imaginary equality, according to Hakamaya and Matsumoto, reinforced the existing problems in society, depriving believers of the need to make any unpleasant moral choices, since, if “Buddha-nature” was present in everything, then social discrimination was also a state of affairs pleasing to Buddha himself. By analyzing academic studies of the ideologists of “Critical Buddhism,” it is shown that they did not seek any compromise option for further coexistence of Buddhism in Japan with the doctrines of hongaku and “Buddha-nature” due to their initially heretical nature and the severity of the harm inflicted on society. It is also shown how Japanese feminism actively responded to the agenda of “Critical Buddhism,” adding gender discrimination, which had not gone away, to caste discrimination in Buddhism. The author concludes that this movement, although unable to bring about fundamental changes in Buddhology and society, was not in vain, since it once again highlighted the importance of the ethical aspect of Buddhism and the public demand for reforming Buddhism in Japan.
Envy, Justice, and Democracy
嫉妬・正義・民主主義
Written by Yamamoto Kei
Translation by Robin Herzeg, Matsumoto Momo, and Manuel Steiner
This paper is framed within the broader context of the analytic-continental divide in political philosophy, a topic that has recently gained unexpected amounts of attention (Chin/Thomassen 2016). On the one hand, the questions posed, methodological discussions put forth, and arguments made in John Rawl’s “A Theory of Justice”, a seminal work in the analytic current of political philosophy, challenged conventional debates in mainstream conceptualizations of political thought and political philosophy (continental or not), especially in Japan. Rawls' work prompted methodological self-reflection within political science and a large-scale repositioning with or against the analytic tradition[1]. On the other hand, in continental political philosophy, there has been a growing interest toward a political science that considers the role of feelings and emotions and critically engages with the rationalist paradigm. Notably, there is Chantal Mouffe's critique of deliberative democracy based on the political significance of passion, Martha Nussbaum's more recent discussion on the role of emotions in politics (Nussbaum 2013), as well as Yannis Stavrakakis' attempt to introduce the concept of jouissance (enjoyment) into political theory from a standpoint of Lacanian psychoanalysis (Stavrakakis 2011). Currently, efforts to re-evaluate the relationship between the two (or possibly more) independently developed schools of thought and to establish a framework for dialogue between them are only in their initial stages.
Between these two traditions, so to speak, the question of “envy”[2] appears vaguely as a middle ground. Hence, this essay is not only an attempt to examine a concrete dimension of the “politics of emotion,” which has often been discussed in very general terms, but it is also a suitable topic for both analytic and continental political philosophy, to make them sit down at the same table, so to speak. In addition, this emotion might have decisive implications for the consideration of our current democratic moment, and the recent dominance of populism in Europe and the United States may be examined differently if we can gain a new perspective on this issue. Further, the emotion of envy has not yet garnered an extensive amount of attention within the field of political science. In fact, the situation has not changed much since the time Helmut Schoeck remarked in his extensive book on envy that „[i]t is most curious to note that at the beginning of this century, authors began to show an increasing tendency, above all in the social sciences and moral philosophy, to repress the concept of envy” (Schoeck 1987, 12). Or, in the words of Joan Copjec, “social and political theorists […] have given no serious consideration to this vice and its injurious contributions to social relations” (Copjec 2002, 162). The significant exception to this fallacy, on which Copjec is also building, is John Rawls. This paper aims to examine Rawls' arguments on envy, testing them against critiques stemming primarily from outside the analytic tradition, and to explore points of friction between these arguments. Given its ambiguity, the role played by this emotion in a democratic society is also to be examined.
The article is devoted to the problems of Russian-Japanese relations on the Chinese Eastern Railway at the initial stage of its history. Workers from Japan were not invited to build the road, but the Japanese were already among the first settlers in the city of Harbin, founded in 1898 by the Chinese Eastern Railway Society. After a short break caused by the Russo-Japanese War, bilateral relations were quickly restored. In 1906, as the Russian troops withdrew from Manchuria, the Japanese began to return to the Chinese Eastern Railway. Since 1907, Japanese officials, entrepreneurs, cultural and sports figures from Japan began to take an active part in the sociopolitical, economic, and cultural life of the city of Harbin. It was here that the first “Russian-Japanese Society” was created. The Japanese in Harbin were mainly employed in such areas as trade, medicine, entertainment, and domestic service.An important component of bilateral cooperation was cooperation in the railway sector. Due to the tradition and the inaccessibility of sources, the history of the Japanese population of Harbin is poorly studied in Russian historiography. The purpose of the research is to restore the historical picture and identify the problems of the Japanese presence in Harbin and the Russian- Japanese interaction on the Chinese Eastern Railway in the period between the Russo-Japanese War and the First World War. The study was carried out on the basis of materials from the periodicals of Harbin, with the involvementThe information and analytical materials published in the newspapers Harbinskii Vestnik (Harbin Bulletin), Harbin, Novaya Zhizn’ (New Life), and Man’chzhurskii Kur’er (Manchurian Courier), especially advertising, allow one to get an idea of the composition of the Japanese community, the occupations of the Japanese and some problems of Russian-Japanese relations in 1906–1914. The study of development of the Chinese Eastern Railway by the Japanese, their coexistence and cooperation with the Russians in Harbin will expand knowledge of the history of the Chinese Eastern Railway and achieve a more complete and objective picture of the history of Russian-Japanese relations in the 20th century.
The article deals with the genesis, semantics, and functions of the dragon image in Japanese culture. The relevance of the study is due to the increased attention of researchers to the basic values of local cultures, issues of symbolism, inextricably linked to the problems of national self-identification. The methodological basis of the study is the structural-semiotic approach, which was used to analyze the value content of the dragon image, the descriptive-analytical method, and the method of cognitive interpretation of the semantics of linguistic means verbalizing the dragon image in the Japanese language. In contrast to the Western tradition, in the culture of the peoples of East Asia, a dragon is a revered and significant symbol of power, strength, and authority.Stories about dragons are found in ancient texts of both Hinduism and Buddhism. It is established that the formation and evolution of the dragon cult in Japan was influenced by the mythical Chinese dragons, Indian Naga snakes, and the belief in dragons as deities of the water element. The author examines the genesis and evolution of the dragon image in different historical epochs, the influence of cultural-historical, natural, and religious factors on its transformation. It is shown that, in medieval Japan, the dragon was considered the protector of Buddhism, personifying strength, wisdom, prosperity, good luck, and images of these mythical creatures became an organic element of Buddhist culture. Particular attention is paid to the analysis of the image of the dragon as a sign of the Chinese zodiacal calendar, the representations of dragons in Japanese mythology, fairy tales and legends, in Hitachi Fudoki, Kojiki, Nihon Shoki. In the mythological picture of the world of the Japanese, the dragon is ambivalent and has both positive and negative features.It is revealed that the image of the dragon occupies an important place in Japanese traditional culture, painting, architecture, arts and crafts, calendar holidays, is widely represented in proverbs and sayings, set phrases and idioms. The reference to Japanese phraseology allowed to expand the base of the study and to reveal the totality of ideas about the dragon in the worldview of native speakers of the Japanese language. The author concludes that, nowadays, the image of the dragon in Japan has lost its sacral significance and is mainly used as tribute to tradition.
Speech Language Models (SLMs) have made significant progress in spoken language understanding. Yet it remains unclear whether they can fully perceive non lexical vocal cues alongside spoken words, and respond with empathy that aligns with both emotional and contextual factors. Existing benchmarks typically evaluate linguistic, acoustic, reasoning, or dialogue abilities in isolation, overlooking the integration of these skills that is crucial for human-like, emotionally intelligent conversation. We present EchoMind, the first interrelated, multi-level benchmark that simulates the cognitive process of empathetic dialogue through sequential, context-linked tasks: spoken-content understanding, vocal-cue perception, integrated reasoning, and response generation. All tasks share identical and semantically neutral scripts that are free of explicit emotional or contextual cues, and controlled variations in vocal style are used to test the effect of delivery independent of the transcript. EchoMind is grounded in an empathy-oriented framework spanning 3 coarse and 12 fine-grained dimensions, encompassing 39 vocal attributes, and evaluated using both objective and subjective metrics. Testing 12 advanced SLMs reveals that even state-of-the-art models struggle with high-expressive vocal cues, limiting empathetic response quality. Analyses of prompt strength, speech source, and ideal vocal cue recognition reveal persistent weaknesses in instruction-following, resilience to natural speech variability, and effective use of vocal cues for empathy. These results underscore the need for SLMs that integrate linguistic content with diverse vocal cues to achieve truly empathetic conversational ability.
Universal phoneme recognition typically requires analyzing long speech segments and language-specific patterns. Many speech processing tasks require pure phoneme representations free from contextual influence, which motivated our development of CUPE - a lightweight model that captures key phoneme features in just 120 milliseconds, about one phoneme's length. CUPE processes short, fixed-width windows independently and, despite fewer parameters than current approaches, achieves competitive cross-lingual performance by learning fundamental acoustic patterns common to all languages. Our extensive evaluation through supervised and self-supervised training on diverse languages, including zero-shot tests on the UCLA Phonetic Corpus, demonstrates strong cross-lingual generalization and reveals that effective universal speech processing is possible through modeling basic acoustic patterns within phoneme-length windows.
Toheeb Aduramomi Jimoh, Tabea De Wille, Nikola S. Nikolov
Natural Language Processing (NLP) is becoming a dominant subset of artificial intelligence as the need to help machines understand human language looks indispensable. Several NLP applications are ubiquitous, partly due to the myriad of datasets being churned out daily through mediums like social networking sites. However, the growing development has not been evident in most African languages due to the persisting resource limitations, among other issues. Yorùbá language, a tonal and morphologically rich African language, suffers a similar fate, resulting in limited NLP usage. To encourage further research towards improving this situation, this systematic literature review aims to comprehensively analyse studies addressing NLP development for Yorùbá, identifying challenges, resources, techniques, and applications. A well-defined search string from a structured protocol was employed to search, select, and analyse 105 primary studies between 2014 and 2024 from reputable databases. The review highlights the scarcity of annotated corpora, the limited availability of pre-trained language models, and linguistic challenges like tonal complexity and diacritic dependency as significant obstacles. It also revealed the prominent techniques, including rule-based methods, among others. The findings reveal a growing body of multilingual and monolingual resources, even though the field is constrained by socio-cultural factors such as code-switching and the desertion of language for digital usage. This review synthesises existing research, providing a foundation for advancing NLP for Yorùbá and in African languages generally. It aims to guide future research by identifying gaps and opportunities, thereby contributing to the broader inclusion of Yorùbá and other under-resourced African languages in global NLP advancements.
The fusion of language and vision in large vision-language models (LVLMs) has revolutionized deep learning-based object detection by enhancing adaptability, contextual reasoning, and generalization beyond traditional architectures. This in-depth review presents a structured exploration of the state-of-the-art in LVLMs, systematically organized through a three-step research review process. First, we discuss the functioning of vision language models (VLMs) for object detection, describing how these models harness natural language processing (NLP) and computer vision (CV) techniques to revolutionize object detection and localization. We then explain the architectural innovations, training paradigms, and output flexibility of recent LVLMs for object detection, highlighting how they achieve advanced contextual understanding for object detection. The review thoroughly examines the approaches used in integration of visual and textual information, demonstrating the progress made in object detection using VLMs that facilitate more sophisticated object detection and localization strategies. This review presents comprehensive visualizations demonstrating LVLMs' effectiveness in diverse scenarios including localization and segmentation, and then compares their real-time performance, adaptability, and complexity to traditional deep learning systems. Based on the review, its is expected that LVLMs will soon meet or surpass the performance of conventional methods in object detection. The review also identifies a few major limitations of the current LVLM modes, proposes solutions to address those challenges, and presents a clear roadmap for the future advancement in this field. We conclude, based on this study, that the recent advancement in LVLMs have made and will continue to make a transformative impact on object detection and robotic applications in the future.
Sign language serves as the primary meaning of communication for the deaf-mute community. Different from spoken language, it commonly conveys information by the collaboration of manual features, i.e., hand gestures and body movements, and non-manual features, i.e., facial expressions and mouth cues. To facilitate communication between the deaf-mute and hearing people, a series of sign language understanding (SLU) tasks have been studied in recent years, including isolated/continuous sign language recognition (ISLR/CSLR), gloss-free sign language translation (GF-SLT) and sign language retrieval (SL-RT). Sign language recognition and translation aims to understand the semantic meaning conveyed by sign languages from gloss-level and sentence-level, respectively. In contrast, SL-RT focuses on retrieving sign videos or corresponding texts from a closed-set under the query-by-example search paradigm. These tasks investigate sign language topics from diverse perspectives and raise challenges in learning effective representation of sign language videos. To advance the development of sign language understanding, exploring a generalized model that is applicable across various SLU tasks is a profound research direction.
It is possible to speak about the connection of the Buddhist and Taoist traditions in Japan at different levels: everyday, ritual, philosophical, and others, including the level of word usage in texts that are far from terminological accuracy and do not belong to any of the scholarly traditions. Such are collections of setsuwa didactic tales. In the largest of them, Konjaku monogatari-shū (1120s), there are a number of stories about Taoists (dōshi), but the concept of “immortal” (sen, sennin), important to Taoism, is more common and has a wider range of meanings. Following the Chinese translators of Indian Buddhist texts, the narrators in tales about India call the Indian sages who lived before Buddha sennin. In the tales about China, in the disputes between Buddhists and Taoists, the word “Great Immortal,” daisen, refers to Buddha himself, who taught not about prolonging life, but about complete liberation from the cycle of births and deaths. At the same time, the teachings of Chuang Tzu are spoken of with great reverence. In the very first tale about Japan, Taoists appear as former-life interlocutors of the Japanese prince Shōtoku. Immortals live in the mountains of Japan, many of them belong to the number of ascetics of the Lotus Sūtra, which teaches about the eternal life of the Buddha. If we compare all these tales with stories from the collection Honchō Shinsen-den (turn of 11th–12th centuries), it is clear that the concept of immortal in both texts implies a departure from the world and overcoming suffering inherent in human life, the acquisition of miraculous abilities and their use for the benefit of people; whatever way it is achieved, it sets a good example, and therefore the search for immortals, whether in the mountains or in books, constitutes a useful experience from the Buddhist point of view.
This research is motivated by the characteristics of the Japanese language which are owned by languages from other countries, namely variations in the gender differences of the speakers. Variations in the gender differences of speakers in Japanese are divided into two, namely danseigo (male language variety) and joseigo (female language variety). The purpose of this language variation research is to find out what kinds of languages are used by men and women in a data source, namely the Isshuukan Furenzu film. The method used is descriptive method with a qualitative approach. Data collection was carried out using note-taking techniques. The results of this study found linguistic aspects of the variety of languages based on gender, namely ninshou daimeishi, shuujoshi, and kandoushi. The ninshou daimeishi found were watashi, watashitachi, ore, oretachi, anata, omae, anta, aisu, aitsura, and kanojo. Shuujosi found include zo, ze, sa, wa, wayone, no, noyo, deshou, kashira, yo, and no. Kandoushi found in the form of maa and hora.
Large Vision-Language Models (LVLMs) offer remarkable benefits for a variety of vision-language tasks. However, a challenge hindering their application in real-world scenarios, particularly regarding safety, robustness, and reliability, is their constrained semantic grounding ability, which pertains to connecting language to the physical-world entities or concepts referenced in images. Therefore, a crucial need arises for a comprehensive study to assess the semantic grounding ability of widely used LVLMs. Despite the significance, sufficient investigation in this direction is currently lacking. Our work bridges this gap by designing a pipeline for generating large-scale evaluation datasets covering fine-grained semantic information, such as color, number, material, etc., along with a thorough assessment of seven popular LVLMs' semantic grounding ability. Results highlight prevalent misgrounding across various aspects and degrees. To address this issue, we propose a data-centric enhancement method that aims to improve LVLMs' semantic grounding ability through multimodal instruction tuning on fine-grained conversations. Experiments on enhanced LVLMs demonstrate notable improvements in addressing misgrounding issues.
Realizing the recent advances in Natural Language Processing (NLP) to the legal sector poses challenging problems such as extremely long sequence lengths, specialized vocabulary that is usually only understood by legal professionals, and high amounts of data imbalance. The recent surge of Large Language Models (LLMs) has begun to provide new opportunities to apply NLP in the legal domain due to their ability to handle lengthy, complex sequences. Moreover, the emergence of domain-specific LLMs has displayed extremely promising results on various tasks. In this study, we aim to quantify how general LLMs perform in comparison to legal-domain models (be it an LLM or otherwise). Specifically, we compare the zero-shot performance of three general-purpose LLMs (ChatGPT-20b, LLaMA-2-70b, and Falcon-180b) on the LEDGAR subset of the LexGLUE benchmark for contract provision classification. Although the LLMs were not explicitly trained on legal data, we observe that they are still able to classify the theme correctly in most cases. However, we find that their mic-F1/mac-F1 performance is up to 19.2/26.8\% lesser than smaller models fine-tuned on the legal domain, thus underscoring the need for more powerful legal-domain LLMs.
Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks. However, the procedure to optimizing the mixing of instruction datasets for LLM fine-tuning is still poorly understood. This study categorizes instructions into three primary types: NLP downstream tasks, coding, and general chat. We explore the effects of instruction tuning on different combinations of datasets on LLM performance, and find that certain instruction types are more advantageous for specific applications but can negatively impact other areas. This work provides insights into instruction mixtures, laying the foundations for future research.
Large Language Models (LLMs) have shown a tremendous capacity for generating literary text. However, their effectiveness in generating children's stories has yet to be thoroughly examined. In this study, we evaluate the trustworthiness of children's stories generated by LLMs using various measures, and we compare and contrast our results with both old and new children's stories to better assess their significance. Our findings suggest that LLMs still struggle to generate children's stories at the level of quality and nuance found in actual stories
Larger language models, such as GPT-3, have shown to be excellent in many tasks. However, we demonstrate that out-of-ordinary questions can throw the model off guard. This work focuses on finding answers to negated complementary questions in commonsense scenarios. We illustrate how such questions adversely affect the model responses. We propose a model-agnostic methodology to improve the performance in negated complementary scenarios. Our method outperforms few-shot generation from GPT-3 (by more than 11 points) and, more importantly, highlights the significance of studying the response of large language models in negated complementary questions. The code, data, and experiments are available under: https://github.com/navidre/negated_complementary_commonsense.
An increasingly prevalent problem for intelligent technologies is text safety, as uncontrolled systems may generate recommendations to their users that lead to injury or life-threatening consequences. However, the degree of explicitness of a generated statement that can cause physical harm varies. In this paper, we distinguish types of text that can lead to physical harm and establish one particularly underexplored category: covertly unsafe text. Then, we further break down this category with respect to the system's information and discuss solutions to mitigate the generation of text in each of these subcategories. Ultimately, our work defines the problem of covertly unsafe language that causes physical harm and argues that this subtle yet dangerous issue needs to be prioritized by stakeholders and regulators. We highlight mitigation strategies to inspire future researchers to tackle this challenging problem and help improve safety within smart systems.