Future Context Research Articles

Distilling Knowledge in Machine Translation of Agglutinative Languages with Backward and Morphological Decoders

Agglutinative languages often have morphologically complex words(MCWs) composed of multiple morphemes arranged in a hierarchical structure, posing significant challenges in translation tasks. We present a novel Knowledge Distillation approach tailored for improving the translation of such languages. Our method involves an encoder, a forward decoder, and two auxiliary decoders: a backward decoder and a morphological decoder. The forward decoder generates target morphemes autoregressively and is augmented by distilling knowledge from the auxiliary decoders. The backward decoder incorporates future context, while the morphological decoder integrates target-side morphological information. We have also designed a reliability estimation method to selectively distill only the reliable knowledge from these auxiliary decoders. Our approach relies on morphological word segmentation. We show that the word segmentation method based on unsupervised morphology learning outperforms the commonly used Byte Pair Encoding method on highly agglutinative languages in translation tasks. Our experiments conducted on English-Tamil, English-Manipuri, and English-Marathi datasets show that our proposed approach achieves significant improvements over strong Transformer-based NMT baselines.

George Costakis and the Confines of His Collection

Abstract As George Costakis pointed out: “One of the most important precepts for the collector is that he must precisely, even ruthlessly, define the limits of his collection, and he must resolutely remind himself that – on this particular matter – his reach should never exceed his grasp.” Known as the most famous collector of Russian avant-garde, Costakis, never lost his parallel interest in collecting lubki, religious icons, Russian folk toys and also in the art of the so-called “Non-conformists or the alternative art of the 1960s. He had a twin goal: on the one hand, to expand the confines of his collection of Russian avant-garde, by including the early sources of inspiration of avant-garde artists; and, on the other hand, to demonstrate that this art can provide inspiration to the next generation. In fact, he “exceeded his grasp” only in order to make the art of Russian avant-garde better understood, both in its historical and future context. At the same time he followed Sergei Shchukin’s ethical and educational tradition of keeping his collection in public view, turning his typical Soviet apartment into an open house and finally an unofficial museum of modern art.

An innovative attention infused- BiRecurrenTwin network assisted hybrid segmentation technique for accurate heart disease prediction

Heart disease is a noteworthy global health concern, profoundly affecting public health and individual well-being. Early prediction and diagnosis are crucial for reducing its negative impact. This work focuses on developing an effectual heart disease detection employing advanced optimization approaches and data processing. The proposed system incorporates a hybrid segmentation algorithm, Hybrid K-means-Fuzzy-C-means (HKMFCM) clustering, which assigns membership degrees to data points, enabling soft clustering and accommodating data uncertainty. Additionally, the Artificial Bee Colony (ABC) Optimization method is applied for feature selection, mimicking the foraging behavior of honeybees to identify the most discriminative attributes from the dataset. This optimization algorithm iteratively explores the feature space to select features that enhance the model's predictive accuracy. Furthermore, a novel classification architecture, termed the Attention-infused BiRecurrenTwin Network, is introduced to accurately predict heart disease based on segmented and extracted patient data profiles. This classifier leverages both Bidirectional Gated Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) networks, with their bidirectional nature capturing both past and future contexts, thus increasing the classifier's capability to detect subtle temporal patterns in patient data. In addition, the proposed system addresses traditional approaches limitations through its advanced components like HKMFCM (effectively manages data uncertainty by enabling soft clustering), ABC Optimization technique (enabling an iterative, global search for the most discriminative attributes) Attention-infused BiRecurrenTwin Network (surpasses conventional classifiers by capturing both past and future temporal patterns in patient data). The simulation outcomes demonstrate that the developed system attains improved performance, with accuracy, ROC, F-measure, precision, recall, sensitivity, and specificity values of 96.09%, 97%, 95%, 96.6%, 94.3%, 95.14%, and 97.05%, respectively which is higher than the baseline models with an average of 7.75 % increase in accuracy. These results indicate that the developed predictive models show promise in accurately classifying individuals based on their extracted features, thereby facilitating the early recognition of heart disease.

Maximizing influence via link prediction in evolving networks

Influence Maximization (IM), targeting the optimal selection of k seed nodes to maximize potential information dissemination in prospectively social networks, garners pivotal interest in diverse realms like viral marketing and political discourse dissemination. Despite receiving substantial scholarly attention, prevailing research predominantly addresses the IM problem within the confines of existing networks, thereby neglecting the dynamic evolutionary character of social networks. An inevitable requisite arises to explore the IM problem in social networks of future contexts, which is imperative for certain application scenarios. In this light, we introduce a novel problem, Influence Maximization in Future Networks (IMFN), aimed at resolving the IM problem within an anticipated future network framework. We establish that the IMFN problem is NP-hard and advocate a prospective solution framework, employing judiciously selected link prediction methods to forecast the future network, and subsequently applying a greedy algorithm to select the k most influential nodes. Moreover, we present SCOL (Sketch-based Cost-effective lazy forward selection algorithm Optimized with Labeling technique), a well-designed algorithm to accelerate the query of our IMFN problem. Extensive experimental results, rooted in five real-world datasets, are provided, affirming the efficacy and efficiency of the proffered solution and algorithms.

Rapid context inference in a thalamocortical model using recurrent neural networks

Cognitive flexibility is a fundamental ability that enables humans and animals to exhibit appropriate behaviors in various contexts. The thalamocortical interactions between the prefrontal cortex (PFC) and the mediodorsal thalamus (MD) have been identified as crucial for inferring temporal context, a critical component of cognitive flexibility. However, the neural mechanism responsible for context inference remains unknown. To address this issue, we propose a PFC-MD neural circuit model that utilizes a Hebbian plasticity rule to support rapid, online context inference. Specifically, the model MD thalamus can infer temporal contexts from prefrontal inputs within a few trials. This is achieved through the use of PFC-to-MD synaptic plasticity with pre-synaptic traces and adaptive thresholding, along with winner-take-all normalization in the MD. Furthermore, our model thalamus gates context-irrelevant neurons in the PFC, thus facilitating continual learning. We evaluate our model performance by having it sequentially learn various cognitive tasks. Incorporating an MD-like component alleviates catastrophic forgetting of previously learned contexts and demonstrates the transfer of knowledge to future contexts. Our work provides insight into how biological properties of thalamocortical circuits can be leveraged to achieve rapid context inference and continual learning.

An improved convolutional recurrent neural network for stock price forecasting

<p>Stock price forecasting is a challenging area of research, particularly due to the complexity and unpredictability of financial markets. The accuracy of prediction models is influenced by various factors, including nonlinearity, seasonality, and economic shocks. Deep learning has demonstrated better forecasts of stock prices than traditional approaches. This study, therefore, proposed a new approach to improve forecasting system based on an end-to-end convolutional recurrent neural network (CRNN) with attention mechanism. Our approach first investigates local stock price features using 1D convolutional neural network, and then employs a bidirectional long short-term memory (Bi-LSTM) network for forecasting. This model stands out by effectively utilizing contextual data and representing the temporal character of data. The Bi-LSTM is helpful for understanding the history and future contextual information since it uncovers both past and future contexts of stock data. Furthermore, integrating attention mechanism within the CRNN represents a significant improvement. This allows our model to handle long input sequences more effectively and capture the inherent stochasticity in stock prices, which is often missed by traditional models. The effectiveness of our approach is investigated using data on 10 stock indexes from Yahoo Finance. The results show that our method outperforms ARIMA, LSTM, and conventional methods.</p><p> </p>

Futures empathy for foresight research and practice

Foresight methods help us to think about the future and overcome a bias toward short-term thinking in decision making. However, many approaches to investigating the future tend to leave implicit how individuals involved in the interventions relate to the present and to the future in practice and over time. To address this gap, our study highlights the affordances of two methodologies —Scenario Planning and Future Design. In a pilot study combining these methods, we report the results of the novel hybridized approach applied in strategic planning workshops across four corporations. Initial reflections and learnings from the pilot case study and literature review helps to build understandings of the practical and theoretical workings of the methodological innovation. By investigating this combination of foresight methodologies, this research opens up new ways for thinking about futuring and proposes a theory of Futures Empathy. Futures Empathy harnesses a person’s capacity for empathy in a novel way by applying it toward a future context. It consists of an iterative double looping process between self and a not yet existent future. Through imagination and reflection, foresight methods that build Futures Empathy can enhance personal connection and integration with a longer time horizon, thus overcoming presentism. By proposing a theory of Futures Empathy, we hope to contribute to better present and future relating in practice and over time across a multitude of foresight interventions.

Exploring Non-Native-English-Speaking Preservice EFL Teachers’ Cognitions: What Teaching Philosophy Statements Can Tell Us

This study investigates the content and characteristics of the cognitions of 30 Turkish English-as-a-foreign-language preservice teachers as manifested in their teaching philosophy statements. The study showed that the preservice teachers’ cognition was focused on both the pedagogical (teaching-learning and language teaching methods) and the personal and interpersonal (learners’ and teachers’ roles and the relationship between them) aspects of teaching and was characterized by high idealism and naïveté, but they also demonstrated a certain level of awareness of the future contexts in which they would be teaching. Although their past experiences significantly shaped their cognition, they found that their current teacher education program helped them develop a critical view of these experiences.

The fear-avoidance model as an embodied prediction of threat.

The fear-avoidance model is a well-established framework in the understanding of persistent pain. It proposes a dichotomous path: either the context is interpreted as safe; there is no fear reaction and, therefore, the individual engages in active (positive) coping; or the context is interpreted as threatening, leading to a self-reinforcing vicious circle of fear and (negative) avoidance. We propose an embodied interpretation of this phenomenon employing the joint framework of predictive coding and active inference. The key idea is that multisensory integration of exteroceptive, proprioceptive, and interoceptive sensory inputs can lead to dysfunctional experiences of threat in nonthreatening situations. Threat inference can promote fear responses, maladaptive strategies (i.e., avoidance) and self-provides evidence for threat in associated or future contexts, or both. Under this treatment, the prediction of nonrealized threat becomes self-evidencing and context-invariant, and hence self-perpetuating. Safety cues are unable to attenuate the interpretation of the negative context as the dominant inference of the context is threatful and gains more precision and becomes resistant over time. Our model provides an explanation for the emergence of a dysfunctional fear response in the clinical setting despite apparent safety based on modern concepts from theoretical (computational) neuroscience.

The impact of Chinese volume-based procurement on pharmaceutical market concentration.

Optimizing the pharmaceutical industrial structure is the key mission of China's healthcare reform. From the industrial structure perspective, this study empirically evaluated the impact of China's national volume-based procurement (NVBP) policy on market concentration in the hospital-end drug market. This study used drug procurement data of China's public medical institutions which obtained from the national database. A quasi-natural experiment was designed involving eleven pairs of matched treatment-control region combinations, with NVBP policy as the intervention measure. The market was defined by drug name (molecular boundary) and city/province (geographical boundary). Market changes were measured from three dimensions: the number of enterprises and products, market share, and Herfindahl-Hirschman index (HHI). Dual comparison approach and difference-in-difference (DID) method with fixed effect model were applied to quantify policy impacts. The number of enterprises and products decreased by 18 and 83 in pilot regions after NVBP policy, far more than the decreases in control regions (6 and 21). The accumulative market share of 15 bid-winning enterprises increased by 53.67% in volume and 18.79% in value, among which the increment of enterprises with low baseline market share was more prominent (66.64% and 36.40%). Among three enterprise types, the market share of generic consistency evaluation (GCE) certificated generics significantly increased, GCE uncertificated generics significantly decreased, and originators slightly decreased. DID models indicated significantly positive impact of NVBP policy on market concentration, with HHI-volume and HHI-value increasing by 49.33% (β = 0.401, p < 0.01) and 21.05% (β = 0.191, p < 0.01). The implementation of NVBP promoted the intensive drug circulation and supply of Chinese public hospitals, intensifying the exit of GCE uncertificated generics from the hospital-end market. NVBP combined with GCE standards significantly improved market concentration, which brought a positive signal of pharmaceutical industrial structure optimization in China. In the future context of normalized and institutionalized NVBP, the balance should be further sought between low drug prices and reliable hospital drug supply, sustainable industry development.

Unlocking a taboo topic: the sexuality of institutionalized men within a forensic psychiatric facility

ABSTRACT Inpatients’ sexuality in forensic psychiatric facilities remains a neglected topic. Given these circumstances, it creates barriers that hinder inpatients’ ability to express their sexuality, leading to a poor integration of sexual concerns into inpatient treatments. Inpatients’ sexuality can influence recovery journeys negatively or positively, for example by affecting medication adherence due to sexual side effects, or through the positive effects of having romantic relationships. To better understand inpatients’ needs, this qualitative research explores inpatients’ sexuality in a forensic psychiatric facility. This study aims to describe how inpatients perceive and express their sexuality while also exploring the perceived positive impacts of sexual satisfaction, sexual well-being, and intimacy on their recovery. Nine men hospitalized in a high-security forensic facility took part in an individual interview. Thematic analysis was conducted. Themes, including sexual representations and perceived benefits of sexuality, were gathered from past, present, and future contexts. Findings reveal that inpatients adapt their sexual expression in the face of barriers. Participants, while specifying their sexual needs, indicated an interest in forming a relationship with an intimate partner, seeing potential benefits for themselves and their recovery. Results underscore the need to incorporate a positive form of sexuality in treatment plans considering its positive outcomes.

When, at what speed, and how? Resilient transformation of the Vesdre river basin (Belgium) following the 2021 floods

BackgroundDual crises happen when an acute shock unfolds in the context of a creeping crisis. The July 2021 floods in the Vesdre river basin (Wallonia, Belgium) is a typical case of such dual crises in the context of climate change. This study is based on 16 semi-structured interviews (conducted in Spring 2023) with 10 mayors, 4 representatives of the Public Service of Wallonia, 1 person working for the federal government, and 4 project managers, coupled with a document analysis (n = 13). It investigates the temporal strategies that connect short and long-term considerations in the aftermath of this disaster (timing, futuring, pacing, cyclical adaptation, and determining time horizons), at two different governmental levels: river basin and municipal level.ResultsIn general, the window of opportunity to improve disaster resilience has been seized. Several studies were initiated by the Walloon region that shape the idea of an ideal future for the river basin and give recommendations for how to reach it. Unfortunately, those recommendations still come late compared to the temporal reality of the reconstruction process. Municipalities wish to strengthen disaster resilience as soon as possible, but they have to prioritize certain actions over others because of limited resources. The recommendations are considered flexible enough to adapt strategies to future contexts, but no monitoring and evaluation system for doing so has been implemented so far. In addition, clear policy agendas with transformational goals are scarce, and they diverge between the river basin and the municipalities. All these temporal strategies are shaped by elements of the institutional policy arrangement: resources, which affect them all, as well as actors, power, and formal rules, which affect some. These policy dimensions notably slow down the implementation of disaster resilience strategies and limit the determination of consensual time horizons.ConclusionsThe temporal strategies are passively shaped by the policy arrangement dimensions to a greater extent than actively chosen by the stakeholders. A structural transformation of the institutional policy arrangement is therefore needed to enable more coherent temporal strategies between different governance levels and to facilitate the consideration of long-term resilience during the recovery process from disasters.

Modeling the depressive mind: An artificial intelligence approach to deciphering Beck’s cognitive triad

Mental and cognitive well-being is of paramount significance for human beings. Consequently, the early detection of issues that may culminate in conditions such as depression holds great importance in averting adverse outcomes for individuals. Depression, a prevalent mental health disorder, can severely impact an individual’s quality of life. Timely identification and intervention are critical to prevent its progression. Our research delves into the application of Machine Learning (ML) and Deep Learning (DL) techniques to potentially facilitate the early recognition of depressive tendencies. By leveraging the cognitive triad theory, which encapsulates negative self-perception, a pessimistic outlook on the world, and a bleak vision of the future, we aim to develop predictive models that can assist in identifying individuals at risk. In this regard, we selected The Cognitive Triad Dataset, which takes into account six different categories that encapsulate negative and positive postures about three different contexts: self context, future context and world context. Our proposal achieved great performance, by relying on a strict preprocessing analysis, which led to the models obtaining an accuracy value of 0.97 when classifying aspect contexts; 0.95 when classifying sentiment-aspects; and a value of 0.93 in accuracy was achieved under the aspect-sentiment paradigm. Our models outperformed those reported in the literature.

Enhanced Image Captioning using Bidirectional Long Short-Term Memory and Convolutional Neural Networks

The utilization of Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BLSTM) networks in image captioning has significantly enhanced the quality and relevance of generated captions. In this approach, a CNN serves as the encoder to extract meaningful features from the input image, capturing its visual information. These features are then fed into a BLSTM network, acting as the decoder, which processes the features in a bidirectional manner to generate descriptive and coherent captions by considering both past and future context. The model is trained on a dataset of images and corresponding captions, fine-tuning the parameters to optimize the accuracy of caption generation. This combined approach effectively leverages the strengths of CNNs and BLSTMs, resulting in more detailed and contextually appropriate descriptions for the given images.

Including environmental and climatic considerations for sustainable coral reef restoration.

Coral reefs provide ecosystem benefits to millions of people but are threatened by rapid environmental change and ever-increasing human pressures. Restoration is becoming a priority strategy for coral reef conservation, yet implementation remains challenging and it is becoming increasingly apparent that indirect conservation and restoration approaches will not ensure the long-term sustainability of coral reefs. The important role of environmental conditions in restoration practice are currently undervalued, carrying substantial implications for restoration success. Giving paramount importance to environmental conditions, particularly during the pre-restoration planning phase, has the potential to bring about considerable improvements in coral reef restoration and innovation. This Essay argues that restoration risk may be reduced by adopting an environmentally aware perspective that gives historical, contemporary, and future context to restoration decisions. Such an approach will open up new restoration opportunities with improved sustainability that have the capacity to dynamically respond to environmental trajectories.

Student perceptions of transferable skills learned during a simulated placement

ABSTRACT This research explores students’ perceptions of the knowledge and skills gained from a 12-day simulated placement that could transfer to future placements and workplaces. Using thematic analysis we constructed five themes that collectively highlight the broad range of generic and profession-specific knowledge and skills that speech and language therapy students recognised could transfer to future contexts: 1) Extending communication skills benefits future interactions 2) Confidence in skills aids future clinical practice 3) New workplace skills support effective practice 4) Enhanced client-centred care skills are transferable 5) Understanding the ways in which learning occurred assists future learning. Our study affirms students can develop knowledge and skills during simulated placements in areas relevant to graduate expectations in speech and language therapy and other health professions. The study adds to understanding about learning transfer during and beyond student training programmes which can inform university curricula, workplace placement educators and managers supporting new graduates.

A Viewpoint on Simulation Debriefing to Optimize Student Learning

Purpose: The purpose of this article is to promote the viewpoint that simulation debriefing is a critical element of the simulation experience, which serves to facilitate students' ability to synthesize information and construct new mental models as they prepare for their future work as speech-language pathologists. The use of simulations in communication sciences and disorders (CSD) has grown significantly over the last decade. This article reviews key concepts in clinical simulation pedagogy and highlights the importance of debriefing as part of the simulation learning experience. Current guidelines and standards for simulations and debriefing in CSD are discussed, and debriefing models commonly used in nursing and medicine are described. Suggestions are provided to enhance current debriefing practices and guide future research in CSD. Conclusions: Clinical simulation, particularly the debriefing component, has the power to close the gaps in students' clinical knowledge and skills and to prepare students for robust and diverse careers. In a review of debriefing models originating from nursing and medicine, several crucial debriefing components are noted. Students need time to (a) react to and defuse emotions related to the simulation experience, (b) gather and describe key simulation details, (c) analyze simulation outcomes, and (d) summarize new knowledge and consider how it applies to future contexts. Additional research is needed to continue developing and refining simulation and debriefing best practices in CSD. Debriefing models from nursing and medicine ought to be explored and replicated in future CSD research.

Automatic pipeline fault detection using one-dimensional convolutional bidirectional long short-term memory networks with wide first-layer kernels

Pipeline networks are crucial components of modern infrastructure, and ensuring their reliable operation is essential for sustainable development. The percussion-based methods are considered promising for detecting pipeline faults due to their avoidance of constant-contact sensors and ease of implementation. However, the majority of existing percussion-based methods suffer from limitations such as the requirement for manual feature extraction, as well as subpar noise resilience and adaptability. This paper introduces a one-dimensional convolutional bidirectional long short-term memory network with wide first-layer kernels for the classification of percussion-induced acoustic signals, thus achieving automatic identification of pipeline leakage and water deposit conditions. This approach directly extracts features from audio signals using wide first-layer convolutional kernels, eliminating the need for manual feature extraction. Additionally, it employs bidirectional long short-term memory to effectively capture long-term signal dependencies from both past and future contexts. To validate the effectiveness of the method, two case studies were conducted on three groups of pipes. The results show that the proposed method demonstrates superior noise resistance and adaptability compared to other methods, and it also exhibits strong applicability to other percussion signal datasets. Additionally, the impact of different first convolutional kernel sizes on the noise resistance and adaptive performance of the model was investigated, which provides robust guidance for the effective processing of percussion-induced acoustic signals.

Comparative analysis of phytochemical profile and antioxidant and anti-inflammatory activity of four Gentiana species from the Qinghai-Tibet Plateau

Ethnopharmacological relevanceGentiana species, known as the traditional Tibetan medicine “Bangjian,” have been integral to clinical practice for millennia. Despite their longstanding use, our understanding of the variation in chemical constituents and bioactive effects among different species is limited. Aim of the studyIn the present study, we aimed to assess the differences in chemical profiles and bioactivities among four Gentiana species (G. veitchiorum, G. trichotoma, G. crassuloides, and G. squarrosa) and explore potential bioactive markers. Materials and methodsThe chemical composition of the four Gentiana species was analyzed using UPLC-QE-Orbitrap-MS. The antioxidant activity of the extracts was compared through DPPH, ABTS, and reducing power assays. The anti-inflammatory activity was evaluated by measuring the inhibitory effects on lipopolysaccharide-induced secretion of nitric oxide (NO), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) by RAW264.7 macrophages. Additionally, compounds strongly correlated with anti-inflammatory and antioxidant activities were identified through spectrum–effect relationship analysis. ResultsA total of 50 compounds were identified across the four Gentiana species. In vitro antioxidant assays demonstrated DPPH and ABTS scavenging abilities and reducing power within the concentration range of 62.5–2000 μg/mL. All four species inhibited the production of NO, IL-6, and TNF-α in RAW264.7 cells. Spectrum–effect relationship analysis revealed that gentiascabraside A, gentiatibetine, tachioside, lutonarin, and isotachioside were associated with the highest antioxidant activity; and swertiamarin, tarennoside, eleganoside C, and alpigenoside were associated with the highest anti-inflammatory activity. ConclusionsThis study presents, for the first time, the chemical profiles and bioactivities of G. trichotoma, G. crassuloides, and G. squarrosa, which were comprehensively compared with those of G. veitchiorum. The findings provide novel insights to understand the traditional use and/or expand the current use of Gentiana species. Additionally, this research highlights the potential of Gentiana species as natural sources of antioxidants and anti-inflammatory agents, suggesting promising applications in tea production or medicinal contexts in the near future.

VisTFC: Vision-guided target-side future context learning for neural machine translation

Visual features encompass visual information extracted from images or videos, serving as supplementary input to enhance the efficacy of neural machine translation (NMT) systems. By seamlessly integrating these visual features into the translation process, NMT models can tap into the expansive visual context, thereby making more precise predictions during translation. Nonetheless, limited efforts have been directed towards exploring the potential of learning target-side future context using visual features to augment NMT performance. To bridge this gap, this paper introduces the Vision-guided Target-side Future Context (VisTFC) learning framework for NMT. Our core objective is to refine translation quality by effectively harnessing and incorporating target-side future contextual insights derived from the visual modality. The VisTFC framework consists of three pivotal components. Firstly, a graph-based multimodal encoder–decoder is established, constructing a bipartite vision-source/target graph. This enables the acquisition of vision-fused textual representations, synthesizing both linguistic and visual attributes to enhance translation accuracy. Secondly, a target-side future context predictor, incorporating a dynamic routing mechanism, infers future context by synergizing visual information. This empowers the model to anticipate and assimilate contextual cues, ensuring more coherent and contextually coherent translations. Thirdly, a sigmoid update gate is introduced, facilitating controlled integration of predicted future context into the decoding process, enabling the decoder to flexibly adapt and utilize the inferred context. Moreover, the VisTFC framework is fortified with additional loss functions that enforce source/target-vision consistency, reinforcing its robustness and effectiveness. Our results demonstrate that our model can achieve substantial improvements, reaching up to 1.0/0.9/1.1 BLEU points beyond the performance of the most robust baselines. This superior performance is evident on both the Test2016 and Test2017 datasets, as well as on the MSCOCO test sets for English-French translation tasks. Furthermore, our experiments underscore the efficacy of VisTFC, even in scenarios with limited resources, such as the English-Hausa language pair. Here, the model achieves notable enhancements, with improvements of up to 1.8/1.9 BLEU points compared to the strongest text-only NMT models on E-Test and C-Test test sets. These findings provide strong evidence supporting our model’s ability to effectively harness target-side future contextual information from the visual modality, resulting in substantial enhancements in machine translation quality. Further analysis and visualization suggest that VisTFC is capable of learning target-side future context from visual signals for better translation. We also have open-sourced our work in Github.11https://github.com/nlpdl/VsiTFC.