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- Learning Deep Transformer Models for Machine Translation782
It is claimed that a truly deep Transformer model can surpass the Transformer-Big counterpart by 1) proper use of layer normalization and 2) a novel way of passing the combination of previous layers to the next.
- A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions200
This survey examines various LLM-generated text detection paradigms, shedding light on challenges like out-of-distribution problems, potential attacks, real-world data issues and ineffective evaluation frameworks, and outlines intriguing directions for future research in LLM-generated text detection to advance responsible artificial intelligence (AI).
- Modeling Localness for Self-Attention Networks196
This work cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention in self-attention networks, to maintain the strength of capturing long distance dependencies while enhance the ability of capturing short-range dependencies.
- Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation181
This work designs zero-shot chain-of-thought (CoT) and few-shot CoT settings using in-context learning for ChatGPT to showcase its capabilities in GEC and demonstrates that it has excellent error detection capabilities and can freely correct errors to make the corrected sentences very fluent.
- UM-Corpus: A Large English-Chinese Parallel Corpus for Statistical Machine Translation138
The acquisition of a large scale and high quality parallel corpora for English and Chinese for Statistical Machine Translation (SMT) is described, designed to embrace eight different domains.
- Convolutional Self-Attention Networks137
Novel convolutional self-attention networks are proposed, which offer SANs the abilities to strengthen dependencies among neighboring elements, and model the interaction between features extracted by multiple attention heads.
- Norm-Based Curriculum Learning for Neural Machine Translation135
This paper aims to improve the efficiency of training an NMT by introducing a novel norm-based curriculum learning method that uses the norm (aka length or module) of a word embedding as a measure of the difficulty of the sentence, the competence of the model, and the weight of the sentences.
- Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning125
The biology background of AMP is introduced, various feature encoding methods used to represent the features of peptide sequences are presented, and the most popular DL techniques are explained and highlighted, to survey the latest AMP prediction methods achieved by DL approaches.
- Context-Aware Self-Attention Networks124
This work proposes to contextualize the transformations of the query and key layers, which are used to calculates the relevance between elements, and leverage the internal representations that embed both global and deep contexts, thus avoid relying on external resources.
- Uncertainty-Aware Curriculum Learning for Neural Machine Translation111
This work proposes uncertainty-aware curriculum learning, motivated by the intuition that the higher the uncertainty in a translation pair, the more complex and rarer the information it contains; and the end of the decline in model uncertainty indicates the completeness of current training stage.
- Knowledge modeling via contextualized representations for LSTM-based personalized exercise recommendation101
Two approaches that use a contextualized representation of KCs are proposed, one with a content-based approach and another with a Long Short Term Memory (LSTM) network plus a personalization mechanism.
- Context-aware Self-Attention Networks for Natural Language Processing67
This work casts local context modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention and leverage the internal representations that embed sentence-level information as the global context to contextualize the transformations of query and key layers.
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- Is Your Model Really A Good Math Reasoner? Evaluating Mathematical Reasoning with Checklist63
It is argued that if a model really understands a problem, it should be robustly applied across a diverse array of tasks and MathCheck, a well-designed checklist for testing task generalization and reasoning robustness, is introduced, as well as an automatic tool to generate checklists efficiently.
- LEPOR: A Robust Evaluation Metric for Machine Translation with Augmented Factors59
Experiments show that this novel metric yields the state-of-the-art correlation with human judgments compared with classic metrics BLEU, TER, Meteor-1.3 and two latest metrics (AMBER and MP4IBM1), which proves it a robust one by employing a feature-rich and model-independent approach.
- CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries57
This paper constructs CultureVerse, a large-scale multimodal benchmark covering 19, 682 cultural concepts, 188 countries/regions, 15 cultural concepts, and 3 question types, and proposes CultureVLM, a series of VLMs fine-tuned on the authors' dataset to achieve significant performance improvement in cultural understanding.
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- Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation55
This work directly exposes the raw data into NAT by leveraging pretraining to rejuvenate more alignments for low-frequency target words, and combines these complementary approaches as a new training strategy for further boosting NAT performance.
- Self-Paced Learning for Neural Machine Translation54
Experimental results over multiple translation tasks demonstrate that the proposed NMT model yields better performance than strong baselines and those models trained with human-designed curricula on both translation quality and convergence speed.
- Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization54
The problem of predicting directional movements of stock prices based on news articles is revisited and a unified latent space model is proposed to characterize the "co-movements" between stock prices and news articles.
- Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models51
This study revisits six core challenges of Neural Machine Translation, offering insights into their ongoing relevance in the context of advanced Large Language Models, and identifies three new challenges in LLM-based translation: inference efficiency, translation of low-resource languages during pretraining, and human-aligned evaluation.
- Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation51
Experimental results on 10 different low-resource domains show that meta-curriculum learning can improve the translation performance of both familiar and unfamiliar domains.
- Leveraging Local and Global Patterns for Self-Attention Networks51
The extensive analyses verify that the two types of contexts are complementary to each other, and the proposed hybrid attention mechanism gives highly effective improvements in their integration.
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- Guiding Variational Response Generator to Exploit Persona49
This paper proposes to adopt the personality-related characteristics of human conversations into variational response generators, by designing a specific conditional variational autoencoder based deep model with two new regularization terms employed to the loss function, to guide the optimization towards the direction of generating both persona-aware and relevant responses.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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