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Works196 from public data
- Modeling Coverage for Neural Machine Translation786
This paper proposes coverage-based NMT, which maintains a coverage vector to keep track of the attention history and improves both translation quality and alignment quality over standard attention- based NMT.
- Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs586
This paper introduces novel efficiency metrics from both outcome and process perspectives to evaluate the rational use of computational resources by o1-like models, and proposes strategies to mitigate overthinking, streamlining reasoning processes without compromising accuracy.
- On the "naturalness" of buggy code305
It is found that code with bugs tends to be more entropic (i.e. unnatural), becoming less so as bugs are fixed, suggesting that entropy may be a valid, simple way to complement the effectiveness of PMD or FindBugs, and that search-based bug-fixing methods may benefit from using entropy both for fault-localization and searching for fixes.
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- On the localness of software289
A novel cache language model that consists of both an n-gram and an added “ cache" component to exploit localness, which finds that human-written programs are localized: they have useful local regularities that can be captured and exploited.
- DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning282
DeepMath-103K is introduced, a large-scale mathematical dataset designed with high difficulty, rigorous decontamination against numerous benchmarks, and verifiable answers for rule-based RL reward that fosters the development of generalizable and advancing reasoning.
- Document-Level Machine Translation with Large Language Models219
An in-depth evaluation of LLMs' ability on discourse modeling using document-level machine translation (MT) as a testbed finds that LLMs have demonstrated superior performance and show potential to become a new paradigm for document- level translation.
- Neural Machine Translation with Reconstruction212
Experiments show that the proposed framework significantly improves the adequacy of NMT output and achieves superior translation result over state-of-the-art NMT and statistical MT systems.
- Exploiting Cross-Sentence Context for Neural Machine Translation209
This paper proposes a cross-sentence context-aware approach and investigates the influence of historical contextual information on the performance of neural machine translation (NMT).
- 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.
- Multi-Head Attention with Disagreement Regularization190
This work proposes three types of disagreement regularization, which respectively encourage the subspace, the attended positions, and the output representation associated with each attention head to be different from other heads.
- Learning to Remember Translation History with a Continuous Cache189
This work proposes to augment NMT models with a very light-weight cache-like memory network, which stores recent hidden representations as translation history and the probability distribution over generated words is updated online depending on the translation history retrieved from the memory.
- Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs172
A decoding strategy with thought switching penalty TIP that discourages premature transitions between thoughts, encouraging deeper exploration of each reasoning path and contributes to understanding reasoning inefficiencies in o1-like LLMs and offers a practical solution to enhance their problem-solving capabilities.
- Towards Robust Neural Machine Translation169
Experimental results on Chinese-English, English-German and English-French translation tasks show that the proposed approaches can not only achieve significant improvements over strong NMT systems but also improve the robustness of NMT models.
- Context Gates for Neural Machine Translation152
Context gates are proposed which dynamically control the ratios at which source and target contexts contribute to the generation of target words and can enhance both the adequacy and fluency of NMT with more careful control of the information flow from contexts.
- EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation150
A multi-resolution adversarial model – EmpDG is proposed, to generate more empathetic responses and an interactive adversarial learning framework which exploits the user feedback, to identify whether the generated responses evoke emotion perceptivity in dialogues.
- Modeling Source Syntax for Neural Machine Translation150
This paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements, and linearize parse trees of source sentences to obtain structural label sequences.
- 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.
- On the diversity of multi-head attention134
This work introduces a disagreement regularization to explicitly encourage the diversity among multiple attention heads, and proposes to better capture the diverse information distributed in the extracted partial-representations with the routing-by-agreement algorithm.
- 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.
- Exploring Human-Like Translation Strategy with Large Language Models118
This work proposes the MAPS framework, which stands for Multi-Aspect Prompting and Selection, and enables LLMs first to analyze the given source sentence and induce three aspects of translation-related knowledge to guide the final translation process.
- Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory99
This paper proposes a new framework which exploits retrieval results via a skeleton-to-response paradigm and shows that this approach significantly improves the informativeness of the generated responses.
- Exploiting Deep Representations for Neural Machine Translation98
This work proposes to simultaneously expose all of the top layers of encoder and decoder with layer aggregation and multi-layer attention mechanisms, and introduces an auxiliary regularization term to encourage different layers to capture diverse information.
- Neural Machine Translation Advised by Statistical Machine Translation95
Experimental results on Chinese-English translation show that the proposed approach achieves significant and consistent improvements over state-of-the-art NMT and SMT systems on multiple NIST test sets.
- On the Inference Calibration of Neural Machine Translation94
This work provides in-depth analyses of the correlation between calibration and translation performance as well as linguistic properties of miscalibration and reports a number of interesting findings that might help humans better analyze, understand and improve NMT models.
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 2026-10-13. 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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