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- Relational Graph Attention Network for Aspect-based Sentiment Analysis720
This paper defines a unified aspect-oriented dependency tree structure rooted at a target aspect by reshaping and pruning an ordinary dependency parse tree and proposes a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction.
- SG-Net: Syntax-Guided Machine Reading Comprehension197
This work uses syntax to guide the text modeling by incorporating explicit syntactic constraints into attention mechanism for better linguistically motivated word representations and shows that the proposed SG-Net design helps achieve substantial performance improvement over strong baselines.
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- Instance Weighting for Neural Machine Translation Domain Adaptation141
Two instance weighting technologies, i.e., sentence weighting and domain weighting with a dynamic weight learning strategy, are proposed for NMT domain adaptation and empirical results show that the proposed methods can substantially improve NMT performance.
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- Boundary Enhanced Neural Span Classification for Nested Named Entity Recognition135
This work proposes a boundary enhanced neural span classification model that has the ability to generate high-quality candidate spans and greatly reduces the time complexity during inference, and incorporates an additional boundary detection task to predict those words that are boundaries of entities.
- Igniting Language Intelligence: The Hitchhiker’s Guide from Chain-of-Thought Reasoning to Language Agents126
A thorough discourse is orchestrated, penetrating vital research dimensions, encompassing the foundational mechanics of CoT techniques, with a focus on elucidating the circumstances and justification behind its efficacy; the paradigm shift in CoT; and the burgeoning of language agents fortified by CoT approaches.
- Neural Machine Translation with Universal Visual Representation124
A universal visual representation learned over the monolingual corpora with image annotations is presented, which overcomes the lack of large-scale bilingual sentence-image pairs, thereby extending image applicability in NMT.
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- 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.
- Sentence Embedding for Neural Machine Translation Domain Adaptation110
The NMT’s internal embedding of the source sentence is exploited and the sentence embedding similarity is used to select the sentences which are close to in-domain data to substantially improve NMT performance.
- Neural Topic Modeling with Bidirectional Adversarial Training99
The proposed Bidirectional Adversarial Topic (BAT) model is the first attempt of applying bidirectional adversarial training for neural topic modeling and shows that BAT and Gaussian-BAT obtain more coherent topics, outperforming several competitive baselines.
- Syntax-Directed Attention for Neural Machine Translation96
This paper extends the local attention with syntax-distance constraint, which focuses on syntactically related source words with the predicted target word to learning a more effective context vector for predicting translation, to provide more translation performance for NMT from source representation.
- A Study on ReLU and Softmax in Transformer94
A full ReLU architecture named ReLUFormer is proposed which performs better than the baseline Transformer on long sequence tasks such as document translation and FFN and key-value memory are equivalent, and thus the Transformer can be viewed as a memory network where FFNs and self-attention networks are both key- value memories.
- SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis93
This paper pre-train a sentiment-aware language model (SentiX) via domain-invariant sentiment knowledge from large-scale review datasets, and utilize it for cross-domain sentiment analysis task without fine-tuning.
- Threshold segmentation algorithm for automatic extraction of cerebral vessels from brain magnetic resonance angiography images74
The proposed threshold segmentation method can be used to accurately and efficiently extract cerebral vessels from brain MRA images and may be used for studies focusing on three-dimensional visualization and volumetric quantification of cerebral vessels.
- Data-dependent Gaussian Prior Objective for Language Generation70
Experimental results show that the proposed data-dependent Gaussian prior objective (D2GPo) can effectively make use of more detailed prior in the data and significantly improve the performance of typical language generation tasks, including supervised and unsupervised machine translation, text summarization, storytelling, and image caption.
- High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays68
Besides the effectiveness and high accuracy, the simplified OCU architecture served as a building block could be easily duplicated and integrated to future chip-scale optical CNNs.
- Multi-Domain Dialogue Acts and Response Co-Generation66
A neural co-generation model that generates dialogue acts and responses concurrently and achieves very favorable improvement over several state-of-the-art models in both automatic and human evaluations.
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- Neural Machine Translation with Source Dependency Representation64
A novel NMT with source dependency representation to improve translation performance of NMT, especially long sentences, is proposed and empirical results show that this method achieves 1.6 BLEU improvements on average over a strong NMT system.
- Recognizing textual entailment using sentence similarity based on dependency tree skeletons64
A novel approach to RTE that exploits a structure-oriented sentence representation followed by a similarity function that makes use of a limited size of training data without any external knowledge bases or handcrafted inference rules is presented.
- BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization61
A novel Bi-directional Selective Encoding with Template (BiSET) model, which leverages template discovered from training data to softly select key information from each source article to guide its summarization process, is proposed.
- Using Word Embeddings to Enhance Keyword Identification for Scientific Publications61
This paper proposes an approach that uses word embedding vectors as an external knowledge base for both keyword extraction and generation, and shows that this approach outperforms many baseline algorithms, and is comparable to the state-of-the-art algorithm on the authors' chosen dataset.
- Text Compression-aided Transformer Encoding54
This paper proposes explicit and implicit text compression approaches to enhance the Transformer encoding and evaluates models using this approach on several typical downstream tasks that rely on the encoding heavily, and concludes that text compression helps the encoders to learn better language representations.
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- Recognizing textual entailment using a subsequence kernel method53
This work presents a novel approach to recognizing Textual Entailment by using machine learning techniques using a limited data set, no external knowledge bases, and no handcrafted inference rules to learn whether an entailment relation holds between two texts.
- Neural Network Based Bilingual Language Model Growing for Statistical Machine Translation51
The results show that the proposed novel neural network based bilingual LM growing method can improve both the perplexity score for LM evaluation and BLEU score for SMT, and significantly outperforms the existing LM growing methods without extra corpus.
- Blind Spectrum Sensing by Information Theoretic Criteria for Cognitive Radios51
This paper introduces a new overdetermined channel model constructed by applying multiple antennas or oversampling at the secondary user to make ITC applicable, and presents a generalized ITC sensing algorithm that can provide a flexible tradeoff between the probabilities of detection and the probability of false alarm.
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- FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition47
This work proposes FastCorrect 2, an error correction model that takes multiple ASR candidates as input for better correction accuracy, and develops a candidate predictor to detect the most suitable candidate for the decoder.
- Lattice-Based Transformer Encoder for Neural Machine Translation47
This work proposes lattice-based encoders to explore effective word or subword representation in an automatic way during training and proposes two methods: 1) lattice positional encoding and 2) lattICE-aware self-attention to further improve translation performance.
- WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types46
- DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings45
The proposed DialogueCSE introduces a novel matching-guided embedding mechanism, which generates a context-aware embedding for each candidate response embedding according to the guidance of the multi-turn context-response matching matrices and minimizes the contrastive loss across all pairs.
- The ACL Anthology Searchbench45
A novel application for structured search in scientific digital libraries that provides search in both its bibliographic metadata and semantically analyzed full textual content and serves as a showcase for the recent progress in natural language processing research and language technology.
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- Semi-supervised Domain Adaptation for Dependency Parsing43
A simple domain embedding approach is proposed to merge the source- and target-domain training data, which is shown to be more effective than both direct corpus concatenation and multi-task learning and shows that a simple fine-tuning procedure can further boost cross-domain parsing accuracy by large margin.
- Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation43
Empirical results show that the proposed methods significantly outperform conventional UNMT, and two methods that train UNMT with UBWE agreement are proposed.
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- Self-attentive Biaffine Dependency Parsing42
This work for the first time applies the self-attention mechanism to dependency parsing as the replacement of BiLSTM, leading to competitive performance on both English and Chinese benchmark data.
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- Universal Multimodal Representation for Language Understanding38
Analysis indicates that the visual signals enrich textual representations of content words, provide fine-grained grounding information about the relationship between concepts and events, and potentially conduce to disambiguation.
- Exploring Recombination for Efficient Decoding of Neural Machine Translation38
This work introduces recombination in NMT decoding based on the concept of the “equivalence” of partial hypotheses and uses a simple n-gram suffix based equivalence function to adapt it into beam search decoding and shows that the proposed method can obtain similar translation quality with a smaller beam size, making NMT decode more efficient.
- Entity Relation Extraction as Dependency Parsing in Visually Rich Documents37
This paper adapts the popular dependency parsing model, the biaffine parser, to this entity relation extraction task, and identifies relations between groups of words with layout information instead.
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- Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks33
This work introduces multi-task learning (MTL) to train the parser and the ORL model simultaneously and proposes dependency graph convolutional networks (DEPGCN) to encode parser information at different processing levels.
- Explicit Sentence Compression for Neural Machine Translation32
This paper proposed an explicit sentence compression method to enhance the source sentence representation for NMT, and proposes three ways, including backbone source-side fusion, target- side fusion, and both-side fused, to integrate the compressed sentence into NMT.
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- A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents27
The experimental results show that the proposed unified span-based approach achieves significant improvements over previous works in the exact F1 score and reduces the number of wrongly-predicted opinion expressions and roles, showing the effectiveness of the method.
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- Syntax-Enhanced Self-Attention-Based Semantic Role Labeling21
The experiment results demonstrate that with proper incorporation of the high quality syntactic information, the model achieves a new state-of-the-art performance for the Chinese SRL task on the CoNLL-2009 dataset.
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- Overview of the NLPCC 2019 Shared Task: Cross-Domain Dependency Parsing19
An overview of the NLPCC 2019 shared task on cross-domain dependency parsing, including (1) the data annotation process, (2) task settings, methods, results, and analysis of submitted systems and the authors' recent work (Li+19), and discussions on related works and future directions.
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- SJTU-NICT at MRP 2019: Multi-Task Learning for End-to-End Uniform Semantic Graph Parsing17
This paper describes SJTU-NICT’s system for participating in the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL) and introduces multi-task learning for multiple objectives within the same framework.
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- SUDA-Alibaba at MRP 2019: Graph-Based Models with BERT16
This paper describes participating systems in the shared task on Cross- Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL), which includes five frameworks for graph-based meaning representations, i.e., DM, PSD, EDS, UCCA, and AMR.
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- Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogue9
SAFARI is a novel framework that decouples the knowledge grounding into multiple sources and response generation, which allows easy extension to various knowledge sources including the possibility of not using any sources.
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- Chinese Opinion Role Labeling with Corpus Translation: A Pivot Study8
This paper presents the first work of Chinese ORL, constructing a Chinese dataset by manually translating and projecting annotations from a standard English MPQA dataset, and investigates the effectiveness of cross-lingual transfer methods, including model transfer and corpus translation.
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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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