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Works332 from public data
- Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency839
A new word replacement order determined by both the wordsaliency and the classification probability is introduced, and a greedy algorithm called probability weighted word saliency (PWWS) is proposed for text adversarial attack.
- LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding756
LayoutLMv2 architecture with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework and achieves new state-of-the-art results on a wide variety of downstream visually-rich document understanding tasks.
- LTP: A Chinese Language Technology Platform503
LTP (Language Technology Platform) is an integrated Chinese processing platform which includes a suite of high performance natural language processing modules and relevant corpora that achieved good results in some relevant evaluations, such as CoNLL and SemEval.
- Data augmentation approaches in natural language processing: A survey405
This paper frames DA methods into three categories based on the diversity of augmented data, including paraphrasing, noising, and sampling, and introduces their applications in NLP tasks as well as the challenges.
- A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding299
A novel framework for SLU to better incorporate the intent information, which further guiding the slot filling is proposed, which achieves the state-of-the-art performance and outperforms other previous methods by a large margin.
- Learning Semantic Hierarchies via Word Embeddings289
This paper proposes a novel and effective method for the construction of semantic hierarchies based on word embeddings, which can be used to measure the semantic relationship between words.
- Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting278
A recall and learn mechanism, which adopts the idea of multi-task learning and jointly learns pretraining tasks and downstream tasks, and an Objective Shifting mechanism to focus the learning on downstream tasks gradually.
- Towards Better261
This paper describes the system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies, which was ranked first according to LAS and outperformed the other systems by a large margin.
- Towards Conversational Recommendation over Multi-Type Dialogs254
This work creates a human-to-human Chinese dialog dataset DuRecDial, where there are multiple sequential dialogs for a pair of a recommendation seeker and a recommender, where the recommender proactively leads a multi-type dialog to approach recommendation targets and then makes multiple recommendations with rich interaction behavior.
- Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network210
A Label-enhanced Task-Adaptive Projection Network (L-TapNet) based on the state-of-the-art few-shot classification model – TapNet is proposed, by leveraging label name semantics in representing labels.
- Cross-lingual Dependency Parsing Based on Distributed Representations201
This paper provides two algorithms for inducing cross-lingual distributed representations of words, which map vocabularies from two different languages into a common vector space and bridges the lexical feature gap by using distributed feature representations and their composition.
- CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP179
A data augmentation framework to generate multi-lingual code-switching data to fine-tune mBERT, which encourages model to align representations from source and multiple target languages once by mixing their context information.
- N-LTP: An Open-source Neural Language Technology Platform for Chinese172
N-LTP, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: lexical analysis (Chinese word segmentation, part-of-speech tagging, and named entity recognition), syntactic parsing (dependency parsing), and semantic parsing (semantic dependency parsing and semantic role labeling).
- Convolution Neural Network for Relation Extraction172
This paper proposes a novel convolution network, incorporating lexical features, applied to Relation Extraction, and compares the Convolution Neural Network CNN on relation extraction with the state-of-art tree kernel approach, including Typed Dependency Path Kernel and Shortest Dependency path Kernel and Context-Sensitive tree kernel.
- A Co-Interactive Transformer for Joint Slot Filling and Intent Detection156
This paper proposes a co-interactive module to consider the cross-impact by building a bidirectional connection between the two related tasks, where slot and intent can be able to attend on the corresponding mutual information.
- A survey of multilingual large language models148
A unified and thorough review of the field, highlighting recent progress and emerging trends in MLLM research, and provides a unified framework to summarize the current progress in MLLMs.
- Revisiting Embedding Features for Simple Semi-supervised Learning127
Experiments on the task of named entity recognition show that each of the proposed approaches can better utilize the word embedding features, among which the distributional prototype approach performs the best.
- A Survey on Spoken Language Understanding: Recent Advances and New Frontiers126
This paper gives a thorough review of this research, covering different aspects including a new taxonomy, and provides a new perspective for SLU, including single model vs. joint model, implicit joint modeling vs. pretrained paradigm.
- Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing124
This paper proposes Cross-Lingual BERT Transformation (CLBT), a simple and efficient approach to generate cross-lingual contextualized word embeddings based on publicly available pre-trained BERT models (Devlin et al., 2018).
- AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling123
An Adaptive Graph-Interactive Framework for joint multiple intent detection and slot filling is proposed, where an intent-slot graph interaction layer is applied to each token adaptively to model the strong correlation between the slot and intents.
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- Named Entity Recognition with Bilingual Constraints108
A method is proposed that formulates the problem of exploring signals on unannotated bilingual text as a simple Integer Linear Program, which encourages entity tags to agree via bilingual constraints and can improve strong baselines for both Chinese and English.
- GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling107
This paper proposes a non-autoregressive model for joint multiple intent detection and slot filling, achieving more fast and accurate performance while being 11.5 times faster.
- NL-Augmenter 🦎 → 🐍 A Framework for Task-Sensitive Natural Language Augmentation105
The efficacy of NL-Augmenter is demonstrated by using its transformations to analyze the robustness of popular language models by finding different models to be differently challenged on different tasks, with quasi-systematic score decreases.
- DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification103
A Deep Co-Interactive Relation Network (DCR-Net) is proposed to explicitly consider the cross-impact and model the interaction between the two tasks by introducing a co-interactive relation layer and employs the Bidirectional Encoder Representation from Transformer in the framework, which can further boost the model's performance in both tasks.
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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