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Found 30 papers, 15 papers with code
A Coarse-to-Fine Labeling Framework for Joint Word Segmentation, POS Tagging, and Constituent Parsing
In the coarse labeling stage, the joint model outputs a bracketed tree, in which each node corresponds to one of four labels (i. e., phrase, subphrase, word, subword).
APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing
Thanks to the strong representation learning capability of deep learning, especially pre-training techniques with language model loss, dependency parsing has achieved great performance boost in the in-domain scenario with abundant labeled training data for target domains.
MSceneSpeech: A Multi-Scene Speech Dataset For Expressive Speech Synthesis
We introduce an open source high-quality Mandarin TTS dataset MSceneSpeech (Multiple Scene Speech Dataset), which is intended to provide resources for expressive speech synthesis.
A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching
Although DC-Match is a simple yet effective method for semantic matching, it highly depends on the external NER techniques to identify the keywords of sentences, which limits the performance of semantic matching for minor languages since satisfactory NER tools are usually hard to obtain.
StyleSinger: Style Transfer for Out-of-Domain Singing Voice Synthesis
Moreover, existing SVS methods encounter a decline in the quality of synthesized singing voices in OOD scenarios, as they rest upon the assumption that the target vocal attributes are discernible during the training phase.
Mirror: A Universal Framework for Various Information Extraction Tasks
Sharing knowledge between information extraction tasks has always been a challenge due to the diverse data formats and task variations.
TextrolSpeech: A Text Style Control Speech Corpus With Codec Language Text-to-Speech Models
The dataset comprises 236, 220 pairs of style prompt in natural text descriptions with five style factors and corresponding speech samples.
Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation
Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new real-world knowledge for machines continuously.
AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing
Developing monolingual large Pre-trained Language Models (PLMs) is shown to be very successful in handling different tasks in Natural Language Processing (NLP).
Reference Matters: Benchmarking Factual Error Correction for Dialogue Summarization with Fine-grained Evaluation Framework
To address this problem, we are the first to manually annotate a FEC dataset for dialogue summarization containing 4000 items and propose FERRANTI, a fine-grained evaluation framework based on reference correction that automatically evaluates the performance of FEC models on different error categories.
CopyNE: Better Contextual ASR by Copying Named Entities
Previous approaches have attempted to address this by utilizing the NE dictionary.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
CED: Catalog Extraction from Documents
To address this problem, we build a large manually annotated corpus, which is the first dataset for the Catalog Extraction from Documents (CED) task.
Ranked #1 on
Catalog Extraction
on ChCatExt
A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends
In this paper, we provide a comprehensive review of the development of Arabic NER, especially the recent advances in deep learning and pre-trained language model.
ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks
Causal chain reasoning (CCR) is an essential ability for many decision-making AI systems, which requires the model to build reliable causal chains by connecting causal pairs.
Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble
Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates the data scarcity problem in NER by automatically generating training samples.
Mining Word Boundaries in Speech as Naturally Annotated Word Segmentation Data
Inspired by early research on exploring naturally annotated data for Chinese word segmentation (CWS), and also by recent research on integration of speech and text processing, this work for the first time proposes to mine word boundaries from parallel speech/text data.
Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Understanding
There is a growing body of work in recent years to develop pre-trained language models (PLMs) for the Arabic language.
Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition
Recent years have witnessed the improving performance of Chinese Named Entity Recognition (NER) from proposing new frameworks or incorporating word lexicons.
Chinese Named Entity Recognition
named-entity-recognition
+3
VLAD-VSA: Cross-Domain Face Presentation Attack Detection with Vocabulary Separation and Adaptation
In this paper, the VLAD aggregation method is adopted to quantize local features with visual vocabulary locally partitioning the feature space, and hence preserve the local discriminability.
Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph
Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in both training and inference.
Ranked #3 on
Document-level Event Extraction
on ChFinAnn
JABER and SABER: Junior and Senior Arabic BERt
Language-specific pre-trained models have proven to be more accurate than multilingual ones in a monolingual evaluation setting, Arabic is no exception.
SingGAN: Generative Adversarial Network For High-Fidelity Singing Voice Generation
In this work, we propose SingGAN, a generative adversarial network designed for high-fidelity singing voice synthesis.
Parallel and High-Fidelity Text-to-Lip Generation
However, the AR decoding manner generates current lip frame conditioned on frames generated previously, which inherently hinders the inference speed, and also has a detrimental effect on the quality of generated lip frames due to error propagation.
An In-depth Study on Internal Structure of Chinese Words
Several previous works on syntactic parsing propose to annotate shallow word-internal structures for better utilizing character-level information.
Drug Package Recommendation via Interaction-aware Graph Induction
Then, the drug interaction graph will be initialized based on medical records and domain knowledge.
Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking
Entity linking (EL) for the rapidly growing short text (e. g. search queries and news titles) is critical to industrial applications.
A High Precision Pipeline for Financial Knowledge Graph Construction
This pipeline combines multiple information extraction techniques with a financial dictionary that we built, all working together to produce over 342, 000 compact extractions from over 288, 000 financial news articles, with a precision of 78{\%} at the top-100 extractions. The extracted triples are stored in a knowledge graph making them readily available for use in downstream applications.
FastLR: Non-Autoregressive Lipreading Model with Integrate-and-Fire
NAR lipreading is a challenging task that has many difficulties: 1) the discrepancy of sequence lengths between source and target makes it difficult to estimate the length of the output sequence; 2) the conditionally independent behavior of NAR generation lacks the correlation across time which leads to a poor approximation of target distribution; 3) the feature representation ability of encoder can be weak due to lack of effective alignment mechanism; and 4) the removal of AR language model exacerbates the inherent ambiguity problem of lipreading.
