Search Results for author:
Found 34 papers, 16 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.
Beware of Calibration Data for Pruning Large Language Models
As large language models (LLMs) are widely applied across various fields, model compression has become increasingly crucial for reducing costs and improving inference efficiency.
A Convex-optimization-based Layer-wise Post-training Pruner for Large Language Models
Pruning is a critical strategy for compressing trained large language models (LLMs), aiming at substantial memory conservation and computational acceleration without compromising performance.
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.
OPT-Tree: Speculative Decoding with Adaptive Draft Tree Structure
It searches the optimal tree structure that maximizes the mathematical expectation of the acceptance length in each decoding step.
Optimizing Large Model Training through Overlapped Activation Recomputation
Our comprehensive evaluation using GPT models with 1. 3B-20B parameters shows that both OPT and HEU outperform the state-of-the-art recomputation approaches (e. g., Megatron-LM and Checkmake) by 1. 02-1. 53x.
Adapprox: Adaptive Approximation in Adam Optimization via Randomized Low-Rank Matrices
As deep learning models exponentially increase in size, optimizers such as Adam encounter significant memory consumption challenges due to the storage of first and second moment data.
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.
Shai: A large language model for asset management
This paper introduces "Shai" a 10B level large language model specifically designed for the asset management industry, built upon an open-source foundational model.
How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions
While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or do they merely mimic comprehension through pattern recognition?
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.
High-order Joint Constituency and Dependency Parsing
Compared to their work, we make progress in three aspects: (1) adopting a much more efficient decoding algorithm of $O(n^4)$ time complexity, (2) exploring joint modeling at the training phase, instead of only at the inference phase, (3) proposing high-order scoring components to promote constituent-dependency interaction.
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.
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.
Multi-modal Siamese Network for Entity Alignment
To deal with that problem, in this paper, we propose a novel Multi-modal Siamese Network for Entity Alignment (MSNEA) to align entities in different MMKGs, in which multi-modal knowledge could be comprehensively leveraged by the exploitation of inter-modal effect.
Ranked #7 on
Multi-modal Entity Alignment
on UMVM-oea-d-w-v1
(using extra training 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
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.
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.
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.
MMEA: Entity Alignment for Multi-Modal Knowledge Graphs
To that end, in this paper, we propose a novel solution called Multi-Modal Entity Alignment (MMEA) to address the problem of entity alignment in a multi-modal view.
Robust Estimation of Similarity Transformation for Visual Object Tracking
In order to efficiently search in such a large 4-DoF space in real-time, we formulate the problem into two 2-DoF sub-problems and apply an efficient Block Coordinates Descent solver to optimize the estimation result.
Finding Theme Communities from Database Networks
Given a database network where each vertex is associated with a transaction database, we are interested in finding theme communities.
