Search Results for author:
Found 69 papers, 24 papers with code
XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation
In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks.
AutoDirector: Online Auto-scheduling Agents for Multi-sensory Composition
With the advancement of generative models, the synthesis of different sensory elements such as music, visuals, and speech has achieved significant realism.
Towards Truthful Multilingual Large Language Models: Benchmarking and Alignment Strategies
In the era of large language models (LLMs), building multilingual large language models (MLLMs) that can serve users worldwide holds great significance.
Step-Back Profiling: Distilling User History for Personalized Scientific Writing
To conduct the experiments, we construct a Personalized Scientific Writing (PSW) dataset to study multi-user personalization.
TGGLinesPlus: A robust topological graph-guided computer vision algorithm for line detection from images
Line detection is a classic and essential problem in image processing, computer vision and machine intelligence.
Hypertext Entity Extraction in Webpage
Webpage entity extraction is a fundamental natural language processing task in both research and applications.
Grounding Language Models for Visual Entity Recognition
We introduce AutoVER, an Autoregressive model for Visual Entity Recognition.
Ploutos: Towards interpretable stock movement prediction with financial large language model
Recent advancements in large language models (LLMs) have opened new pathways for many domains.
Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment
Understanding and identifying musical shape plays an important role in music education and performance assessment.
Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers
This paper presents an in-depth analysis of Large Language Models (LLMs), focusing on LLaMA, a prominent open-source foundational model in natural language processing.
Instructed Language Models with Retrievers Are Powerful Entity Linkers
Generative approaches powered by large language models (LLMs) have demonstrated emergent abilities in tasks that require complex reasoning abilities.
Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency
We propose CoherentED, an ED system equipped with novel designs aimed at enhancing the coherence of entity predictions.
Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations
This indicates that crafting multilingual corpora can be regarded as a vital strategy for enhancing model performance in a specific language, especially in mathematical reasoning tasks.
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
RUEL is the first method that connects user browsing data with typical recommendation datasets and can be generalized to various recommendation scenarios and datasets.
Alleviating Over-smoothing for Unsupervised Sentence Representation
Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities.
Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval
To address this challenge, we propose ToRoDer (TypOs-aware bottlenecked pre-training for RObust DEnse Retrieval), a novel re-training strategy for DRs that increases their robustness to misspelled queries while preserving their effectiveness in downstream retrieval tasks.
TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
On the other hand, there are also many existing models and systems (symbolic-based or neural-based) that can do some domain-specific tasks very well.
Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval
In this paper, we propose to mine and generate self-supervised training data based on a large-scale unlabeled corpus.
Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval
However, we find that the performance of the cross-encoder re-ranker is heavily influenced by the number of training samples and the quality of negative samples, which is hard to obtain in the cross-lingual setting.
Large Language Models are Diverse Role-Players for Summarization Evaluation
First, we propose to model objective and subjective dimensions of generated text based on roleplayers prompting mechanism.
Bridge the Gap between Language models and Tabular Understanding
For instance, TPLMs jointly pre-trained with table and text input could be effective for tasks also with table-text joint input like table question answering, but it may fail for tasks with only tables or text as input such as table retrieval.
Modeling Sequential Sentence Relation to Improve Cross-lingual Dense Retrieval
Specifically, we propose a multilingual PLM called masked sentence model (MSM), which consists of a sentence encoder to generate the sentence representations, and a document encoder applied to a sequence of sentence vectors from a document.
Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA
However, training an effective dense table-text retriever is difficult due to the challenges of table-text discrepancy and data sparsity problem.
Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation
This problem is further exacerbated when using DSI for cross-lingual retrieval, where document text and query text are in different languages.
Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval
On the multilingual sentence retrieval task Tatoeba, our model achieves new SOTA results among methods without using bilingual data.
Negative Sampling for Contrastive Representation Learning: A Review
The learn-to-compare paradigm of contrastive representation learning (CRL), which compares positive samples with negative ones for representation learning, has achieved great success in a wide range of domains, including natural language processing, computer vision, information retrieval and graph learning.
Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding
Despite the great success of spoken language understanding (SLU) in high-resource languages, it remains challenging in low-resource languages mainly due to the lack of labeled training data.
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling
Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks (xSL), such as cross-lingual machine reading comprehension (xMRC) by transferring knowledge from a high-resource language to low-resource languages.
Transformer-Empowered Content-Aware Collaborative Filtering
Knowledge graph (KG) based Collaborative Filtering is an effective approach to personalizing recommendation systems for relatively static domains such as movies and books, by leveraging structured information from KG to enrich both item and user representations.
Multi-View Document Representation Learning for Open-Domain Dense Retrieval
Second, to prevent multi-view embeddings from collapsing to the same one, we further propose a global-local loss with annealed temperature to encourage the multiple viewers to better align with different potential queries.
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding
Although various data augmentation approaches have been proposed to synthesize training data in low-resource target languages, the augmented data sets are often noisy, and thus impede the performance of SLU models.
A Joint and Domain-Adaptive Approach to Spoken Language Understanding
In this paper, we attempt to bridge these two lines of research and propose a joint and domain adaptive approach to SLU.
Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition
Named entity recognition (NER) is a fundamental component in many applications, such as Web Search and Voice Assistants.
CoSQA: 20,000+ Web Queries for Code Search and Question Answering
Finding codes given natural language query isb eneficial to the productivity of software developers.
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model
Many downstream tasks and human readers rely on the output of the ASR system; therefore, errors introduced by the speaker and ASR system alike will be propagated to the next task in the pipeline.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Benchmark datasets have a significant impact on accelerating research in programming language tasks.
Ranked #1 on
Cloze Test
on CodeXGLUE - CT-maxmin
Quantum walks on a programmable two-dimensional 62-qubit superconducting processor
Quantum walks are the quantum mechanical analogue of classical random walks and an extremely powerful tool in quantum simulations, quantum search algorithms, and even for universal quantum computing.
Quantum Physics
Syntax-Enhanced Pre-trained Model
We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa.
Experimental characterization of quantum many-body localization transition
Here, we experimentally implement a scalable protocol for detecting the many-body localization transition point, using the dynamics of a $N=12$ superconducting qubit array.
Quantum Physics Mesoscale and Nanoscale Physics Strongly Correlated Electrons
Reinforced Multi-Teacher Selection for Knowledge Distillation
When multiple teacher models are available in distillation, the state-of-the-art methods assign a fixed weight to a teacher model in the whole distillation.
GLGE: A New General Language Generation Evaluation Benchmark
Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP).
CalibreNet: Calibration Networks for Multilingual Sequence Labeling
To tackle the challenge of lack of training data in low-resource languages, we dedicatedly develop a novel unsupervised phrase boundary recovery pre-training task to enhance the multilingual boundary detection capability of CalibreNet.
Nonvolatile electric control of exciton complex in monolayer MoSe$_2$ with two dimensional ferroelectric CuInP$_2$S$_6$
With the saturated ferroelectric polarization of CIPS, electron-doped or hole-doped MoSe$_2$ is realized in a single device with a large carrier density tunability up to $5\times 10^{12}$cm$^{-2}$.
Materials Science
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation
Then, we devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
A Graph Representation of Semi-structured Data for Web Question Answering
The abundant semi-structured data on the Web, such as HTML-based tables and lists, provide commercial search engines a rich information source for question answering (QA).
Experimental Quantum Generative Adversarial Networks for Image Generation
For the first time, we experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor.
MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension
In this paper, we propose a novel approach that extends the probability vector to a probability matrix.
No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension
The Natural Questions (NQ) benchmark set brings new challenges to Machine Reading Comprehension: the answers are not only at different levels of granularity (long and short), but also of richer types (including no-answer, yes/no, single-span and multi-span).
Tag and Correct: Question aware Open Information Extraction with Two-stage Decoding
The semi-structured answer has two advantages which are more readable and falsifiable compared to span answer.
Mining Implicit Relevance Feedback from User Behavior for Web Question Answering
In this paper, we make the first study to explore the correlation between user behavior and passage relevance, and propose a novel approach for mining training data for Web QA.
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension
Multilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve performance on low resource languages.
LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network
The graph is used to obtain graph-enhanced contextual representations of words in Transformer-based architecture.
Pre-training Text Representations as Meta Learning
In this work, we introduce a learning algorithm which directly optimizes model's ability to learn text representations for effective learning of downstream tasks.
Improving Readability for Automatic Speech Recognition Transcription
In this work, we propose a novel NLP task called ASR post-processing for readability (APR) that aims to transform the noisy ASR output into a readable text for humans and downstream tasks while maintaining the semantic meaning of the speaker.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Inferential Text Generation with Multiple Knowledge Sources and Meta-Learning
In this work, we use multiple knowledge sources as fuels for the model.
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evaluate their performance across a diverse set of cross-lingual tasks.
TGGLines: A Robust Topological Graph Guided Line Segment Detector for Low Quality Binary Images
Line segment detection is an essential task in computer vision and image analysis, as it is the critical foundation for advanced tasks such as shape modeling and road lane line detection for autonomous driving.
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation tasks.
Ranked #1 on
Code Documentation Generation
on CodeSearchNet - Go
Model Compression with Two-stage Multi-teacher Knowledge Distillation for Web Question Answering System
The experiment results show that our method can significantly outperform the baseline methods and even achieve comparable results with the original teacher models, along with substantial speedup of model inference.
Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering
In this work, we propose to automatically extract evidence from heterogeneous knowledge sources, and answer questions based on the extracted evidence.
Ranked #14 on
Common Sense Reasoning
on CommonsenseQA
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks
On XNLI, 1. 8% averaged accuracy improvement (on 15 languages) is obtained.
Cross-Lingual Natural Language Inference
Cross-Lingual Question Answering
+1
Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training
We propose Unicoder-VL, a universal encoder that aims to learn joint representations of vision and language in a pre-training manner.
Ranked #5 on
Image-to-Text Retrieval
on MS COCO
(Recall@10 metric)
Joint Type Inference on Entities and Relations via Graph Convolutional Networks
We develop a new paradigm for the task of joint entity relation extraction.
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric)
NeuronBlocks: Building Your NLP DNN Models Like Playing Lego
Deep Neural Networks (DNN) have been widely employed in industry to address various Natural Language Processing (NLP) tasks.
Model Compression with Multi-Task Knowledge Distillation for Web-scale Question Answering System
Deep pre-training and fine-tuning models (like BERT, OpenAI GPT) have demonstrated excellent results in question answering areas.
Naturally Combined Shape-Color Moment Invariants under Affine Transformations
We proposed a kind of naturally combined shape-color affine moment invariants (SCAMI), which consider both shape and color affine transformations simultaneously in one single system.
