Search Results for author:
Found 45 papers, 25 papers with code
Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning
For dialogue summarization, the subdomain of text summarization, utterances are concatenated to flat text before being processed.
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes
This paper proposes a semi-supervised methodology for training physics-informed machine learning methods.
Multi-LoRA Composition for Image Generation
Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models for the accurate rendition of specific elements like distinct characters or unique styles in generated images.
Investigating Data Contamination for Pre-training Language Models
Language models pre-trained on web-scale corpora demonstrate impressive capabilities on diverse downstream tasks.
PINNs-Based Uncertainty Quantification for Transient Stability Analysis
This paper addresses the challenge of transient stability in power systems with missing parameters and uncertainty propagation in swing equations.
Learning Collective Behaviors from Observation
We present a comprehensive examination of learning methodologies employed for the structural identification of dynamical systems.
Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
However, when it comes to information extraction - a classic task in natural language processing - most task-specific systems cannot align well with long-tail ad hoc extraction use cases for non-expert users.
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions
Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks.
Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective
In this paper, we seek to empirically investigate knowledge transfer from larger to smaller models through a parametric perspective.
L-Eval: Instituting Standardized Evaluation for Long Context Language Models
Recently, there has been growing interest in extending the context length of large language models (LLMs), aiming to effectively process long inputs of one turn or conversations with more extensive histories.
Towards Saner Deep Image Registration
Moreover, we derive a set of theoretical guarantees for our sanity-checked image registration method, with experimental results supporting our theoretical findings and their effectiveness in increasing the sanity of models without sacrificing any performance.
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation
Additionally, based on the same intuition, we propose a 2-Step method, which takes both conversation and summary as input to simulate human annotation process.
ReactIE: Enhancing Chemical Reaction Extraction with Weak Supervision
Structured chemical reaction information plays a vital role for chemists engaged in laboratory work and advanced endeavors such as computer-aided drug design.
Mimicking the Thinking Process for Emotion Recognition in Conversation with Prompts and Paraphrasing
It is a challenging task since the recognition of the emotion in one utterance involves many complex factors, such as the conversational context, the speaker's background, and the subtle difference between emotion labels.
Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation
Instruction tuning has emerged to enhance the capabilities of large language models (LLMs) to comprehend instructions and generate appropriate responses.
Open-Vocabulary Argument Role Prediction for Event Extraction
Specifically, we formulate the role prediction problem as an in-filling task and construct prompts for a pre-trained language model to generate candidate roles.
Towards a Unified Multi-Dimensional Evaluator for Text Generation
We re-frame NLG evaluation as a Boolean Question Answering (QA) task, and by guiding the model with different questions, we can use one evaluator to evaluate from multiple dimensions.
COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization
Traditional training paradigms for extractive and abstractive summarization systems always only use token-level or sentence-level training objectives.
Data-driven soliton mappings for integrable fractional nonlinear wave equations via deep learning with Fourier neural operator
The results obtained in this paper may be useful to further understand the neural networks in the fractional integrable nonlinear wave systems and the mappings between two spaces.
Learning Interaction Variables and Kernels from Observations of Agent-Based Systems
Dynamical systems across many disciplines are modeled as interacting particles or agents, with interaction rules that depend on a very small number of variables (e. g. pairwise distances, pairwise differences of phases, etc...), functions of the state of pairs of agents.
CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision
Scientific extreme summarization (TLDR) aims to form ultra-short summaries of scientific papers.
Ranked #1 on
Extreme Summarization
on CiteSum
PREME: Preference-based Meeting Exploration through an Interactive Questionnaire
The recent increase in the volume of online meetings necessitates automated tools for managing and organizing the material, especially when an attendee has missed the discussion and needs assistance in quickly exploring it.
The Cross-lingual Conversation Summarization Challenge
We propose the shared task of cross-lingual conversation summarization, \emph{ConvSumX Challenge}, opening new avenues for researchers to investigate solutions that integrate conversation summarization and machine translation.
Abstractive Dialogue Summarization
Cross-Lingual Abstractive Summarization
+3
Unsupervised Multi-Granularity Summarization
In this paper, we propose the first unsupervised multi-granularity summarization framework, GranuSum.
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.
Ranked #1 on
Task-Oriented Dialogue Systems
on KVRET
An Improved Composite Functional Gradient Learning by Wasserstein Regularization for Generative adversarial networks
However, the discriminator using logistic regression from the CFG framework is gradually hard to discriminate between real and fake images while the training steps go on.
RetrievalSum: A Retrieval Enhanced Framework for Abstractive Summarization
Existing summarization systems mostly generate summaries purely relying on the content of the source document.
DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization
For a dialogue, it corrupts a window of text with dialogue-inspired noise, and guides the model to reconstruct this window based on the content of the remaining conversation.
Machine Learning for Discovering Effective Interaction Kernels between Celestial Bodies from Ephemerides
Building accurate and predictive models of the underlying mechanisms of celestial motion has inspired fundamental developments in theoretical physics.
GEM: A General Evaluation Benchmark for Multimodal Tasks
Comparing with existing multimodal datasets such as MSCOCO and Flicker30K for image-language tasks, YouCook2 and MSR-VTT for video-language tasks, GEM is not only the largest vision-language dataset covering image-language tasks and video-language tasks at the same time, but also labeled in multiple languages.
CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval
In this paper, we propose a CLIP4Clip model to transfer the knowledge of the CLIP model to video-language retrieval in an end-to-end manner.
Ranked #1 on
Text to Video Retrieval
on MSR-VTT
QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization
As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed.
Enhancing Scientific Papers Summarization with Citation Graph
Previous work for text summarization in scientific domain mainly focused on the content of the input document, but seldom considering its citation network.
Dynamic Network Embedding Survey
Due to the lack of comprehensive investigation of them, we give a survey of dynamic network embedding in this paper.
Learning Interaction Kernels for Agent Systems on Riemannian Manifolds
Interacting agent and particle systems are extensively used to model complex phenomena in science and engineering.
CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems
In this paper, we perform an in-depth analysis of characteristics of different datasets and investigate the performance of different summarization models under a cross-dataset setting, in which a summarizer trained on one corpus will be evaluated on a range of out-of-domain corpora.
Learning Theory for Inferring Interaction Kernels in Second-Order Interacting Agent Systems
Modeling the complex interactions of systems of particles or agents is a fundamental scientific and mathematical problem that is studied in diverse fields, ranging from physics and biology, to economics and machine learning.
Extractive Summarization as Text Matching
This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems.
Ranked #1 on
Text Summarization
on BBC XSum
Data-driven Discovery of Emergent Behaviors in Collective Dynamics
We consider the fundamental problem of inferring interaction kernels from observations of agent-based dynamical systems given observations of trajectories, in particular for collective dynamical systems exhibiting emergent behaviors with complicated interaction kernels, in a nonparametric fashion, and for kernels which are parametrized by a single unknown parameter.
A Closer Look at Data Bias in Neural Extractive Summarization Models
In this paper, we take stock of the current state of summarization datasets and explore how different factors of datasets influence the generalization behaviour of neural extractive summarization models.
GDP: Generalized Device Placement for Dataflow Graphs
Runtime and scalability of large neural networks can be significantly affected by the placement of operations in their dataflow graphs on suitable devices.
Exploring Domain Shift in Extractive Text Summarization
Although domain shift has been well explored in many NLP applications, it still has received little attention in the domain of extractive text summarization.
Searching for Effective Neural Extractive Summarization: What Works and What's Next
The recent years have seen remarkable success in the use of deep neural networks on text summarization.
Ranked #6 on
Extractive Text Summarization
on CNN / Daily Mail
Nonparametric inference of interaction laws in systems of agents from trajectory data
Inferring the laws of interaction between particles and agents in complex dynamical systems from observational data is a fundamental challenge in a wide variety of disciplines.
Feature Selection with Redundancy-complementariness Dispersion
Feature selection has attracted significant attention in data mining and machine learning in the past decades.
