Search Results for author:
Found 33 papers, 11 papers with code
Multilingual Text Summarization on Financial Documents
The language models were fine-tuned on a financial document collection of three languages (English, Spanish, and Greek) and aim to identify the beginning of the summary narrative part of the document.
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants
We investigate the potential scale of this vulnerability by measuring the degree to which AI assistants can complete assessment questions in standard university-level STEM courses.
Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience
Federated learning (FL) has recently emerged as a compelling machine learning paradigm, prioritizing the protection of privacy for training data.
LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters
The rapid expansion of large language models (LLMs) has underscored the need for parameter-efficient fine-tuning methods, with LoRA (Low-Rank Adaptation) emerging as a popular solution.
Stance Detection on Social Media with Fine-Tuned Large Language Models
This study emphasizes the potential of LLMs in stance detection and calls for more extensive research in this field.
CRAB: Assessing the Strength of Causal Relationships Between Real-world Events
In this work, we present CRAB, a new Causal Reasoning Assessment Benchmark designed to evaluate causal understanding of events in real-world narratives.
Breaking the Language Barrier: Improving Cross-Lingual Reasoning with Structured Self-Attention
We evaluate the cross-lingual reasoning abilities of MultiLMs in two schemes: (1) where the language of the context and the question remain the same in the new languages that are tested (i. e., the reasoning is still monolingual, but the model must transfer the learned reasoning ability across languages), and (2) where the language of the context and the question is different (which we term code-switched reasoning).
Stop Pre-Training: Adapt Visual-Language Models to Unseen Languages
Our evaluation across three distinct tasks (image-text retrieval, visual entailment, and natural language visual reasoning) demonstrates that this approach outperforms the state-of-the-art multilingual vision-language models without requiring large parallel corpora.
Revisiting Offline Compression: Going Beyond Factorization-based Methods for Transformer Language Models
Recent transformer language models achieve outstanding results in many natural language processing (NLP) tasks.
Beyond S-curves: Recurrent Neural Networks for Technology Forecasting
Our autoencoder approach improves the MAPE by 13. 5% on average over the second-best result.
An Efficient Active Learning Pipeline for Legal Text Classification
However, most AL strategies require a set of labeled samples to start with, which is expensive to acquire.
Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction
Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data.
Discovering Language-neutral Sub-networks in Multilingual Language Models
Multilingual pre-trained language models transfer remarkably well on cross-lingual downstream tasks.
From Scattered Sources to Comprehensive Technology Landscape: A Recommendation-based Retrieval Approach
This is a two-task setup involving (i) technology classification of entities extracted from company corpus, and (ii) technology and company retrieval based on classified technologies.
SciClops: Detecting and Contextualizing Scientific Claims for Assisting Manual Fact-Checking
This paper describes SciClops, a method to help combat online scientific misinformation.
Legal Transformer Models May Not Always Help
This work investigates the value of domain adaptive pre-training and language adapters in legal NLP tasks.
Direction is what you need: Improving Word Embedding Compression in Large Language Models
The adoption of Transformer-based models in natural language processing (NLP) has led to great success using a massive number of parameters.
On Representation Learning for Scientific News Articles Using Heterogeneous Knowledge Graphs
In this work, we present a methodology for creating scientific news article representations by modeling the directed graph between the scientific news articles and the cited scientific publications.
SciLens News Platform: A System for Real-Time Evaluation of News Articles
We demonstrate the SciLens News Platform, a novel system for evaluating the quality of news articles.
Spoken dialect identification in Twitter using a multi-filter architecture
This paper presents our approach for SwissText & KONVENS 2020 shared task 2, which is a multi-stage neural model for Swiss German (GSW) identification on Twitter.
Upgrading the Newsroom: An Automated Image Selection System for News Articles
The system fuses multiple textual sources extracted from news articles and accepts multilingual inputs.
On Node Features for Graph Neural Networks
Graph neural network (GNN) is a deep model for graph representation learning.
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task
In this paper, we propose a new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages.
Unsupervised Learning of Node Embeddings by Detecting Communities
We provide empirical evidence that the communities learned by DMC are meaningful and that the node embeddings are competitive in different node classification benchmarks.
Parallel Computation of Graph Embeddings
We therefore propose a framework for parallel computation of a graph embedding using a cluster of compute nodes with resource constraints.
SciLens: Evaluating the Quality of Scientific News Articles Using Social Media and Scientific Literature Indicators
Based on these aspects, we describe a series of indicators of news quality.
Weakly Supervised Active Learning with Cluster Annotation
In this work, we introduce a novel framework that employs cluster annotation to boost active learning by reducing the number of human interactions required to train deep neural networks.
Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning
Companies, users, researchers, and regulators still lack usable and scalable tools to cope with the breadth and depth of privacy policies.
Multimodal Classification for Analysing Social Media
Although information on social media can be of different modalities such as texts, images, audio or videos, traditional approaches in classification usually leverage only one prominent modality.
280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification
We propose a simple, yet effective, approach towards inducing multilingual taxonomies from Wikipedia.
Taxonomy Induction using Hypernym Subsequences
We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms.
Robust Online Time Series Prediction with Recurrent Neural Networks
Time series forecasting for streaming data plays an important role in many real applications, ranging from IoT systems, cyber-networks, to industrial systems and healthcare.
Matching Demand with Supply in the Smart Grid using Agent-Based Multiunit Auction
Recent work has suggested reducing electricity generation cost by cutting the peak to average ratio (PAR) without reducing the total amount of the loads.
