Search Results for author:
Found 13 papers, 6 papers with code
Self-trained Pretrained Language Models for Evidence Detection
We provide a thorough investigation on how to utilize pseudo labels effectively in the self-training scheme.
Exploring Multitask Learning for Low-Resource Abstractive Summarization
In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target task of abstractive summarization via multitask learning.
ReflectSumm: A Benchmark for Course Reflection Summarization
This paper introduces ReflectSumm, a novel summarization dataset specifically designed for summarizing students' reflective writing.
Overview of ImageArg-2023: The First Shared Task in Multimodal Argument Mining
This paper presents an overview of the ImageArg shared task, the first multimodal Argument Mining shared task co-located with the 10th Workshop on Argument Mining at EMNLP 2023.
Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Models
Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP).
Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking
We propose a simple approach for the abstractive summarization of long legal opinions that considers the argument structure of the document.
ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining
A challenging task when generating summaries of legal documents is the ability to address their argumentative nature.
Exploring Multitask Learning for Low-Resource AbstractiveSummarization
In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target task of abstractive summarization via multitask learning.
A Character Level Convolutional BiLSTM for Arabic Dialect Identification
In this paper, we describe CU-RAISA teamcontribution to the 2019Madar shared task2, which focused on Twitter User fine-grained dialect identification. Among par-ticipating teams, our system ranked the4th(with 61. 54{\%}) F1-Macro measure. Our sys-tem is trained using a character level convo-lutional bidirectional long-short-term memorynetwork trained on 2k users{'} data.
UBC-NLP at IEST 2018: Learning Implicit Emotion With an Ensemble of Language Models
We also offer an analysis of system performance and the impact of training data size on the task.
Deep Models for Arabic Dialect Identification on Benchmarked Data
We treat these two limitations:We (1) benchmark the data, and (2) empirically test6different deep learning methods on thetask, comparing peformance to several classical machine learning models under different condi-tions (i. e., both binary and multi-way classification).
An Unsupervised Speaker Clustering Technique based on SOM and I-vectors for Speech Recognition Systems
The proposed technique is mainly based on I-vectors and Self-Organizing Map Neural Network(SOM). The input to the proposed algorithm is a set of speech utterances.
