Papers Task 2
“Task 2” 태그가 달린 논문 572편 · 필터 해제
Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation
With the recent advances of large language models (LLMs), it is no longer infeasible to build an automated debate system that helps people to synthesise persuasive arguments. Previous work attempted this task by integrat…
Argument MiningMetric LearningTask 2SCE-MAE: Selective Correspondence Enhancement with Masked Autoencoder for Self-Supervised Landmark Estimation
Self-supervised landmark estimation is a challenging task that demands the formation of locally distinct feature representations to identify sparse facial landmarks in the absence of annotated data. To tackle this task, …
Self-Supervised LearningTask 2Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attention
When reading temporarily ambiguous garden-path sentences, misinterpretations sometimes linger past the point of disambiguation. This phenomenon has traditionally been studied in psycholinguistic experiments using online …
Question AnsweringSentenceTask 2D-NLP at SemEval-2024 Task 2: Evaluating Clinical Inference Capabilities of Large Language Models
Large language models (LLMs) have garnered significant attention and widespread usage due to their impressive performance in various tasks. However, they are not without their own set of challenges, including issues such…
MiscellaneousNatural Language InferenceNatural Language UnderstandingTask 2CRCL at SemEval-2024 Task 2: Simple prompt optimizations
We present a baseline for the SemEval 2024 task 2 challenge, whose objective is to ascertain the inference relationship between pairs of clinical trial report sections and statements. We apply prompt optimization techniq…
Language ModelingLanguage ModellingTask 2DFKI-NLP at SemEval-2024 Task 2: Towards Robust LLMs Using Data Perturbations and MinMax Training
The NLI4CT task at SemEval-2024 emphasizes the development of robust models for Natural Language Inference on Clinical Trial Reports (CTRs) using large language models (LLMs). This edition introduces interventions specif…
Natural Language InferenceTask 2TLDR at SemEval-2024 Task 2: T5-generated clinical-Language summaries for DeBERTa Report Analysis
This paper introduces novel methodologies for the Natural Language Inference for Clinical Trials (NLI4CT) task. We present TLDR (T5-generated clinical-Language summaries for DeBERTa Report Analysis) which incorporates T5…
Natural Language InferenceTask 2DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness
Safe and reliable natural language inference is critical for extracting insights from clinical trial reports but poses challenges due to biases in large pre-trained language models. This paper presents a novel data augme…
Data AugmentationDiversityMulti-Task LearningNatural Language Inference+1Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
Feed recommendation is currently the mainstream mode for many real-world applications (e.g., TikTok, Dianping), it is usually necessary to model and predict user interests in multiple scenarios (domains) within and even …
Feature EngineeringTask 2Enhancing Lip Reading with Multi-Scale Video and Multi-Encoder
Automatic lip-reading (ALR) aims to automatically transcribe spoken content from a speaker's silent lip motion captured in video. Current mainstream lip-reading approaches only use a single visual encoder to model input …
LipreadingLip ReadingTask 2SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials
Large Language Models (LLMs) are at the forefront of NLP achievements but fall short in dealing with shortcut learning, factual inconsistency, and vulnerability to adversarial inputs.These shortcomings are especially cri…
Decision MakingNatural Language InferenceTask 2IITK at SemEval-2024 Task 2: Exploring the Capabilities of LLMs for Safe Biomedical Natural Language Inference for Clinical Trials
Large Language models (LLMs) have demonstrated state-of-the-art performance in various natural language processing (NLP) tasks across multiple domains, yet they are prone to shortcut learning and factual inconsistencies.…
Natural Language InferenceRAGRetrievalRetrieval-augmented Generation+1SEME at SemEval-2024 Task 2: Comparing Masked and Generative Language Models on Natural Language Inference for Clinical Trials
This paper describes our submission to Task 2 of SemEval-2024: Safe Biomedical Natural Language Inference for Clinical Trials. The Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT) consists of a …
Natural Language InferenceTask 2Edinburgh Clinical NLP at SemEval-2024 Task 2: Fine-tune your model unless you have access to GPT-4
The NLI4CT task assesses Natural Language Inference systems in predicting whether hypotheses entail or contradict evidence from Clinical Trial Reports. In this study, we evaluate various Large Language Models (LLMs) with…
In-Context LearningLanguage ModellingNatural Language Inferenceparameter-efficient fine-tuning+2Asymmetric and trial-dependent modeling: the contribution of LIA to SdSV Challenge Task 2
The SdSv challenge Task 2 provided an opportunity to assess efficiency and robustness of modern text-independent speaker verification systems. But it also made it possible to test new approaches, capable of taking into a…
Speaker RecognitionSpeaker VerificationTask 2Text-Independent Speaker VerificationThe Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model
In this paper, we propose a deep learning based model for Acoustic Anomaly Detection of Machines, the task for detecting abnormal machines by analysing the machine sound. By conducting extensive experiments, we indicate …
Anomaly DetectionData AugmentationFeature EngineeringTask 2M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection
The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels. This raises legitimate concerns about its potential misuse and societal implications.…
Task 2Text DetectionMOD-CL: Multi-label Object Detection with Constrained Loss
We introduce MOD-CL, a multi-label object detection framework that utilizes constrained loss in the training process to produce outputs that better satisfy the given requirements. In this paper, we use $\mathrm{MOD_{YOLO…
Objectobject-detectionObject DetectionTask 2MasonTigers@LT-EDI-2024: An Ensemble Approach Towards Detecting Homophobia and Transphobia in Social Media Comments
In this paper, we describe our approaches and results for Task 2 of the LT-EDI 2024 Workshop, aimed at detecting homophobia and/or transphobia across ten languages. Our methodologies include monolingual transformers and …
Task 2ConvConcatNet: a deep convolutional neural network to reconstruct mel spectrogram from the EEG
To investigate the processing of speech in the brain, simple linear models are commonly used to establish a relationship between brain signals and speech features. However, these linear models are ill-equipped to model a…
EEGTask 2