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Found 22 papers, 10 papers with code
Navigating the Path of Writing: Outline-guided Text Generation with Large Language Models
Large Language Models (LLMs) have significantly impacted the writing process, enabling collaborative content creation and enhancing productivity.
CheckEval: Robust Evaluation Framework using Large Language Model via Checklist
We introduce CheckEval, a novel evaluation framework using Large Language Models, addressing the challenges of ambiguity and inconsistency in current evaluation methods.
Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class Classification
Recently, prompt-based fine-tuning has garnered considerable interest as a core technique for few-shot text classification task.
RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level information
We introduce RAPID, a model that capitalizes on the inherent features of log data to enable anomaly detection without training delays, ensuring real-time capability.
Which is better? Exploring Prompting Strategy For LLM-based Metrics
With advanced Large Language Models (LLMs) such as GPT-4, evaluating the quality of Natural Language Generation (NLG) has become increasingly paramount.
Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews
As the e-commerce market continues to expand and online transactions proliferate, customer reviews have emerged as a critical element in shaping the purchasing decisions of prospective buyers.
Multi-Task Self-Supervised Time-Series Representation Learning
We propose a new time-series representation learning method by combining the advantages of self-supervised tasks related to contextual, temporal, and transformation consistency.
DSTEA: Improving Dialogue State Tracking via Entity Adaptive Pre-training
Dialogue State Tracking (DST) is critical for comprehensively interpreting user and system utterances, thereby forming the cornerstone of efficient dialogue systems.
REVECA -- Rich Encoder-decoder framework for Video Event CAptioner
We designed a Rich Encoder-decoder framework for Video Event CAptioner (REVECA) that utilizes spatial and temporal information from the video to generate a caption for the corresponding the event boundary.
AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-Decoder
Therefore, current image anomaly detection methods have commonly used convolutional encoder-decoders to extract normal information through the local features of images.
Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking
Dialogue state tracking (DST) aims to extract essential information from multi-turn dialogue situations and take appropriate actions.
Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection
Predictive coding is further introduced to encourage the model to learn the temporal dependencies of the time series.
LAnoBERT: System Log Anomaly Detection based on BERT Masked Language Model
The system log generated in a computer system refers to large-scale data that are collected simultaneously and used as the basic data for determining errors, intrusion and abnormal behaviors.
K-Wav2vec 2.0: Automatic Speech Recognition based on Joint Decoding of Graphemes and Syllables
Wav2vec 2. 0 is an end-to-end framework of self-supervised learning for speech representation that is successful in automatic speech recognition (ASR), but most of the work on the topic has been developed with a single language: English.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Oh My Mistake!: Toward Realistic Dialogue State Tracking including Turnback Utterances
The primary purpose of dialogue state tracking (DST), a critical component of an end-to-end conversational system, is to build a model that responds well to real-world situations.
Back-Translated Task Adaptive Pretraining: Improving Accuracy and Robustness on Text Classification
Language models (LMs) pretrained on a large text corpus and fine-tuned on a downstream text corpus and fine-tuned on a downstream task becomes a de facto training strategy for several natural language processing (NLP) tasks.
Multi\^2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT
In this paper, we propose Multi$^2$OIE, which performs open information extraction (open IE) by combining BERT with multi-head attention.
Multi$^2$OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT
In this paper, we propose Multi$^2$OIE, which performs open information extraction (open IE) by combining BERT with multi-head attention.
Ranked #7 on
Open Information Extraction
on CaRB
Sentence transition matrix: An efficient approach that preserves sentence semantics
Sentence embedding is a significant research topic in the field of natural language processing (NLP).
Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition
However, because the performances of sentence classification and sentiment analysis can be enhanced by using a simple sentence representation method, it is not sufficient to claim that these models fully reflect the meanings of sentences based on good performances for such tasks.
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning
In this paper, we propose a new recurrent neural network (RNN)-based Seq2seq model, RNN semantic variational autoencoder (RNN--SVAE), to better capture the global latent information of a sequence of words.
Sentiment Classification with Word Attention based on Weakly Supervised Learning with a Convolutional Neural Network
In order to maximize the applicability of sentiment analysis results, it is necessary to not only classify the overall sentiment (positive/negative) of a given document but also to identify the main words that contribute to the classification.
