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Found 22 papers, 3 papers with code
ABSA-Bench: Towards the Unified Evaluation of Aspect-based Sentiment Analysis Research
Aspect-Based Sentiment Analysis (ABSA)has gained much attention in recent years.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Distractor Generation for Multiple-Choice Questions: A Survey of Methods, Datasets, and Evaluation
Distractors are important in learning evaluation.
When Large Language Models Meet Citation: A Survey
Such information could be incorporated into LLMs pre-training and improve the text representation in LLMs.
SWAP: Exploiting Second-Ranked Logits for Adversarial Attacks on Time Series
Time series classification (TSC) has emerged as a critical task in various domains, and deep neural models have shown superior performance in TSC tasks.
Learning to Select the Relevant History Turns in Conversational Question Answering
In this paper, we propose a framework, DHS-ConvQA (Dynamic History Selection in Conversational Question Answering), that first generates the context and question entities for all the history turns, which are then pruned on the basis of similarity they share in common with the question at hand.
Keeping the Questions Conversational: Using Structured Representations to Resolve Dependency in Conversational Question Answering
However, these sequential questions are sometimes left implicit and thus require the resolution of some natural language phenomena such as anaphora and ellipsis.
The Exploration of Knowledge-Preserving Prompts for Document Summarisation
Despite the great development of document summarisation techniques nowadays, factual inconsistencies between the generated summaries and the original texts still occur from time to time.
Document-aware Positional Encoding and Linguistic-guided Encoding for Abstractive Multi-document Summarization
One effective way is to encode document positional information to assist models in capturing cross-document relations.
Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations
Although deep neural networks have achieved state-of-the-art performance in various machine learning tasks, adversarial examples, constructed by adding small non-random perturbations to correctly classified inputs, successfully fool highly expressive deep classifiers into incorrect predictions.
Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures
Knowledge-aware methods have boosted a range of natural language processing applications over the last decades.
Dependency Structure for News Document Summarization
In this work, we develop a neural network based model which leverages dependency parsing to capture cross-positional dependencies and grammatical structures.
Conversational Question Answering: A Survey
Question answering (QA) systems provide a way of querying the information available in various formats including, but not limited to, unstructured and structured data in natural languages.
A Review of the Non-Invasive Techniques for Monitoring Different Aspects of Sleep
In this paper, we present a comprehensive survey of the latest research works (2015 and after) conducted in various categories of sleep monitoring including sleep stage classification, sleep posture recognition, sleep disorders detection, and vital signs monitoring.
A Short Survey of Pre-trained Language Models for Conversational AI-A NewAge in NLP
Building a dialogue system that can communicate naturally with humans is a challenging yet interesting problem of agent-based computing.
Aspect Extraction Using Coreference Resolution and Unsupervised Filtering
Aspect extraction is a widely researched field of natural language processing in which aspects are identified from the text as a means for information.
Multi-document Summarization via Deep Learning Techniques: A Survey
Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents.
Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering
However, there are few works studying the data augmentation problem for VQA and none of the existing image based augmentation schemes (such as rotation and flipping) can be directly applied to VQA due to its semantic structure -- an $\langle image, question, answer\rangle$ triplet needs to be maintained correctly.
Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems
Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods.
Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems
In recent years, the emerging topics of recommender systems that take advantage of natural language processing techniques have attracted much attention, and one of their applications is the Conversational Recommender System (CRS).
Different Approaches for Human Activity Recognition: A Survey
We further divide these areas into ten different sub-topics and present the latest research work in these sub-topics.
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey
In this article, we review research works that address this difference and generatetextual adversarial examples on DNNs.
Deep Autoencoder for Recommender Systems: Parameter Influence Analysis
Specifically, we find that DAE parameters strongly affect the prediction accuracy of the recommender systems, and the effect is transferable to similar datasets in a larger size.
