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Found 12 papers, 3 papers with code
GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution
Learning from multimodal data has become a popular research topic in recent years.
netFound: Foundation Model for Network Security
In ML for network security, traditional workflows rely on high-quality labeled data and manual feature engineering, but limited datasets and human expertise hinder feature selection, leading to models struggling to capture crucial relationships and generalize effectively.
Mitigating Bias for Question Answering Models by Tracking Bias Influence
Based on the intuition that a model would lean to be more biased if it learns from a biased example, we measure the bias level of a query instance by observing its influence on another instance.
On Compositionality and Improved Training of NADO
NeurAlly-Decomposed Oracle (NADO) is a powerful approach for controllable generation with large language models.
In Search of netUnicorn: A Data-Collection Platform to Develop Generalizable ML Models for Network Security Problems
This issue is commonly referred to as the generalizability problem of ML models.
Challenges and Opportunities for Beyond-5G Wireless Security
The demand for broadband wireless access is driving research and standardization of 5G and beyond-5G wireless systems.
Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning
Dialogue state tracking (DST) is an important step in dialogue management to keep track of users' beliefs.
A dataset for resolving referring expressions in spoken dialogue via contextual query rewrites (CQR)
In this paper, we describe our methodology for creating the query reformulation extension to the dialog corpus, and present an initial set of experiments to establish a baseline for the CQR task.
Scaling Multi-Domain Dialogue State Tracking via Query Reformulation
We present a novel approach to dialogue state tracking and referring expression resolution tasks.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
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Cross-Lingual Approaches to Reference Resolution in Dialogue Systems
In the cross-lingual setup, we assume there is access to annotated resources as well as a well trained model in the source language and little to no annotated data in the target language.
Contextual Slot Carryover for Disparate Schemas
In the slot-filling paradigm, where a user can refer back to slots in the context during a conversation, the goal of the contextual understanding system is to resolve the referring expressions to the appropriate slots in the context.
Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding
This paper presents the design of the machine learning architecture that underlies the Alexa Skills Kit (ASK) a large scale Spoken Language Understanding (SLU) Software Development Kit (SDK) that enables developers to extend the capabilities of Amazon's virtual assistant, Alexa.
