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Found 31 papers, 7 papers with code
Energy-based Neural Modelling for Large-Scale Multiple Domain Dialogue State Tracking
Scaling up dialogue state tracking to multiple domains is challenging due to the growth in the number of variables being tracked.
Domain-Informed Probing of wav2vec 2.0 Embeddings for Phonetic Features
In recent years large transformer model architectures have become available which provide a novel means of generating high-quality vector representations of speech audio.
Topic Aware Probing: From Sentence Length Prediction to Idiom Identification how reliant are Neural Language Models on Topic?
Transformer-based Neural Language Models achieve state-of-the-art performance on various natural language processing tasks.
Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies
These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time.
TWIG: Towards pre-hoc Hyperparameter Optimisation and Cross-Graph Generalisation via Simulated KGE Models
Our experiments on the UMLS dataset show that a single TWIG neural network can predict the results of state-of-the-art ComplEx-N3 KGE model nearly exactly on across all hyperparameter configurations.
Hierarchical Framework for Interpretable and Probabilistic Model-Based Safe Reinforcement Learning
Deep reinforcement learning has been the pioneer for solving this problem without the need for relying on the physical model of complex systems by just interacting with it.
Ranked #1 on
Decision Making
on NASA C-MAPSS
Specialized Deep Residual Policy Safe Reinforcement Learning-Based Controller for Complex and Continuous State-Action Spaces
Deep reinforcement learning has the potential to address these problems by learning optimal control policies through exploration in an environment.
Missing Information, Unresponsive Authors, Experimental Flaws: The Impossibility of Assessing the Reproducibility of Previous Human Evaluations in NLP
We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining what makes human evaluations in NLP more/less reproducible.
Idioms, Probing and Dangerous Things: Towards Structural Probing for Idiomaticity in Vector Space
The goal of this paper is to learn more about how idiomatic information is structurally encoded in embeddings, using a structural probing method.
Adaptive Machine Translation with Large Language Models
By feeding an LLM at inference time with a prompt that consists of a list of translation pairs, it can then simulate the domain and style characteristics.
Probing Taxonomic and Thematic Embeddings for Taxonomic Information
Modelling taxonomic and thematic relatedness is important for building AI with comprehensive natural language understanding.
Probing with Noise: Unpicking the Warp and Weft of Embeddings
Improving our understanding of how information is encoded in vector space can yield valuable interpretability insights.
Domain-Specific Text Generation for Machine Translation
Preservation of domain knowledge from the source to target is crucial in any translation workflow.
Interpretable Hidden Markov Model-Based Deep Reinforcement Learning Hierarchical Framework for Predictive Maintenance of Turbofan Engines
An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain.
Detecting Interlocutor Confusion in Situated Human-Avatar Dialogue: A Pilot Study
To this end, in this paper, we present our initial research centred on a user-avatar dialogue scenario that we have developed to study the manifestation of confusion and in the long term its mitigation.
Mutual Information Decay Curves and Hyper-Parameter Grid Search Design for Recurrent Neural Architectures
We present an approach to design the grid searches for hyper-parameter optimization for recurrent neural architectures.
Language-Driven Region Pointer Advancement for Controllable Image Captioning
A vital component of the Controllable Image Captioning architecture is the mechanism that decides the timing of attending to each region through the advancement of a region pointer.
Semantic Relatedness and Taxonomic Word Embeddings
This paper connects a series of papers dealing with taxonomic word embeddings.
Bigger versus Similar: Selecting a Background Corpus for First Story Detection Based on Distributional Similarity
Using these metrics we rank different background corpora relative to a target corpus.
Capturing Dialogue State Variable Dependencies with an Energy-based Neural Dialogue State Tracker
In this paper we argue that treating the prediction of each slot value as an independent prediction task may ignore important associations between the slot values, and, consequently, we argue that treating dialogue state tracking as a structured prediction problem can help to improve dialogue state tracking performance.
Multi-Element Long Distance Dependencies: Using SPk Languages to Explore the Characteristics of Long-Distance Dependencies
In order to successfully model Long Distance Dependencies (LDDs) it is necessary to understand the full-range of the characteristics of the LDDs exhibited in a target dataset.
TEST: A Terminology Extraction System for Technology Related Terms
This tool is used to automatically detect the existence of new technologies and tools in text, and extract terms used to describe these new technologies.
Generating Diverse and Meaningful Captions
Image Captioning is a task that requires models to acquire a multi-modal understanding of the world and to express this understanding in natural language text.
Persistence pays off: Paying Attention to What the LSTM Gating Mechanism Persists
Language Models (LMs) are important components in several Natural Language Processing systems.
Exploring the Use of Attention within an Neural Machine Translation Decoder States to Translate Idioms
Idioms pose problems to almost all Machine Translation systems.
Understanding Recurrent Neural Architectures by Analyzing and Synthesizing Long Distance Dependencies in Benchmark Sequential Datasets
In this paper, we presentdetailed analysis of the dependency decay curve exhibited by various datasets.
Using Regular Languages to Explore the Representational Capacity of Recurrent Neural Architectures
However, one of the drawbacks of existing datasets is the lack of experimental control with regards to the presence and/or degree of LDDs.
What is not where: the challenge of integrating spatial representations into deep learning architectures
This paper examines to what degree current deep learning architectures for image caption generation capture spatial language.
Modular Mechanistic Networks: On Bridging Mechanistic and Phenomenological Models with Deep Neural Networks in Natural Language Processing
Natural language processing (NLP) can be done using either top-down (theory driven) and bottom-up (data driven) approaches, which we call mechanistic and phenomenological respectively.
Is it worth it? Budget-related evaluation metrics for model selection
Creating a linguistic resource is often done by using a machine learning model that filters the content that goes through to a human annotator, before going into the final resource.
