Search Results for author:
Found 40 papers, 19 papers with code
Beyond Emotion: A Multi-Modal Dataset for Human Desire Understanding
Desire is a strong wish to do or have something, which involves not only a linguistic expression, but also underlying cognitive phenomena driving human feelings.
Recommending Target Actions Outside Sessions in the Data-poor Insurance Domain
Our models cope with data scarcity by learning from multiple sessions and different types of user actions.
Query Augmentation by Decoding Semantics from Brain Signals
If the quality of the initially retrieved documents is low, then the effectiveness of query augmentation would be limited as well.
Investigating the Impact of Model Instability on Explanations and Uncertainty
In this large-scale empirical study, we insert different levels of noise perturbations and measure the effect on the output of pre-trained language models and different uncertainty metrics.
Digital-analog hybrid matrix multiplication processor for optical neural networks
Here, we propose a digital-analog hybrid optical computing architecture for ONNs, which utilizes digital optical inputs in the form of binary words.
Language Generation from Brain Recordings
Inspired by recent research that revealed associations between the brain and the large computational language models, we propose a generative language BCI that utilizes the capacity of a large language model (LLM) jointly with a semantic brain decoder to directly generate language from functional magnetic resonance imaging (fMRI) input.
Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study
To our knowledge, this is the first critical comparison of individual item fairness measures in recommender systems.
Faithfulness Tests for Natural Language Explanations
Explanations of neural models aim to reveal a model's decision-making process for its predictions.
Adapting Pre-trained Language Models for Quantum Natural Language Processing
The emerging classical-quantum transfer learning paradigm has brought a decent performance to quantum computational models in many tasks, such as computer vision, by enabling a combination of quantum models and classical pre-trained neural networks.
Graph-based Recommendation for Sparse and Heterogeneous User Interactions
We propose a graph-based recommender model which utilizes heterogeneous interactions between users and content of different types and is able to operate well on small-scale datasets.
Template-based Recruitment Email Generation For Job Recommendation
Text generation has long been a popular research topic in NLP.
Principled Multi-Aspect Evaluation Measures of Rankings
Information Retrieval evaluation has traditionally focused on defining principled ways of assessing the relevance of a ranked list of documents with respect to a query.
Learning Recommendations from User Actions in the Item-poor Insurance Domain
To address this, we present a recurrent neural network recommendation model that uses past user sessions as signals for learning recommendations.
Fact Checking with Insufficient Evidence
To this end, we are the first to study what information FC models consider sufficient by introducing a novel task and advancing it with three main contributions.
Diagnostics-Guided Explanation Generation
When such annotations are not available, explanations are often selected as those portions of the input that maximise a downstream task's performance, which corresponds to optimising an explanation's Faithfulness to a given model.
Projected Hamming Dissimilarity for Bit-Level Importance Coding in Collaborative Filtering
While this is highly efficient, each bit dimension is equally weighted, which means that potentially discriminative information of the data is lost.
Unsupervised Multi-Index Semantic Hashing
In this work, we propose Multi-Index Semantic Hashing (MISH), an unsupervised hashing model that learns hash codes that are both effective and highly efficient by being optimized for multi-index hashing.
On Position Embeddings in BERT
Various Position Embeddings (PEs) have been proposed in Transformer based architectures~(e. g. BERT) to model word order.
Multi-Head Self-Attention with Role-Guided Masks
The state of the art in learning meaningful semantic representations of words is the Transformer model and its attention mechanisms.
Denmark's Participation in the Search Engine TREC COVID-19 Challenge: Lessons Learned about Searching for Precise Biomedical Scientific Information on COVID-19
This report describes the participation of two Danish universities, University of Copenhagen and Aalborg University, in the international search engine competition on COVID-19 (the 2020 TREC-COVID Challenge) organised by the U. S. National Institute of Standards and Technology (NIST) and its Text Retrieval Conference (TREC) division.
A Diagnostic Study of Explainability Techniques for Text Classification
Recent developments in machine learning have introduced models that approach human performance at the cost of increased architectural complexity.
Unsupervised Semantic Hashing with Pairwise Reconstruction
Inspired by this, we present Semantic Hashing with Pairwise Reconstruction (PairRec), which is a discrete variational autoencoder based hashing model.
Factuality Checking in News Headlines with Eye Tracking
We study whether it is possible to infer if a news headline is true or false using only the movement of the human eyes when reading news headlines.
Content-aware Neural Hashing for Cold-start Recommendation
NeuHash-CF is modelled as an autoencoder architecture, consisting of two joint hashing components for generating user and item hash codes.
Generating Fact Checking Explanations
Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims.
Encoding word order in complex embeddings
The benefit of continuous functions over variable positions is that word representations shift smoothly with increasing positions.
Variational Hashing-based Collaborative Filtering with Self-Masking
To this end, we propose an end-to-end trainable variational hashing-based collaborative filtering approach that uses the novel concept of self-masking: the user hash code acts as a mask on the items (using the Boolean AND operation), such that it learns to encode which bits are important to the user, rather than the user's preference towards the underlying item property that the bits represent.
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims
We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification.
Contextually Propagated Term Weights for Document Representation
Word embeddings predict a word from its neighbours by learning small, dense embedding vectors.
Unsupervised Neural Generative Semantic Hashing
We present a novel unsupervised generative semantic hashing approach, \textit{Ranking based Semantic Hashing} (RBSH) that consists of both a variational and a ranking based component.
Neural Speed Reading with Structural-Jump-LSTM
We present Structural-Jump-LSTM: the first neural speed reading model to both skip and jump text during inference.
Modelling Sequential Music Track Skips using a Multi-RNN Approach
Modelling sequential music skips provides streaming companies the ability to better understand the needs of the user base, resulting in a better user experience by reducing the need to manually skip certain music tracks.
Neural Check-Worthiness Ranking with Weak Supervision: Finding Sentences for Fact-Checking
Automatic fact-checking systems detect misinformation, such as fake news, by (i) selecting check-worthy sentences for fact-checking, (ii) gathering related information to the sentences, and (iii) inferring the factuality of the sentences.
Contextual Compositionality Detection with External Knowledge Bases andWord Embeddings
In this paper, we operationalize the viewpoint that compositionality is contextual rather than deterministic, i. e., that whether a phrase is compositional or non-compositional depends on its context.
Dependencies: Formalising Semantic Catenae for Information Retrieval
A prerequisite for processing text semantics, common to the above examples, is having some computational representation of text as an abstract object.
Rhetorical relations for information retrieval
Typically, every part in most coherent text has some plausible reason for its presence, some function that it performs to the overall semantics of the text.
A Study of Metrics of Distance and Correlation Between Ranked Lists for Compositionality Detection
Compositionality in language refers to how much the meaning of some phrase can be decomposed into the meaning of its constituents and the way these constituents are combined.
Multi-Dueling Bandits and Their Application to Online Ranker Evaluation
Online ranker evaluation can be modeled by dueling ban- dits, a mathematical model for online learning under limited feedback from pairwise comparisons.
Entropy and Graph Based Modelling of Document Coherence using Discourse Entities: An Application
We present two novel models of document coherence and their application to information retrieval (IR).
A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion
Our novel hierarchical recurrent encoder-decoder architecture allows the model to be sensitive to the order of queries in the context while avoiding data sparsity.
