Search Results for author:
Found 30 papers, 7 papers with code
Multi-Task Learning using Dynamic Task Weighting for Conversational Question Answering
Furthermore, we propose a novel hybrid dynamic method combining Abridged Linear for the main task with a Loss-Balanced Task Weighting (LBTW) for the auxiliary tasks, so as to automatically fine-tune task weighting during learning, ensuring that each of the task’s weights is adjusted by the relative importance of the different tasks.
RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification
The relationships that exist between entities can be a reliable indicator for classifying sensitive information, such as commercially sensitive information.
Ranked #1 on
Sensitivity Classification
on GovSensitivity
A Directional Diffusion Graph Transformer for Recommendation
To account for and model possible noise in the users' interactions in graph neural recommenders, we propose a novel Diffusion Graph Transformer (DiffGT) model for top-k recommendation.
'One size doesn't fit all': Learning how many Examples to use for In-Context Learning for Improved Text Classification
While existing work uses a static number of examples during inference for each data instance, in this paper we propose a novel methodology of dynamically adapting the number of examples as per the data.
Query Exposure Prediction for Groups of Documents in Rankings
With this in mind, it is beneficial to predict the amount of exposure that a group of documents is likely to receive in the results of the first stage retrieval process, in order to ensure that there are a sufficient number of documents included from each of the groups.
A Social-aware Gaussian Pre-trained Model for Effective Cold-start Recommendation
We propose a novel recommendation model, the Social-aware Gaussian Pre-trained model (SGP), which encodes the user social relations and interaction data at the pre-training stage in a Graph Neural Network (GNN).
Large Multi-modal Encoders for Recommendation
In recent years, the rapid growth of online multimedia services, such as e-commerce platforms, has necessitated the development of personalised recommendation approaches that can encode diverse content about each item.
Contrastive Graph Prompt-tuning for Cross-domain Recommendation
In our research, we introduce the Personalised Graph Prompt-based Recommendation (PGPRec) framework.
On the Effects of Regional Spelling Conventions in Retrieval Models
One advantage of neural ranking models is that they are meant to generalise well in situations of synonymity i. e. where two words have similar or identical meanings.
Generative Query Reformulation for Effective Adhoc Search
GenQR directly reformulates the user's input query, while GenPRF provides additional context for the query by making use of pseudo-relevance feedback information.
Effective Hierarchical Information Threading Using Network Community Detection
With the tremendous growth in the volume of information produced online every day (e. g. news articles), there is a need for automatic methods to identify related information about events as the events evolve over time (i. e., information threads).
Ranked #1 on
Information Threading
on NewSHead
Identifying chronological and coherent information threads using 5W1H questions and temporal relationships
Due to the massive volume of articles produced online every day, it is challenging for online platforms (e. g., news agencies) to present the information about an event, activity or discussion to their users in an easily digestible format.
Ranked #1 on
Information Threading
on Multi-News
Leveraging Users' Social Network Embeddings for Fake News Detection on Twitter
We conclude that the Twitter users' friendship and followers network information can significantly outperform language-based approaches, as well as the existing state-of-the-art fake news detection models that use a more sophisticated network structure, in classifying fake news on Twitter.
Entity-Assisted Language Models for Identifying Check-worthy Sentences
Our results show that the neural language models significantly outperform traditional TF. IDF and LSTM methods.
Reproducing Personalised Session Search over the AOL Query Log
Given that web documents are prone to change over time, we study the differences present between a version of the corpus containing documents as they appeared in 2017 (which has been used by several recent works) and a new version we construct that includes documents close to as they appeared at the time the query log was produced (2006).
Streamlining Evaluation with ir-measures
We present ir-measures, a new tool that makes it convenient to calculate a diverse set of evaluation measures used in information retrieval.
On Single and Multiple Representations in Dense Passage Retrieval
The advent of contextualised language models has brought gains in search effectiveness, not just when applied for re-ranking the output of classical weighting models such as BM25, but also when used directly for passage indexing and retrieval, a technique which is called dense retrieval.
DVM-CAR: A large-scale automotive dataset for visual marketing research and applications
There is a growing interest in product aesthetics analytics and design.
IntenT5: Search Result Diversification using Causal Language Models
Existing approaches often rely on massive query logs and interaction data to generate a variety of possible query intents, which then can be used to re-rank documents.
Graph Neural Pre-training for Enhancing Recommendations using Side Information
For the GCN-P model, two single-relational graphs are constructed from all the users' and items' side information respectively, to pre-train entity representations by using the Graph Convolutional Networks.
Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval
In particular, based on the pseudo-relevant set of documents identified using a first-pass dense retrieval, we extract representative feedback embeddings (using KMeans clustering) -- while ensuring that these embeddings discriminate among passages (based on IDF) -- which are then added to the query representation.
Ranked #1 on
TREC 2019 Passage Ranking
on MSMARCO
The Simpson's Paradox in the Offline Evaluation of Recommendation Systems
Using the relative comparison of many recommendation models as in the typical offline evaluation of recommender systems, and based on the Kendall rank correlation coefficient, we show that our proposed evaluation methodology exhibits statistically significant improvements of 14% and 40% on the examined open loop datasets (Yahoo!
Leveraging Review Properties for Effective Recommendation
Furthermore, inspired by the users' information adoption framework, we integrate two loss functions and a negative sampling strategy into our proposed RPRM model, to ensure that the properties of reviews are correlated with the users' preferences.
Negative Confidence-Aware Weakly Supervised Binary Classification for Effective Review Helpfulness Classification
However, a classification model that learns to classify binary instances with incomplete positive labels while assuming all unlabelled data to be negative examples will often generate a biased classifier.
Exploring Data Splitting Strategies for the Evaluation of Recommendation Models
In this paper, we both show that there is no standard splitting strategy and that the selection of splitting strategy can have a strong impact on the ranking of recommender systems.
Deep Reinforced Query Reformulation for Information Retrieval
Query reformulations have long been a key mechanism to alleviate the vocabulary-mismatch problem in information retrieval, for example by expanding the queries with related query terms or by generating paraphrases of the queries.
Variational Bayesian Context-aware Representation for Grocery Recommendation
We train our VBCAR model based on the Bayesian Skip-gram framework coupled with the amortized variational inference so that it can learn more expressive latent representations that integrate both the non-linearity and Bayesian behaviour.
The FACTS of Technology-Assisted Sensitivity Review
However, many government documents contain sensitive information, such as personal or confidential information.
Using Word Embeddings in Twitter Election Classification
In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, the context window size and the dimensionality of word embeddings on the classification performance.
