Search Results for author: Iadh Ounis

Found 18 papers, 4 papers with code

Multi-Task Learning using Dynamic Task Weighting for Conversational Question Answering

no code implementations EMNLP (scai) 2020 Sarawoot Kongyoung, Craig Macdonald, Iadh Ounis

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.

Conversational Question Answering Conversational Search +1

RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification

no code implementations Findings (EMNLP) 2021 Hitarth Narvala, Graham McDonald, Iadh Ounis

The relationships that exist between entities can be a reliable indicator for classifying sensitive information, such as commercially sensitive information.

Classification

Reproducing Personalised Session Search over the AOL Query Log

no code implementations21 Jan 2022 Sean MacAvaney, Craig Macdonald, Iadh Ounis

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

no code implementations26 Nov 2021 Sean MacAvaney, Craig Macdonald, Iadh Ounis

We present ir-measures, a new tool that makes it convenient to calculate a diverse set of evaluation measures used in information retrieval.

Information Retrieval

On Single and Multiple Representations in Dense Passage Retrieval

1 code implementation13 Aug 2021 Craig Macdonald, Nicola Tonellotto, Iadh Ounis

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.

Passage Retrieval Re-Ranking

DVM-CAR: A large-scale automotive dataset for visual marketing research and applications

no code implementations10 Aug 2021 Jingming Huang, Bowei Chen, Lan Luo, Shigang Yue, Iadh Ounis

In this paper, we present our multidisciplinary initiative of creating a publicly available dataset to facilitate the visual-related marketing research and applications in automotive industry such as automotive exterior design, consumer analytics and sales modelling.

Marketing

IntenT5: Search Result Diversification using Causal Language Models

no code implementations9 Aug 2021 Sean MacAvaney, Craig Macdonald, Roderick Murray-Smith, Iadh Ounis

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.

Causal Language Modeling Language Modelling +1

Graph Neural Pre-training for Enhancing Recommendations using Side Information

no code implementations8 Jul 2021 Zaiqiao Meng, Siwei Liu, Craig Macdonald, Iadh Ounis

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.

Entity Embeddings Recommendation Systems

Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval

3 code implementations21 Jun 2021 Xiao Wang, Craig Macdonald, Nicola Tonellotto, Iadh Ounis

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.

Information Retrieval Passage Ranking +1

The Simpson's Paradox in the Offline Evaluation of Recommendation Systems

1 code implementation18 Apr 2021 Amir H. Jadidinejad, Craig Macdonald, Iadh Ounis

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!

Recommendation Systems

Leveraging Review Properties for Effective Recommendation

no code implementations5 Feb 2021 Xi Wang, Iadh Ounis, Craig Macdonald

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.

Recommendation Systems

Negative Confidence-Aware Weakly Supervised Binary Classification for Effective Review Helpfulness Classification

no code implementations14 Aug 2020 Xi Wang, Iadh Ounis, Craig Macdonald

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.

Classification General Classification

Exploring Data Splitting Strategies for the Evaluation of Recommendation Models

no code implementations26 Jul 2020 Zaiqiao Meng, Richard McCreadie, Craig Macdonald, Iadh Ounis

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.

Recommendation Systems

Deep Reinforced Query Reformulation for Information Retrieval

no code implementations15 Jul 2020 Xiao Wang, Craig Macdonald, Iadh Ounis

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.

Document Ranking Information Retrieval

Variational Bayesian Context-aware Representation for Grocery Recommendation

1 code implementation17 Sep 2019 Zaiqiao Meng, Richard McCreadie, Craig Macdonald, Iadh Ounis

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.

Variational Inference

The FACTS of Technology-Assisted Sensitivity Review

no code implementations5 Jul 2019 Graham McDonald, Craig Macdonald, Iadh Ounis

However, many government documents contain sensitive information, such as personal or confidential information.

Fairness

Using Word Embeddings in Twitter Election Classification

no code implementations22 Jun 2016 Xiao Yang, Craig Macdonald, Iadh Ounis

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.

Classification General Classification +2

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