Search Results for author: Emine Yilmaz

Found 36 papers, 17 papers with code

Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation

1 code implementation Findings (EMNLP) 2021 Jarana Manotumruksa, Jeff Dalton, Edgar Meij, Emine Yilmaz

While state-of-the-art Dialogue State Tracking (DST) models show promising results, all of them rely on a traditional cross-entropy loss function during the training process, which may not be optimal for improving the joint goal accuracy.

Data Augmentation Dialogue State Tracking

Integrated Weak Learning

no code implementations19 Jun 2022 Peter Hayes, Mingtian Zhang, Raza Habib, Jordan Burgess, Emine Yilmaz, David Barber

We introduce a label model that can learn to aggregate weak supervision sources differently for different datapoints and takes into consideration the performance of the end-model during training.

ViralBERT: A User Focused BERT-Based Approach to Virality Prediction

1 code implementation17 May 2022 Rikaz Rameez, Hossein A. Rahmani, Emine Yilmaz

We collect a dataset of 330k tweets to train ViralBERT and validate the efficacy of our model using baselines from current studies in this field.

Sentiment Analysis

Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking

no code implementations ACL 2022 Yue Feng, Aldo Lipani, Fanghua Ye, Qiang Zhang, Emine Yilmaz

Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership relations and dialogue-aware dynamic slot relations explicitly, and (2) generalizing to unseen domains.

Dialogue State Tracking Multi-domain Dialogue State Tracking +1

ASSIST: Towards Label Noise-Robust Dialogue State Tracking

1 code implementation Findings (ACL) 2022 Fanghua Ye, Yue Feng, Emine Yilmaz

In this paper, instead of improving the annotation quality further, we propose a general framework, named ASSIST (lAbel noiSe-robuSt dIalogue State Tracking), to train DST models robustly from noisy labels.

Dialogue State Tracking

Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems

no code implementations8 Dec 2021 Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor

In informational recommenders, many challenges arise from the need to handle the semantic and hierarchical structure between knowledge areas.

Knowledge Graphs Recommendation Systems

Towards More Accountable Search Engines: Online Evaluation of Representation Bias

no code implementations17 Oct 2021 Aldo Lipani, Florina Piroi, Emine Yilmaz

Information availability affects people's behavior and perception of the world.

Sample Efficient Model Evaluation

no code implementations24 Sep 2021 Emine Yilmaz, Peter Hayes, Raza Habib, Jordan Burgess, David Barber

Labelling data is a major practical bottleneck in training and testing classifiers.

PEEK: A Large Dataset of Learner Engagement with Educational Videos

1 code implementation3 Sep 2021 Sahan Bulathwela, Maria Perez-Ortiz, Erik Novak, Emine Yilmaz, John Shawe-Taylor

One of the main challenges in advancing this research direction is the scarcity of large, publicly available datasets.

Recommendation Systems

Estimation of Fair Ranking Metrics with Incomplete Judgments

no code implementations11 Aug 2021 Ömer Kırnap, Fernando Diaz, Asia Biega, Michael Ekstrand, Ben Carterette, Emine Yilmaz

There is increasing attention to evaluating the fairness of search system ranking decisions.


MS MARCO: Benchmarking Ranking Models in the Large-Data Regime

no code implementations9 May 2021 Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Jimmy Lin

Evaluation efforts such as TREC, CLEF, NTCIR and FIRE, alongside public leaderboard such as MS MARCO, are intended to encourage research and track our progress, addressing big questions in our field.

TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime

no code implementations19 Apr 2021 Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Ellen M. Voorhees, Ian Soboroff

The TREC Deep Learning (DL) Track studies ad hoc search in the large data regime, meaning that a large set of human-labeled training data is available.

Selection bias

Overview of the TREC 2020 deep learning track

1 code implementation15 Feb 2021 Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos

This is the second year of the TREC Deep Learning Track, with the goal of studying ad hoc ranking in the large training data regime.

Passage Retrieval

Slot Self-Attentive Dialogue State Tracking

1 code implementation22 Jan 2021 Fanghua Ye, Jarana Manotumruksa, Qiang Zhang, Shenghui Li, Emine Yilmaz

Then a stacked slot self-attention is applied on these features to learn the correlations among slots.

Dialogue State Tracking Task-Oriented Dialogue Systems

VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement

1 code implementation2 Nov 2020 Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, John Shawe-Taylor

This paper introduces VLEngagement, a novel dataset that consists of content-based and video-specific features extracted from publicly available scientific video lectures and several metrics related to user engagement.

ORCAS: 18 Million Clicked Query-Document Pairs for Analyzing Search

no code implementations9 Jun 2020 Nick Craswell, Daniel Campos, Bhaskar Mitra, Emine Yilmaz, Bodo Billerbeck

Users of Web search engines reveal their information needs through queries and clicks, making click logs a useful asset for information retrieval.

Information Retrieval

Predicting Engagement in Video Lectures

1 code implementation31 May 2020 Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani, Emine Yilmaz, John Shawe-Taylor

The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners.

Recommendation Systems

On the Reliability of Test Collections for Evaluating Systems of Different Types

no code implementations28 Apr 2020 Emine Yilmaz, Nick Craswell, Bhaskar Mitra, Daniel Campos

As deep learning based models are increasingly being used for information retrieval (IR), a major challenge is to ensure the availability of test collections for measuring their quality.

Fairness Information Retrieval +1

Overview of the TREC 2019 deep learning track

2 code implementations17 Mar 2020 Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Ellen M. Voorhees

The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc ranking in a large data regime.

Passage Retrieval Transfer Learning

Towards an Integrative Educational Recommender for Lifelong Learners

1 code implementation3 Dec 2019 Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, John Shawe-Taylor

One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning.

TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources

1 code implementation21 Nov 2019 Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, John Shawe-Taylor

The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient, high-quality education to large masses of learners.

Knowledge Tracing

Disentangling Interpretable Generative Parameters of Random and Real-World Graphs

1 code implementation12 Oct 2019 Niklas Stoehr, Emine Yilmaz, Marc Brockschmidt, Jan Stuehmer

While a wide range of interpretable generative procedures for graphs exist, matching observed graph topologies with such procedures and choices for its parameters remains an open problem.

Disentanglement Graph Embedding +2

Self-Attentive Hawkes Processes

1 code implementation17 Jul 2019 Qiang Zhang, Aldo Lipani, Omer Kirnap, Emine Yilmaz

The proposed method adapts self-attention to fit the intensity function of Hawkes processes.

Variational Self-attention Model for Sentence Representation

no code implementations30 Dec 2018 Qiang Zhang, Shangsong Liang, Emine Yilmaz

This paper proposes a variational self-attention model (VSAM) that employs variational inference to derive self-attention.

Stance Detection Variational Inference

Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach

no code implementations6 Jun 2017 Rishabh Mehrotra, Emine Yilmaz

As a result, significant amount of research has been devoted to extracting proper representations of tasks in order to enable search systems to help users complete their tasks, as well as providing the end user with better query suggestions, for better recommendations, for satisfaction prediction, and for improved personalization in terms of tasks.

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